Publications

2020

• Understanding and mitigating the tradeoff between robustness and accuracy. Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang. arXiv preprint arXiv:2002.10716, 2020.

  @article{raghunathan2020understanding,
    author = {A. Raghunathan and S. M. Xie and F. Yang and J. C. Duchi and P. Liang},
    journal = {arXiv preprint arXiv:2002.10716},
    title = {Understanding and Mitigating the Tradeoff Between Robustness and Accuracy},
    year = {2020},
  }
  

  [bib] [paper]
  
• Understanding self-training for gradual domain adaptation. Ananya Kumar, Tengyu Ma, Percy Liang. arXiv preprint arXiv:2002.11361, 2020.

  @article{kumar2020gradual,
    author = {A. Kumar and T. Ma and P. Liang},
    journal = {arXiv preprint arXiv:2002.11361},
    title = {Understanding Self-Training for Gradual Domain Adaptation},
    year = {2020},
  }
  

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• Distributionally robust neural networks for group shifts: on the importance of regularization for worst-case generalization. Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto, Percy Liang. International Conference on Learning Representations (ICLR), 2020.

  @inproceedings{sagawa2020group,
    author = {S. Sagawa and P. W. Koh and T. B. Hashimoto and P. Liang},
    booktitle = {International Conference on Learning Representations (ICLR)},
    title = {Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization},
    year = {2020},
  }
  

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• Strategies for pre-training graph neural networks. Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec. International Conference on Learning Representations (ICLR), 2020.

  @inproceedings{hu2020pretraining,
    author = {W. Hu and B. Liu and J. Gomes and M. Zitnik and P. Liang and V. Pande and J. Leskovec},
    booktitle = {International Conference on Learning Representations (ICLR)},
    title = {Strategies for Pre-training Graph Neural Networks},
    year = {2020},
  }
  

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• Selection via proxy: efficient data selection for deep learning. Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, Matei Zaharia. International Conference on Learning Representations (ICLR), 2020.

  @inproceedings{coleman2020selection,
    author = {C. Coleman and C. Yeh and S. Mussmann and B. Mirzasoleiman and P. Bailis and P. Liang and J. Leskovec and M. Zaharia},
    booktitle = {International Conference on Learning Representations (ICLR)},
    title = {Selection via Proxy: Efficient Data Selection for Deep Learning},
    year = {2020},
  }
  

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• A tight analysis of greedy yields subexponential time approximation for uniform decision tree. Ray Li, Percy Liang, Stephen Mussmann. Symposium on Discrete Algorithms (SODA), 2020.

  @inproceedings{li2020greedy,
    author = {R. Li and P. Liang and S. Mussmann},
    booktitle = {Symposium on Discrete Algorithms (SODA)},
    title = {A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree},
    year = {2020},
  }
  

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2019

• Noise induces loss discrepancy across groups for linear regression. Fereshte Khani, Percy Liang. arXiv preprint arXiv:1911.09876, 2019.

  @article{khani2019noise,
    author = {F. Khani and P. Liang},
    journal = {arXiv preprint arXiv:1911.09876},
    title = {Noise Induces Loss Discrepancy Across Groups for Linear Regression},
    year = {2019},
  }
  

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• Shaping visual representations with language for few-shot classification. Jesse Mu, Percy Liang, Noah Goodman. arXiv preprint arXiv:1911.02683, 2019.

  @article{mu2019shaping,
    author = {J. Mu and P. Liang and N. Goodman},
    journal = {arXiv preprint arXiv:1911.02683},
    title = {Shaping Visual Representations with Language for Few-shot Classification},
    year = {2019},
  }
  

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• Certified robustness to adversarial word substitutions. Robin Jia, Aditi Raghunathan, Kerem Göksel, Percy Liang. Empirical Methods in Natural Language Processing (EMNLP), 2019.

  @inproceedings{jia2019certified,
    author = {R. Jia and A. Raghunathan and K. Göksel and P. Liang},
    booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
    title = {Certified Robustness to Adversarial Word Substitutions},
    year = {2019},
  }
  

  [bib] [paper] [CodaLab]
  
• Distributionally robust language modeling. Yonatan Oren, Shiori Sagawa, Tatsunori Hashimoto, Percy Liang. Empirical Methods in Natural Language Processing (EMNLP), 2019.

  @inproceedings{oren2019drolm,
    author = {Y. Oren and S. Sagawa and T. Hashimoto and P. Liang},
    booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
    title = {Distributionally Robust Language Modeling},
    year = {2019},
  }
  

  [bib] [paper] [CodaLab]
  
• Designing and interpreting probes with control tasks. John Hewitt, Percy Liang. Empirical Methods in Natural Language Processing (EMNLP), 2019. Best paper runner up.

  @inproceedings{hewitt2019control,
    author = {J. Hewitt and P. Liang},
    booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
    title = {Designing and Interpreting Probes with Control Tasks},
    year = {2019},
  }
  

  [bib] [paper] [CodaLab]
  
• SPoC: search-based pseudocode to code. Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken, Percy Liang. Advances in Neural Information Processing Systems (NeurIPS), 2019.

  @inproceedings{kulal2019spoc,
    author = {S. Kulal and P. Pasupat and K. Chandra and M. Lee and O. Padon and A. Aiken and P. Liang},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {SPoC: Search-based Pseudocode to Code},
    year = {2019},
  }
  

  [bib] [paper] [CodaLab]
  
• Unlabeled data improves adversarial robustness. Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, John C. Duchi. Advances in Neural Information Processing Systems (NeurIPS), 2019.

  @inproceedings{carmon2019unlabeled,
    author = {Y. Carmon and A. Raghunathan and L. Schmidt and P. Liang and J. C. Duchi},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Unlabeled Data Improves Adversarial Robustness},
    year = {2019},
  }
  

  [bib] [paper] [CodaLab]
  
• Verified uncertainty calibration. Ananya Kumar, Percy Liang, Tengyu Ma. Advances in Neural Information Processing Systems (NeurIPS), 2019.

  @inproceedings{kumar2019calibration,
    author = {A. Kumar and P. Liang and T. Ma},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Verified Uncertainty Calibration},
    year = {2019},
  }
  

  [bib] [paper] [CodaLab]
  
• On the accuracy of influence functions for measuring group effects. Pang Wei Koh, Kai-Siang Ang, Hubert H. K. Teo, Percy Liang. Advances in Neural Information Processing Systems (NeurIPS), 2019.

  @inproceedings{koh2019influence,
    author = {P. W. Koh and K. Ang and H. H. K. Teo and P. Liang},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {On the Accuracy of Influence Functions for Measuring Group Effects},
    year = {2019},
  }
  

  [bib] [paper] [CodaLab]
  
• Learning autocomplete systems as a communication game. Mina Lee, Tatsunori Hashimoto, Percy Liang. Emergent Communication Workshop at Neural Information Processing Systems (NeurIPS), 2019.

  @inproceedings{lee2019autocomplete,
    author = {M. Lee and T. Hashimoto and P. Liang},
    booktitle = {Emergent Communication Workshop at Neural Information Processing Systems (NeurIPS)},
    title = {Learning Autocomplete Systems as a Communication Game},
    year = {2019},
  }
  

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• Adversarial training can hurt generalization. Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang. arXiv preprint arXiv:1906.06032, 2019.

  @article{raghunathan2019hurt,
    author = {A. Raghunathan and S. M. Xie and F. Yang and J. C. Duchi and P. Liang},
    journal = {arXiv preprint arXiv:1906.06032},
    title = {Adversarial Training Can Hurt Generalization},
    year = {2019},
  }
  

  [bib] [paper]
  
• Maximum weighted loss discrepancy. Fereshte Khani, Aditi Raghunathan, Percy Liang. arXiv preprint arXiv:1906.03518, 2019.

  @article{khani2019mwld,
    author = {F. Khani and A. Raghunathan and P. Liang},
    journal = {arXiv preprint arXiv:1906.03518},
    title = {Maximum Weighted Loss Discrepancy},
    year = {2019},
  }
  

  [bib] [paper] [CodaLab]
  
• Ambitious data science can be painless. Hatef Monajemi, Riccardo Murri, Eric Jonas, Percy Liang, Victoria Stodden, David L. Donoho. Harvard Data Science Review, 2019.

  @article{monajemi2019painless,
    author = {H. Monajemi and R. Murri and E. Jonas and P. Liang and V. Stodden and D. L. Donoho},
    journal = {Harvard Data Science Review},
    title = {Ambitious Data Science Can Be Painless},
    volume = {1},
    year = {2019},
  }
  

  [bib] [paper]
  
• Unifying human and statistical evaluation for natural language generation. Tatsu Hashimoto, Hugh Zhang, Percy Liang. North American Association for Computational Linguistics (NAACL), 2019.

  @inproceedings{hashimoto2019huse,
    author = {T. Hashimoto and H. Zhang and P. Liang},
    booktitle = {North American Association for Computational Linguistics (NAACL)},
    title = {Unifying Human and Statistical Evaluation for Natural Language Generation},
    year = {2019},
  }
  

  [bib] [paper] [CodaLab]
  
• Pun generation with surprise. Nanyun Peng, He He, Percy Liang. North American Association for Computational Linguistics (NAACL), 2019.

  @inproceedings{peng2019pun,
    author = {N. Peng and H. He and P. Liang},
    booktitle = {North American Association for Computational Linguistics (NAACL)},
    title = {Pun Generation with Surprise},
    year = {2019},
  }
  

  [bib] [paper] [CodaLab]
  
• Stronger data poisoning attacks break data sanitization defenses. Pang Wei Koh, Jacob Steinhardt, Percy Liang. arXiv preprint arXiv:1811.00741, 2019.

  @article{koh2019stronger,
    author = {P. W. Koh and J. Steinhardt and P. Liang},
    journal = {arXiv preprint arXiv:1811.00741},
    title = {Stronger Data Poisoning Attacks Break Data Sanitization Defenses},
    year = {2019},
  }
  

  [bib] [paper]
  
• Learning a SAT solver from single-bit supervision. Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, David L. Dill. International Conference on Learning Representations (ICLR), 2019.

  @inproceedings{selsam2019sat,
    author = {D. Selsam and M. Lamm and B. Bünz and P. Liang and L. de Moura and D. L. Dill},
    booktitle = {International Conference on Learning Representations (ICLR)},
    title = {Learning a {SAT} Solver from Single-Bit Supervision},
    year = {2019},
  }
  

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• Defending against whitebox adversarial attacks via randomized discretization. Yuchen Zhang, Percy Liang. Artificial Intelligence and Statistics (AISTATS), 2019.

  @inproceedings{zhang2019discretization,
    author = {Y. Zhang and P. Liang},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    title = {Defending against Whitebox Adversarial Attacks via Randomized Discretization},
    year = {2019},
  }
  

  [bib] [paper] [CodaLab]
  
• Inferring multidimensional rates of aging from cross-sectional data. Emma Pierson, Pang Wei Koh, Tatsunori Hashimoto, Daphne Koller, Jure Leskovec, Nick Eriksson, Percy Liang. Artificial Intelligence and Statistics (AISTATS), 2019.

  @inproceedings{pierson2019aging,
    author = {E. Pierson and P. W. Koh and T. Hashimoto and D. Koller and J. Leskovec and N. Eriksson and P. Liang},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    title = {Inferring Multidimensional Rates of Aging from Cross-Sectional Data},
    year = {2019},
  }
  

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• FrAngel: component-based synthesis with control structures. Kensen Shi, Jacob Steinhardt, Percy Liang. Principles of Programming Languages (POPL), 2019.

  @inproceedings{shi2019frangel,
    author = {K. Shi and J. Steinhardt and P. Liang},
    booktitle = {Principles of Programming Languages (POPL)},
    title = {{F}r{A}ngel: Component-Based Synthesis with Control Structures},
    year = {2019},
  }
  

  [bib] [paper] [CodaLab]
  

2018

• Semidefinite relaxations for certifying robustness to adversarial examples. Aditi Raghunathan, Jacob Steinhardt, Percy Liang. Advances in Neural Information Processing Systems (NeurIPS), 2018.

  @inproceedings{raghunathan2018sdp,
    author = {A. Raghunathan and J. Steinhardt and P. Liang},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Semidefinite relaxations for certifying robustness to adversarial examples},
    year = {2018},
  }
  

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• Uncertainty sampling is preconditioned stochastic gradient descent on zero-one loss. Stephen Mussmann, Percy Liang. Advances in Neural Information Processing Systems (NeurIPS), 2018.

  @inproceedings{mussmann2018sgd,
    author = {S. Mussmann and P. Liang},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss},
    year = {2018},
  }
  

  [bib] [paper] [CodaLab]
  
• A retrieve-and-edit framework for predicting structured outputs. Tatsunori Hashimoto, Kelvin Guu, Yonatan Oren, Percy Liang. Advances in Neural Information Processing Systems (NeurIPS), 2018.

  @inproceedings{hashimoto2018edit,
    author = {T. Hashimoto and K. Guu and Y. Oren and P. Liang},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {A Retrieve-and-Edit Framework for Predicting Structured Outputs},
    year = {2018},
  }
  

  [bib] [paper] [CodaLab]
  
• QuAC: question answering in context. Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, Luke Zettlemoyer. Empirical Methods in Natural Language Processing (EMNLP), 2018.

  @inproceedings{choi2018quac,
    author = {E. Choi and H. He and M. Iyyer and M. Yatskar and W. Yih and Y. Choi and P. Liang and L. Zettlemoyer},
    booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
    title = {QuAC: Question Answering in Context},
    year = {2018},
  }
  

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• Decoupling strategy and generation in negotiation dialogues. He He, Derek Chen, Anusha Balakrishnan, Percy Liang. Empirical Methods in Natural Language Processing (EMNLP), 2018.

  @inproceedings{he2018negotiation,
    author = {H. He and D. Chen and A. Balakrishnan and P. Liang},
    booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
    title = {Decoupling Strategy and Generation in Negotiation Dialogues},
    year = {2018},
  }
  

  [bib] [paper] [CodaLab]
  
• Mapping natural language commands to web elements. Panupong Pasupat, Tian-Shun Jiang, Evan Liu, Kelvin Guu, Percy Liang. Empirical Methods in Natural Language Processing (EMNLP), 2018. Short paper.

  @inproceedings{pasupat2018elements,
    author = {P. Pasupat and T. Jiang and E. Liu and K. Guu and P. Liang},
    booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
    title = {Mapping Natural Language Commands to Web Elements},
    year = {2018},
  }
  

  [bib] [paper] [CodaLab]
  
• Textual analogy parsing: what's shared and what's compared among analogous facts. Matthew Lamm, Arun Chaganty, Christopher D. Manning, Dan Jurafsky, Percy Liang. Empirical Methods in Natural Language Processing (EMNLP), 2018.

  @inproceedings{lamm2018tap,
    author = {M. Lamm and A. Chaganty and C. D. Manning and D. Jurafsky and P. Liang},
    booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
    title = {Textual Analogy Parsing: What's Shared and What's Compared among Analogous Facts},
    year = {2018},
  }
  

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• On the relationship between data efficiency and error in active learning. Stephen Mussmann, Percy Liang. International Conference on Machine Learning (ICML), 2018.

  @inproceedings{mussmann2018accuracy,
    author = {S. Mussmann and P. Liang},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {On the Relationship between Data Efficiency and Error in Active Learning},
    year = {2018},
  }
  

  [bib] [paper] [CodaLab]
  
• Fairness without demographics in repeated loss minimization. Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang. International Conference on Machine Learning (ICML), 2018. Best paper runner up.

  @inproceedings{hashimoto2018repeated,
    author = {T. B. Hashimoto and M. Srivastava and H. Namkoong and P. Liang},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {Fairness Without Demographics in Repeated Loss Minimization},
    year = {2018},
  }
  

  [bib] [paper] [CodaLab]
  
• Training classifiers with natural language explanations. Braden Hancock, Paroma Varma, Stephanie Wang, Martin Bringmann, Percy Liang, Christopher Ré. Association for Computational Linguistics (ACL), 2018.

  @inproceedings{hancock2018babble,
    author = {B. Hancock and P. Varma and S. Wang and M. Bringmann and P. Liang and C. Ré},
    booktitle = {Association for Computational Linguistics (ACL)},
    title = {Training Classifiers with Natural Language Explanations},
    year = {2018},
  }
  

  [bib] [paper] [CodaLab]
  
• The price of debiasing automatic metrics in natural language evaluation. Arun Chaganty, Stephen Mussmann, Percy Liang. Association for Computational Linguistics (ACL), 2018.

  @inproceedings{chaganty2018evaluation,
    author = {A. Chaganty and S. Mussmann and P. Liang},
    booktitle = {Association for Computational Linguistics (ACL)},
    title = {The price of debiasing automatic metrics in natural language evaluation},
    year = {2018},
  }
  

  [bib] [paper] [CodaLab]
  
• Know what you don't know: unanswerable questions for SQuAD. Pranav Rajpurkar, Robin Jia, Percy Liang. Association for Computational Linguistics (ACL), 2018. Best short paper award.

  @inproceedings{rajpurkar2018squadrun,
    author = {P. Rajpurkar and R. Jia and P. Liang},
    booktitle = {Association for Computational Linguistics (ACL)},
    title = {Know What You Don't Know: Unanswerable Questions for {SQuAD}},
    year = {2018},
  }
  

  [bib] [paper] [CodaLab]
  
• Generalized binary search for split-neighborly problems. Stephen Mussmann, Percy Liang. Artificial Intelligence and Statistics (AISTATS), 2018.

  @inproceedings{mussmann2018gbs,
    author = {S. Mussmann and P. Liang},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    title = {Generalized Binary Search For Split-Neighborly Problems},
    year = {2018},
  }
  

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• Planning, inference and pragmatics in sequential language games. Fereshte Khani, Noah D. Goodman, Percy Liang. Transactions of the Association for Computational Linguistics (TACL), 2018.

  @article{khani2018pip,
    author = {F. Khani and N. D. Goodman and P. Liang},
    journal = {Transactions of the Association for Computational Linguistics (TACL)},
    title = {Planning, Inference and Pragmatics in Sequential Language Games},
    volume = {0},
    year = {2018},
  }
  

  [bib] [paper] [CodaLab]
  
• Generating sentences by editing prototypes. Kelvin Guu, Tatsunori B. Hashimoto, Yonatan Oren, Percy Liang. Transactions of the Association for Computational Linguistics (TACL), 2018.

  @article{guu2018edit,
    author = {K. Guu and T. B. Hashimoto and Y. Oren and P. Liang},
    journal = {Transactions of the Association for Computational Linguistics (TACL)},
    title = {Generating Sentences by Editing Prototypes},
    volume = {0},
    year = {2018},
  }
  

  [bib] [paper] [CodaLab]
  
• Delete, retrieve, generate: a simple approach to sentiment and style transfer. Juncen Li, Robin Jia, He He, Percy Liang. North American Association for Computational Linguistics (NAACL), 2018.

  @inproceedings{li2018style,
    author = {J. Li and R. Jia and H. He and P. Liang},
    booktitle = {North American Association for Computational Linguistics (NAACL)},
    title = {Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer},
    year = {2018},
  }
  

  [bib] [paper] [CodaLab]
  
• Reinforcement learning on web interfaces using workflow-guided exploration. Evan Zheran Liu, Kelvin Guu, Panupong Pasupat, Tianlin Shi, Percy Liang. International Conference on Learning Representations (ICLR), 2018.

  @inproceedings{liu2018workflow,
    author = {E. Z. Liu and K. Guu and P. Pasupat and T. Shi and P. Liang},
    booktitle = {International Conference on Learning Representations (ICLR)},
    title = {Reinforcement Learning on Web Interfaces using Workflow-Guided Exploration},
    year = {2018},
  }
  

  [bib] [paper]
  
• Certified defenses against adversarial examples. Aditi Raghunathan, Jacob Steinhardt, Percy Liang. International Conference on Learning Representations (ICLR), 2018.

  @inproceedings{raghunathan2018certified,
    author = {A. Raghunathan and J. Steinhardt and P. Liang},
    booktitle = {International Conference on Learning Representations (ICLR)},
    title = {Certified defenses against adversarial examples},
    year = {2018},
  }
  

  [bib] [paper] [CodaLab]
  
• Active learning of points-to specifications. Osbert Bastani, Rahul Sharma, Alex Aiken, Percy Liang. Programming Language Design and Implementation (PLDI), 2018.

  @inproceedings{bastani2018active,
    author = {O. Bastani and R. Sharma and A. Aiken and P. Liang},
    booktitle = {Programming Language Design and Implementation (PLDI)},
    title = {Active Learning of Points-To Specifications},
    year = {2018},
  }
  

  [bib] [paper]
  
• Prediction with a short memory. Vatsal Sharan, Sham Kakade, Percy Liang, Gregory Valiant. Symposium on Theory of Computing (STOC), 2018.

  @inproceedings{sharan2018prediction,
    author = {V. Sharan and S. Kakade and P. Liang and G. Valiant},
    booktitle = {Symposium on Theory of Computing (STOC)},
    title = {Prediction with a Short Memory},
    year = {2018},
  }
  

  [bib] [paper]
  
• Transforming question answering datasets into natural language inference datasets. Dorottya Demszky, Kelvin Guu, Percy Liang. arXiv preprint arXiv:1809.02922, 2018.

  @article{demszky2018transforming,
    author = {D. Demszky and K. Guu and P. Liang},
    journal = {arXiv preprint arXiv:1809.02922},
    title = {Transforming Question Answering Datasets Into Natural Language Inference Datasets},
    year = {2018},
  }
  

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2017

• Certified defenses for data poisoning attacks. Jacob Steinhardt, Pang Wei Koh, Percy Liang. Advances in Neural Information Processing Systems (NeurIPS), 2017.

  @inproceedings{steinhardt2017certified,
    author = {J. Steinhardt and P. W. Koh and P. Liang},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Certified Defenses for Data Poisoning Attacks},
    year = {2017},
  }
  

  [bib] [paper] [CodaLab]
  
• Unsupervised transformation learning via convex relaxations. Tatsunori B. Hashimoto, John Duchi, Percy Liang. Advances in Neural Information Processing Systems (NeurIPS), 2017.

  @inproceedings{hashimoto2017transformation,
    author = {T. B. Hashimoto and J. Duchi and P. Liang},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Unsupervised Transformation Learning via Convex Relaxations},
    year = {2017},
  }
  

  [bib] [paper] [CodaLab]
  
• Learning overcomplete HMMs. Vatsal Sharan, Sham Kakade, Percy Liang, Gregory Valiant. Advances in Neural Information Processing Systems (NeurIPS), 2017.

  @inproceedings{sharan2017overcomplete,
    author = {V. Sharan and S. Kakade and P. Liang and G. Valiant},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Learning Overcomplete {HMM}s},
    year = {2017},
  }
  

  [bib] [paper]
  
• Adversarial examples for evaluating reading comprehension systems. Robin Jia, Percy Liang. Empirical Methods in Natural Language Processing (EMNLP), 2017. Outstanding paper award.

  @inproceedings{jia2017adversarial,
    author = {R. Jia and P. Liang},
    booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
    title = {Adversarial Examples for Evaluating Reading Comprehension Systems},
    year = {2017},
  }
  

  [bib] [paper] [CodaLab]
  
• Macro grammars and holistic triggering for efficient semantic parsing. Yuchen Zhang, Panupong Pasupat, Percy Liang. Empirical Methods in Natural Language Processing (EMNLP), 2017.

  @inproceedings{zhang2017macro,
    author = {Y. Zhang and P. Pasupat and P. Liang},
    booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
    title = {Macro Grammars and Holistic Triggering for Efficient Semantic Parsing},
    year = {2017},
  }
  

  [bib] [paper] [CodaLab]
  
• Importance sampling for unbiased on-demand evaluation of knowledge base population. Arun Chaganty, Ashwin Paranjape, Percy Liang, Chris Manning. Empirical Methods in Natural Language Processing (EMNLP), 2017.

  @inproceedings{chaganty2017unbiased,
    author = {A. Chaganty and A. Paranjape and P. Liang and C. Manning},
    booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
    title = {Importance sampling for unbiased on-demand evaluation of knowledge base population},
    year = {2017},
  }
  

  [bib] [paper]
  
• Understanding black-box predictions via influence functions. Pang Wei Koh, Percy Liang. International Conference on Machine Learning (ICML), 2017. Best paper award.

  @inproceedings{koh2017understanding,
    author = {P. W. Koh and P. Liang},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {Understanding Black-box Predictions via Influence Functions},
    year = {2017},
  }
  

  [bib] [paper] [CodaLab]
  
• Convexified convolutional neural networks. Yuchen Zhang, Percy Liang, Martin J. Wainwright. International Conference on Machine Learning (ICML), 2017.

  @inproceedings{zhang2017convexified,
    author = {Y. Zhang and P. Liang and M. J. Wainwright},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {Convexified Convolutional Neural Networks},
    year = {2017},
  }
  

  [bib] [paper]
  
• Developing bug-free machine learning systems with formal mathematics. Daniel Selsam, Percy Liang, David Dill. International Conference on Machine Learning (ICML), 2017.

  @inproceedings{selsam2017bugfree,
    author = {D. Selsam and P. Liang and D. Dill},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {Developing Bug-Free Machine Learning Systems With Formal Mathematics},
    year = {2017},
  }
  

  [bib] [paper] [code]
  
• World of bits: an open-domain platform for web-based agents. Tianlin Shi, Andrej Karpathy, Linxi Fan, Jonathan Hernandez, Percy Liang. International Conference on Machine Learning (ICML), 2017.

  @inproceedings{shi2017wob,
    author = {T. Shi and A. Karpathy and L. Fan and J. Hernandez and P. Liang},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {World of Bits: An Open-Domain Platform for Web-Based Agents},
    year = {2017},
  }
  

  [bib] [paper]
  
• A hitting time analysis of stochastic gradient Langevin dynamics. Yuchen Zhang, Percy Liang, Moses Charikar. Conference on Learning Theory (COLT), 2017. Best paper award.

  @inproceedings{zhang2017hitting,
    author = {Y. Zhang and P. Liang and M. Charikar},
    booktitle = {Conference on Learning Theory (COLT)},
    title = {A Hitting Time Analysis of Stochastic Gradient {L}angevin Dynamics},
    year = {2017},
  }
  

  [bib] [paper]
  
• Synthesizing program input grammars. Osbert Bastani, Rahul Sharma, Alex Aiken, Percy Liang. Programming Language Design and Implementation (PLDI), 2017.

  @inproceedings{bastani2017synthesizing,
    author = {O. Bastani and R. Sharma and A. Aiken and P. Liang},
    booktitle = {Programming Language Design and Implementation (PLDI)},
    title = {Synthesizing Program Input Grammars},
    year = {2017},
  }
  

  [bib] [paper]
  
• Naturalizing a programming language via interactive learning. Sida I. Wang, Sam Ginn, Percy Liang, Christopher D. Manning. Association for Computational Linguistics (ACL), 2017.

  @inproceedings{wang2017naturalizing,
    author = {S. I. Wang and S. Ginn and P. Liang and C. D. Manning},
    booktitle = {Association for Computational Linguistics (ACL)},
    title = {Naturalizing a Programming Language via Interactive Learning},
    year = {2017},
  }
  

  [bib] [paper] [project] [CodaLab]
  
• Learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings. He He, Anusha Balakrishnan, Mihail Eric, Percy Liang. Association for Computational Linguistics (ACL), 2017.

  @inproceedings{he2017symmetric,
    author = {H. He and A. Balakrishnan and M. Eric and P. Liang},
    booktitle = {Association for Computational Linguistics (ACL)},
    pages = {1766--1776},
    title = {Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings},
    year = {2017},
  }
  

  [bib] [paper] [CodaLab]
  
• From language to programs: bridging reinforcement learning and maximum marginal likelihood. Kelvin Guu, Panupong Pasupat, Evan Zheran Liu, Percy Liang. Association for Computational Linguistics (ACL), 2017.

  @inproceedings{guu2017bridging,
    author = {K. Guu and P. Pasupat and E. Z. Liu and P. Liang},
    booktitle = {Association for Computational Linguistics (ACL)},
    title = {From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood},
    year = {2017},
  }
  

  [bib] [paper] [CodaLab]
  

2016

• Unsupervised risk estimation using only conditional independence structure. Jacob Steinhardt, Percy Liang. Advances in Neural Information Processing Systems (NeurIPS), 2016.

  @inproceedings{steinhardt2016risk,
    author = {J. Steinhardt and P. Liang},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Unsupervised Risk Estimation Using Only Conditional Independence Structure},
    year = {2016},
  }
  

  [bib] [paper]
  
• SQuAD: 100,000+ questions for machine comprehension of text. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang. Empirical Methods in Natural Language Processing (EMNLP), 2016. Best resource paper award.

  @inproceedings{rajpurkar2016squad,
    author = {P. Rajpurkar and J. Zhang and K. Lopyrev and P. Liang},
    booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
    title = {{SQuAD}: 100,000+ Questions for Machine Comprehension of Text},
    year = {2016},
  }
  

  [bib] [paper] [CodaLab]
  
• Learning language games through interaction. Sida I. Wang, Percy Liang, Chris Manning. Association for Computational Linguistics (ACL), 2016. Outstanding paper award.

  @inproceedings{wang2016games,
    author = {S. I. Wang and P. Liang and C. Manning},
    booktitle = {Association for Computational Linguistics (ACL)},
    title = {Learning Language Games through Interaction},
    year = {2016},
  }
  

  [bib] [paper] [CodaLab]
  
• Data recombination for neural semantic parsing. Robin Jia, Percy Liang. Association for Computational Linguistics (ACL), 2016.

  @inproceedings{jia2016recombination,
    author = {R. Jia and P. Liang},
    booktitle = {Association for Computational Linguistics (ACL)},
    title = {Data Recombination for Neural Semantic Parsing},
    year = {2016},
  }
  

  [bib] [paper] [CodaLab]
  
• Simpler context-dependent logical forms via model projections. Reginald Long, Panupong Pasupat, Percy Liang. Association for Computational Linguistics (ACL), 2016.

  @inproceedings{long2016projections,
    author = {R. Long and P. Pasupat and P. Liang},
    booktitle = {Association for Computational Linguistics (ACL)},
    title = {Simpler Context-Dependent Logical Forms via Model Projections},
    year = {2016},
  }
  

  [bib] [paper] [CodaLab]
  
• Inferring logical forms from denotations. Panupong Pasupat, Percy Liang. Association for Computational Linguistics (ACL), 2016.

  @inproceedings{pasupat2016inferring,
    author = {P. Pasupat and P. Liang},
    booktitle = {Association for Computational Linguistics (ACL)},
    title = {Inferring Logical Forms From Denotations},
    year = {2016},
  }
  

  [bib] [paper] [CodaLab]
  
• Unanimous prediction for 100% precision with application to learning semantic mappings. Fereshte Khani, Martin Rinard, Percy Liang. Association for Computational Linguistics (ACL), 2016.

  @inproceedings{khani2016unanimity,
    author = {F. Khani and M. Rinard and P. Liang},
    booktitle = {Association for Computational Linguistics (ACL)},
    title = {Unanimous Prediction for 100% Precision with Application to Learning Semantic Mappings},
    year = {2016},
  }
  

  [bib] [paper] [CodaLab]
  
• How much is 131 million dollars? Putting numbers in perspective with compositional descriptions. Arun Tejasvi Chaganty, Percy Liang. Association for Computational Linguistics (ACL), 2016.

  @inproceedings{chaganty2016perspectives,
    author = {A. T. Chaganty and P. Liang},
    booktitle = {Association for Computational Linguistics (ACL)},
    title = {How Much is 131 Million Dollars? {P}utting Numbers in Perspective with Compositional Descriptions},
    year = {2016},
  }
  

  [bib] [paper] [CodaLab]
  
• Estimation from indirect supervision with linear moments. Aditi Raghunathan, Roy Frostig, John Duchi, Percy Liang. International Conference on Machine Learning (ICML), 2016.

  @inproceedings{raghunathan2016linear,
    author = {A. Raghunathan and R. Frostig and J. Duchi and P. Liang},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {Estimation from Indirect Supervision with Linear Moments},
    year = {2016},
  }
  

  [bib] [paper] [CodaLab]
  
• Data augmentation via Lévy processes. Stefan Wager, Will Fithian, Percy Liang. Perturbations, Optimization and Statistics, 2016.

  @incollection{wager2016levy,
    author = {S. Wager and W. Fithian and P. Liang},
    booktitle = {Perturbations, Optimization and Statistics},
    title = {Data Augmentation via {L}évy Processes},
    year = {2016},
  }
  

  [bib] [paper] [code]
  
• Learning executable semantic parsers for natural language understanding. Percy Liang. Communications of the ACM, 2016.

  @article{liang2016executable,
    author = {P. Liang},
    journal = {Communications of the ACM},
    title = {Learning Executable Semantic Parsers for Natural Language Understanding},
    volume = {59},
    year = {2016},
  }
  

  [bib] [paper]
  

2015

• Building a semantic parser overnight. Yushi Wang, Jonathan Berant, Percy Liang. Association for Computational Linguistics (ACL), 2015.

  @inproceedings{wang2015overnight,
    author = {Y. Wang and J. Berant and P. Liang},
    booktitle = {Association for Computational Linguistics (ACL)},
    title = {Building a Semantic Parser Overnight},
    year = {2015},
  }
  

  [bib] [paper] [project] [CodaLab]
  
• Imitation learning of agenda-based semantic parsers. Jonathan Berant, Percy Liang. Transactions of the Association for Computational Linguistics (TACL), 2015.

  @article{berant2015agenda,
    author = {J. Berant and P. Liang},
    journal = {Transactions of the Association for Computational Linguistics (TACL)},
    pages = {545--558},
    title = {Imitation Learning of Agenda-Based Semantic Parsers},
    volume = {3},
    year = {2015},
  }
  

  [bib] [paper] [project] [CodaLab]
  
• Learning with relaxed supervision. Jacob Steinhardt, Percy Liang. Advances in Neural Information Processing Systems (NeurIPS), 2015.

  @inproceedings{steinhardt2015relaxed,
    author = {J. Steinhardt and P. Liang},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Learning with Relaxed Supervision},
    year = {2015},
  }
  

  [bib] [paper] [CodaLab]
  
• Estimating mixture models via mixture of polynomials. Sida I. Wang, Arun Chaganty, Percy Liang. Advances in Neural Information Processing Systems (NeurIPS), 2015.

  @inproceedings{wang2015polynomial,
    author = {S. I. Wang and A. Chaganty and P. Liang},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Estimating Mixture Models via Mixture of Polynomials},
    year = {2015},
  }
  

  [bib] [paper] [CodaLab]
  
• On-the-Job learning with Bayesian decision theory. Keenon Werling, Arun Chaganty, Percy Liang, Chris Manning. Advances in Neural Information Processing Systems (NeurIPS), 2015.

  @inproceedings{werling2015onthejob,
    author = {K. Werling and A. Chaganty and P. Liang and C. Manning},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {On-the-Job Learning with {B}ayesian Decision Theory},
    year = {2015},
  }
  

  [bib] [paper] [CodaLab]
  
• Calibrated structured prediction. Volodymyr Kuleshov, Percy Liang. Advances in Neural Information Processing Systems (NeurIPS), 2015.

  @inproceedings{kuleshov2015calibrated,
    author = {V. Kuleshov and P. Liang},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Calibrated Structured Prediction},
    year = {2015},
  }
  

  [bib] [paper] [CodaLab]
  
• Traversing knowledge graphs in vector space. Kelvin Guu, John Miller, Percy Liang. Empirical Methods in Natural Language Processing (EMNLP), 2015. Best paper honorable mention.

  @inproceedings{guu2015traversing,
    author = {K. Guu and J. Miller and P. Liang},
    booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
    title = {Traversing Knowledge Graphs in Vector Space},
    year = {2015},
  }
  

  [bib] [paper] [CodaLab]
  
• Compositional semantic parsing on semi-structured tables. Panupong Pasupat, Percy Liang. Association for Computational Linguistics (ACL), 2015.

  @inproceedings{pasupat2015compositional,
    author = {P. Pasupat and P. Liang},
    booktitle = {Association for Computational Linguistics (ACL)},
    title = {Compositional Semantic Parsing on Semi-Structured Tables},
    year = {2015},
  }
  

  [bib] [paper] [CodaLab]
  
• Environment-Driven lexicon induction for high-level instructions. Dipendra K. Misra, Kejia Tao, Percy Liang, Ashutosh Saxena. Association for Computational Linguistics (ACL), 2015.

  @inproceedings{misra2015environment,
    author = {D. K. Misra and K. Tao and P. Liang and A. Saxena},
    booktitle = {Association for Computational Linguistics (ACL)},
    title = {Environment-Driven Lexicon Induction for High-Level Instructions},
    year = {2015},
  }
  

  [bib] [paper] [CodaLab]
  
• Reified context models. Jacob Steinhardt, Percy Liang. International Conference on Machine Learning (ICML), 2015.

  @inproceedings{steinhardt2015rcm,
    author = {J. Steinhardt and P. Liang},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {Reified Context Models},
    year = {2015},
  }
  

  [bib] [paper] [CodaLab]
  
• Learning fast-mixing models for structured prediction. Jacob Steinhardt, Percy Liang. International Conference on Machine Learning (ICML), 2015.

  @inproceedings{steinhardt2015fast,
    author = {J. Steinhardt and P. Liang},
    booktitle = {International Conference on Machine Learning (ICML)},
    pages = {1063--1072},
    title = {Learning Fast-Mixing Models for Structured Prediction},
    year = {2015},
  }
  

  [bib] [paper] [CodaLab]
  
• Learning where to sample in structured prediction. Tianlin Shi, Jacob Steinhardt, Percy Liang. Artificial Intelligence and Statistics (AISTATS), 2015.

  @inproceedings{shi2015sample,
    author = {T. Shi and J. Steinhardt and P. Liang},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    pages = {875--884},
    title = {Learning Where To Sample in Structured Prediction},
    year = {2015},
  }
  

  [bib] [paper] [CodaLab]
  
• Tensor factorization via matrix factorization. Volodymyr Kuleshov, Arun Chaganty, Percy Liang. Artificial Intelligence and Statistics (AISTATS), 2015.

  @inproceedings{kuleshov2015tensor,
    author = {V. Kuleshov and A. Chaganty and P. Liang},
    booktitle = {Artificial Intelligence and Statistics (AISTATS)},
    title = {Tensor factorization via matrix factorization},
    year = {2015},
  }
  

  [bib] [paper] [CodaLab]
  
• Bringing machine learning and compositional semantics together. Percy Liang, Christopher Potts. Annual Reviews of Linguistics, 2015.

  @article{liang2015semantics,
    author = {P. Liang and C. Potts},
    journal = {Annual Reviews of Linguistics},
    number = {1},
    pages = {355--376},
    title = {Bringing machine learning and compositional semantics together},
    volume = {1},
    year = {2015},
  }
  

  [bib] [paper]
  

2014

• The statistics of streaming sparse regression. Jacob Steinhardt, Stefan Wager, Percy Liang. arXiv preprint arXiv:1412.4182, 2014.

  @article{steinhardt2014sparse,
    author = {J. Steinhardt and S. Wager and P. Liang},
    journal = {arXiv preprint arXiv:1412.4182},
    title = {The Statistics of Streaming Sparse Regression},
    year = {2014},
  }
  

  [bib] [paper]
  
• Linking people with "their" names using coreference resolution. Vignesh Ramanathan, Armand Joulin, Percy Liang, Li Fei-Fei. European Conference on Computer Vision (ECCV), 2014.

  @inproceedings{ramanathan2014linking,
    author = {V. Ramanathan and A. Joulin and P. Liang and L. Fei-Fei},
    booktitle = {European Conference on Computer Vision (ECCV)},
    title = {Linking people with "their" names using coreference resolution},
    year = {2014},
  }
  

  [bib] [paper] [supplemental material]
  
• Talking to computers in natural language. Percy Liang. XRDS: Crossroads, The ACM Magazine for Students, 2014.

  @article{liang2014talking,
    author = {P. Liang},
    journal = {XRDS: Crossroads, The ACM Magazine for Students},
    number = {1},
    pages = {18--21},
    title = {Talking to computers in natural language},
    volume = {21},
    year = {2014},
  }
  

  [bib] [paper]
  
• Semantic parsing via paraphrasing. Jonathan Berant, Percy Liang. Association for Computational Linguistics (ACL), 2014. Best long paper honorable mention.

  @inproceedings{berant2014paraphrasing,
    author = {J. Berant and P. Liang},
    booktitle = {Association for Computational Linguistics (ACL)},
    title = {Semantic Parsing via Paraphrasing},
    year = {2014},
  }
  

  [bib] [paper] [project]
  
• Zero-shot entity extraction from web pages. Panupong Pasupat, Percy Liang. Association for Computational Linguistics (ACL), 2014.

  @inproceedings{pasupat2014extraction,
    author = {P. Pasupat and P. Liang},
    booktitle = {Association for Computational Linguistics (ACL)},
    title = {Zero-shot Entity Extraction from Web Pages},
    year = {2014},
  }
  

  [bib] [paper] [slides] [project]
  
• Estimating latent-variable graphical models using moments and likelihoods. Arun Chaganty, Percy Liang. International Conference on Machine Learning (ICML), 2014.

  @inproceedings{chaganty2014graphical,
    author = {A. Chaganty and P. Liang},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {Estimating Latent-Variable Graphical Models using Moments and Likelihoods},
    year = {2014},
  }
  

  [bib] [paper] [slides]
  
• Adaptivity and optimism: an improved exponentiated gradient algorithm. Jacob Steinhardt, Percy Liang. International Conference on Machine Learning (ICML), 2014.

  @inproceedings{steinhardt2014eg,
    author = {J. Steinhardt and P. Liang},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {Adaptivity and Optimism: An Improved Exponentiated Gradient Algorithm},
    year = {2014},
  }
  

  [bib] [paper]
  
• Filtering with abstract particles. Jacob Steinhardt, Percy Liang. International Conference on Machine Learning (ICML), 2014.

  @inproceedings{steinhardt2014filtering,
    author = {J. Steinhardt and P. Liang},
    booktitle = {International Conference on Machine Learning (ICML)},
    pages = {727--735},
    title = {Filtering with Abstract Particles},
    year = {2014},
  }
  

  [bib] [paper] [supplemental material]
  
• Altitude training: strong bounds for single-layer dropout. Stefan Wager, Will Fithian, Sida I. Wang, Percy Liang. Advances in Neural Information Processing Systems (NeurIPS), 2014.

  @inproceedings{wager2014altitude,
    author = {S. Wager and W. Fithian and S. I. Wang and P. Liang},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Altitude Training: Strong Bounds for Single-Layer Dropout},
    year = {2014},
  }
  

  [bib] [paper]
  
• Simple MAP inference via low-rank relaxations. Roy Frostig, Sida I. Wang, Percy Liang, Chris Manning. Advances in Neural Information Processing Systems (NeurIPS), 2014.

  @inproceedings{frostig2014lowrank,
    author = {R. Frostig and S. I. Wang and P. Liang and C. Manning},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Simple {MAP} inference via low-rank relaxations},
    year = {2014},
  }
  

  [bib] [paper] [CodaLab]
  
• Relaxations for inference in restricted Boltzmann machines. Sida I. Wang, Roy Frostig, Percy Liang, Chris Manning. International Conference on Learning Representations Workshop (ICLR), 2014.

  @inproceedings{wang2014iqp,
    author = {S. I. Wang and R. Frostig and P. Liang and C. Manning},
    booktitle = {International Conference on Learning Representations Workshop (ICLR)},
    title = {Relaxations for inference in restricted {B}oltzmann machines},
    year = {2014},
  }
  

  [bib] [paper]
  

2013

• Lambda dependency-based compositional semantics. Percy Liang. arXiv preprint arXiv:1309.4408, 2013.

  @article{liang2013lambdadcs,
    author = {P. Liang},
    journal = {arXiv preprint arXiv:1309.4408},
    title = {Lambda Dependency-Based Compositional Semantics},
    year = {2013},
  }
  

  [bib] [paper]
  
• Semantic parsing on Freebase from question-answer pairs. Jonathan Berant, Andrew Chou, Roy Frostig, Percy Liang. Empirical Methods in Natural Language Processing (EMNLP), 2013.

  @inproceedings{berant2013freebase,
    author = {J. Berant and A. Chou and R. Frostig and P. Liang},
    booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
    title = {Semantic Parsing on {F}reebase from Question-Answer Pairs},
    year = {2013},
  }
  

  [bib] [paper] [supplemental material] [slides] [project]
  
• Feature noising for log-linear structured prediction. Sida I. Wang, Mengqiu Wang, Stefan Wager, Percy Liang, Chris Manning. Empirical Methods in Natural Language Processing (EMNLP), 2013.

  @inproceedings{wang2013noising,
    author = {S. I. Wang and M. Wang and S. Wager and P. Liang and C. Manning},
    booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
    title = {Feature Noising for Log-linear Structured Prediction},
    year = {2013},
  }
  

  [bib] [paper]
  
• Dropout training as adaptive regularization. Stefan Wager, Sida I. Wang, Percy Liang. Advances in Neural Information Processing Systems (NeurIPS), 2013.

  @inproceedings{wager2013dropout,
    author = {S. Wager and S. I. Wang and P. Liang},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Dropout Training as Adaptive Regularization},
    year = {2013},
  }
  

  [bib] [paper] [poster]
  
• Spectral experts for estimating mixtures of linear regressions. Arun Chaganty, Percy Liang. International Conference on Machine Learning (ICML), 2013.

  @inproceedings{chaganty13regression,
    author = {A. Chaganty and P. Liang},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {Spectral Experts for Estimating Mixtures of Linear Regressions},
    year = {2013},
  }
  

  [bib] [paper]
  
• Video event understanding using natural language descriptions. Vignesh Ramanathan, Percy Liang, Li Fei-Fei. International Conference on Computer Vision (ICCV), 2013.

  @inproceedings{ramanathan2013event,
    author = {V. Ramanathan and P. Liang and L. Fei-Fei},
    booktitle = {International Conference on Computer Vision (ICCV)},
    title = {Video Event Understanding using Natural Language Descriptions},
    year = {2013},
  }
  

  [bib] [paper]
  
• A data driven approach for algebraic loop invariants. Rahul Sharma, Saurabh Gupta, Bharath Hariharan, Alex Aiken, Percy Liang, Aditya V. Nori. European Symposium on Programming (ESOP), 2013.

  @inproceedings{sharma13algebraic,
    author = {R. Sharma and S. Gupta and B. Hariharan and A. Aiken and P. Liang and A. V. Nori},
    booktitle = {European Symposium on Programming (ESOP)},
    title = {A Data Driven Approach for Algebraic Loop Invariants},
    year = {2013},
  }
  

  [bib] [paper]
  

2012

• Identifiability and unmixing of latent parse trees. Daniel Hsu, Sham M. Kakade, Percy Liang. Advances in Neural Information Processing Systems (NeurIPS), 2012.

  This paper explores unsupervised learning of parsing models along two directions. First, which models are identifiable from infinite data? We use a general technique for numerically checking identifiability based on the rank of a Jacobian matrix, and apply it to several standard constituency and dependency parsing models. Second, for identifiable models, how do we estimate the parameters efficiently? EM suffers from local optima, while recent work using spectral methods cannot be directly applied since the topology of the parse tree varies across sentences. We develop a strategy, unmixing, which deals with this additional complexity for restricted classes of parsing models.

  @inproceedings{hsu12identifiability,
    author = {D. Hsu and S. M. Kakade and P. Liang},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Identifiability and Unmixing of Latent Parse Trees},
    year = {2012},
  }
  

  [abstract] [bib] [paper] [techreport] [poster]
  

2011

• Learning dependency-based compositional semantics. Percy Liang, Michael I. Jordan, Dan Klein. Association for Computational Linguistics (ACL), 2011.

  Compositional question answering begins by mapping questions to logical forms, but training a semantic parser to perform this mapping typically requires the costly annotation of the target logical forms. In this paper, we learn to map questions to answers via latent logical forms, which are induced automatically from question-answer pairs. In tackling this challenging learning problem, we introduce a new semantic representation which highlights a parallel between dependency syntax and efficient evaluation of logical forms. On two standard semantic parsing benchmarks (GEO and JOBS), our system obtains the highest published accuracies, despite using less supervision than existing systems.

  Task: learn to map questions to answers via latent logical forms.
  Contribution: new tree-based semantic representation.
  Result: surpass state-of-the-art on semantic parsing with less supervision.

  @inproceedings{liang11dcs,
    author = {P. Liang and M. I. Jordan and D. Klein},
    booktitle = {Association for Computational Linguistics (ACL)},
    pages = {590--599},
    title = {Learning Dependency-Based Compositional Semantics},
    year = {2011},
  }
  

  [abstract] [brief] [bib] [paper] [thesis] [journal] [slides] [code]
  
• Scaling up abstraction refinement via pruning. Percy Liang, Mayur Naik. Programming Language Design and Implementation (PLDI), 2011.

  Many static analyses do not scale as they are made more precise. For example, increasing the amount of context sensitivity in a k-limited pointer analysis causes the number of contexts to grow exponentially with k. Iterative refinement techniques can mitigate this growth by starting with a coarse abstraction and only refining parts of the abstraction that are deemed relevant with respect to a given client.
  In this paper, we introduce a new technique called pruning that uses client feedback in a different way. The basic idea is to use coarse abstractions to prune away parts of the program analysis deemed irrelevant for proving a client query, and then using finer abstractions on the sliced program analysis. For a k-limited pointer analysis, this approach amounts to adaptively refining and pruning a set of prefix patterns representing the contexts relevant for the client. By pruning, we are able to scale up to much more expensive abstractions than before. We also prove that the pruned analysis is both sound and complete, that is, it yields the same results as an analysis that uses a more expensive abstraction directly without pruning.

  Idea: run cheap analysis, use client feedback to prune away irrelvant parts of program analysis (think program slicing); then run expensive analysis.
  Theoretical result: pruning is sound and complete.
  Empirical result: we can use much richer $k$-object-sensitivity abstractions.

  @inproceedings{liang11pruning,
    author = {P. Liang and M. Naik},
    booktitle = {Programming Language Design and Implementation (PLDI)},
    title = {Scaling up Abstraction Refinement via Pruning},
    year = {2011},
  }
  

  [abstract] [brief] [bib] [paper] [slides]
  
• Learning minimal abstractions. Percy Liang, Omer Tripp, Mayur Naik. Principles of Programming Languages (POPL), 2011.

  Static analyses are generally parametrized by an abstraction which is chosen from a family of abstractions. We are interested in flexible families of abstractions with many parameters, as these families can allow one to increase precision in ways tailored to the client without sacrificing scalability. For example, we consider k-limited points-to analyses where each call site and allocation site in a program can have a different k value. We then ask a natural question in this paper: What is the minimal (coarsest) abstraction in a given family which is able to prove a set of queries? In addressing this question, we make the following two contributions: (i) We introduce two machine learning algorithms for efficiently finding a minimal abstraction; and (ii) for a static race detector backed by a k-limited points-to analysis, we show empirically that minimal abstractions are actually quite coarse: It suffices to provide context/object sensitivity to a very small fraction (0.4--2.3%) of the sites to yield equally precise results as providing context/object sensitivity uniformly to all sites.

  Question: how small is the smallest abstraction needed to prove a query?
  Empirical answer: very small (less than 2.5% sites need to be treated context-sensitively for k-limited analyses for race detection).
  Found this answer using a new machine learning algorithm that exploits this sparsity.

  @inproceedings{liang11minimal,
    author = {P. Liang and O. Tripp and M. Naik},
    booktitle = {Principles of Programming Languages (POPL)},
    title = {Learning Minimal Abstractions},
    year = {2011},
  }
  

  [abstract] [brief] [bib] [paper] [slides]
  

2010

• A game-theoretic approach to generating spatial descriptions. Dave Golland, Percy Liang, Dan Klein. Empirical Methods in Natural Language Processing (EMNLP), 2010.

  @inproceedings{golland2010pragmatics,
    author = {D. Golland and P. Liang and D. Klein},
    booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
    pages = {410--419},
    title = {A Game-theoretic Approach to Generating Spatial Descriptions},
    year = {2010},
  }
  

  [bib] [paper] [slides]
  
• A simple domain-independent probabilistic approach to generation. Gabor Angeli, Percy Liang, Dan Klein. Empirical Methods in Natural Language Processing (EMNLP), 2010.

  We present a simple, robust generation system which performs content selection and surface realization in a unified, domain-independent framework. In our approach, we break up the end-to-end generation process into a sequence of local decisions, arranged hierarchically and each trained discriminatively. We deployed our system in three different domains---Robocup sportscasting, technical weather forecasts, and common weather forecasts, obtaining results comparable to state-of-the-art domain-specific systems both in terms of BLEU scores and human evaluation.

  Model natural language generation as a sequence of local decisions, each backed by a log-linear model.
  Advantage: can use arbitrary expressive features, works across multiple domains.

  @inproceedings{angeli10generation,
    author = {G. Angeli and P. Liang and D. Klein},
    booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
    title = {A Simple Domain-Independent Probabilistic Approach to Generation},
    year = {2010},
  }
  

  [abstract] [brief] [bib] [paper] [slides]
  
• A dynamic evaluation of static heap abstractions. Percy Liang, Omer Tripp, Mayur Naik, Mooly Sagiv. Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA), 2010.

  The quality of a static analysis of heap-manipulating programs is largely determined by its heap abstraction. Object allocation sites are a commonly-used abstraction, but are too coarse for some clients. The goal of this paper is to investigate how various refinements of allocation sites can improve precision. In particular, we consider abstractions that use call stack, object recency, and heap connectivity information. We measure the precision of these abstractions dynamically for four different clients motivated by concurrency and on nine Java programs chosen from the DaCapo benchmark suite. Our dynamic results shed new light on aspects of heap abstractions that matter for precision, which allows us to more effectively navigate the large space of possible heap abstractions.

  Question: what aspects of a heap abstraction matter?
  Methodology: run program (9 DaCapo benchmarks) dynamically, compute static heap abstractions (3 dimensions of refinement: context sensitivity, object recency, and shape analysis), answer client queries (4 clients based on concurrency).

  @inproceedings{liang10abstraction,
    author = {P. Liang and O. Tripp and M. Naik and M. Sagiv},
    booktitle = {Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA)},
    title = {A Dynamic Evaluation of Static Heap Abstractions},
    year = {2010},
  }
  

  [abstract] [brief] [bib] [paper] [slides]
  
• Learning programs: a hierarchical Bayesian approach. Percy Liang, Michael I. Jordan, Dan Klein. International Conference on Machine Learning (ICML), 2010.

  We are interested in learning programs for multiple related tasks given only a few training examples per task. Since the program for a single task is underdetermined by its data, we introduce a nonparametric hierarchical Bayesian prior over programs which shares statistical strength across multiple tasks. The key challenge is to parametrize this multi-task sharing. For this, we introduce a new representation of programs based on combinatory logic and provide an MCMC algorithm that can perform safe program transformations on this representation to reveal shared inter-program substructures.

  Programs are trees, subprograms are subtrees, which can be shared across tasks. Combinators refactor programs to expose the appropriate subprograms.

  @inproceedings{liang10programs,
    author = {P. Liang and M. I. Jordan and D. Klein},
    booktitle = {International Conference on Machine Learning (ICML)},
    pages = {639--646},
    title = {Learning Programs: A Hierarchical {B}ayesian Approach},
    year = {2010},
  }
  

  [abstract] [brief] [bib] [paper] [slides] [code]
  
• On the interaction between norm and dimensionality: multiple regimes in learning. Percy Liang, Nati Srebro. International Conference on Machine Learning (ICML), 2010.

  A learning problem might have several measures of complexity (e.g., norm and dimensionality) that affect the generalization error. What is the interaction between these complexities? Dimension-free learning theory bounds and parametric asymptotic analyses each provide a partial picture of the full learning curve. In this paper, we use high-dimensional asymptotics on two classical problems---mean estimation and linear regression---to explore the learning curve more completely. We show that these curves exhibit multiple regimes, where in each regime, the excess risk is controlled by a subset of the problem complexities.

  Goal: understand excess risk as a function of sample size and problem complexity. On simple examples, show that asymptotic risk has multiple regimes, each controlled by different complexities.

  @inproceedings{liang10regimes,
    author = {P. Liang and N. Srebro},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning},
    year = {2010},
  }
  

  [abstract] [brief] [bib] [paper] [slides]
  
• Type-Based MCMC. Percy Liang, Michael I. Jordan, Dan Klein. North American Association for Computational Linguistics (NAACL), 2010.

  Most existing algorithms for learning latent-variable models---such as EM and existing Gibbs samplers---are token-based, meaning that they update the variables associated with one sentence at a time. The incremental nature of these methods makes them susceptible to local optima/slow mixing. In this paper, we introduce a type-based sampler, which updates a block of variables, identified by a type, which spans multiple sentences. We show improvements on part-of-speech induction, word segmentation, and learning tree-substitution grammars.

  NLP perspective: goal is to avoid local optima by processing all tokens associated with a type at once instead of one token or sentence at a time.
  Sampling perspective: new type of block sampling that exploits exchangeability.

  @inproceedings{liang10type,
    author = {P. Liang and M. I. Jordan and D. Klein},
    booktitle = {North American Association for Computational Linguistics (NAACL)},
    title = {Type-Based {MCMC}},
    year = {2010},
  }
  

  [abstract] [brief] [bib] [paper] [slides] [code]
  

2009

• Asymptotically optimal regularization in smooth parametric models. Percy Liang, Francis Bach, Guillaume Bouchard, Michael I. Jordan. Advances in Neural Information Processing Systems (NeurIPS), 2009.

  Many types of regularization schemes have been employed in statistical learning, each one motivated by some assumption about the problem domain. In this paper, we present a unified asymptotic analysis of smooth regularizers, which allows us to see how the validity of these assumptions impacts the success of a particular regularizer. In addition, our analysis motivates an algorithm for optimizing regularization parameters, which in turn can be analyzed within our framework. We apply our analysis to several examples, including hybrid generative-discriminative learning and multi-task learning.

  Setting: estimator defined by minimizing loss plus regularization.
  Question: what is the best regularizer to use?
  This is hard to optimize, so use a Taylor expansion instead, yielding a interpretable closed form solution.

  @inproceedings{liang09regularization,
    author = {P. Liang and F. Bach and G. Bouchard and M. I. Jordan},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Asymptotically Optimal Regularization in Smooth Parametric Models},
    year = {2009},
  }
  

  [abstract] [brief] [bib] [paper] [techreport] [poster]
  
• Probabilistic grammars and hierarchical Dirichlet processes. Percy Liang, Michael I. Jordan, Dan Klein. The Oxford Handbook of Applied Bayesian Analysis, 2009.

  Probabilistic context-free grammars (PCFGs) have played an important role in the modeling of syntax in natural language processing and other applications, but choosing the proper model complexity is often difficult. We present a nonparametric Bayesian generalization of the PCFG based on the hierarchical Dirichlet process (HDP). In our HDP-PCFG model, the effective complexity of the grammar can grow with increasing data. We describe an efficient variational inference algorithm for our model and present experiments on both a synthetic grammar induction task and a large-scale natural language parsing task.

  Details of the EMNLP 2007 paper + general background, empirical intuitions, and derivations for structured mean-field + a small grammar induction experiment.

  @incollection{liang09hdppcfg,
    author = {P. Liang and M. I. Jordan and D. Klein},
    booktitle = {The Oxford Handbook of Applied Bayesian Analysis},
    title = {Probabilistic grammars and hierarchical {D}irichlet processes},
    year = {2009},
  }
  

  [abstract] [brief] [bib] [paper]
  
• Learning semantic correspondences with less supervision. Percy Liang, Michael I. Jordan, Dan Klein. Association for Computational Linguistics and International Joint Conference on Natural Language Processing (ACL-IJCNLP), 2009.

  A central problem in grounded language acquisition is learning the correspondences between a rich world state and a stream of text which references that world state. To deal with the high degree of ambiguity present in this setting, we present a generative model that simultaneously segments the text into utterances and maps each utterance to a meaning representation grounded in the world state. We show that our model generalizes across three domains of increasing difficulty---Robocup sportscasting, weather forecasts (a new domain), and NFL recaps.

  Stuff happens in the world. A text talks about it. Our goal: learn the correspondence between the two.
  Approach: probabilistic model capturing identification of entities/events in the world, segmentation of the text, and alignment between the two.

  @inproceedings{liang09semantics,
    author = {P. Liang and M. I. Jordan and D. Klein},
    booktitle = {Association for Computational Linguistics and International Joint Conference on Natural Language Processing (ACL-IJCNLP)},
    pages = {91--99},
    title = {Learning Semantic Correspondences with Less Supervision},
    year = {2009},
  }
  

  [abstract] [brief] [bib] [paper] [slides] [code] [data]
  
• Learning from measurements in exponential families. Percy Liang, Michael I. Jordan, Dan Klein. International Conference on Machine Learning (ICML), 2009.

  Given a model family and a set of unlabeled examples, one could either label specific examples or state general constraints---both provide information about the desired model. In general, what is the most cost-effective way to learn? To address this question, we introduce measurements, a general class of mechanisms for providing information about a target model. We present a Bayesian decision-theoretic framework, which allows us to both integrate diverse measurements and choose new measurements to make. We use a variational inference algorithm, which exploits exponential family duality. The merits of our approach are demonstrated on two sequence labeling tasks.

  Goal: learning with minimum human effort.
  Things human can do: label data, provide constraints---in general, make measurements.
  Use Bayesian decision theory to choose optimal measurements.

  @inproceedings{liang09measurements,
    author = {P. Liang and M. I. Jordan and D. Klein},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {Learning from Measurements in Exponential Families},
    year = {2009},
  }
  

  [abstract] [brief] [bib] [paper] [slides]
  
• Online EM for unsupervised models. Percy Liang, Dan Klein. North American Association for Computational Linguistics (NAACL), 2009.

  The (batch) EM algorithm plays an important role in unsupervised induction, but it sometimes suffers from slow convergence. In this paper, we show that online variants (1) provide significant speedups and (2) can even find better solutions than those found by batch EM. We support these findings on four unsupervised tasks: part-of-speech tagging, document classification, word segmentation, and word alignment.

  What you'd expect: online is faster than batch.
  What you might not expect: online gets better accuarcy than batch.

  @inproceedings{liang09online,
    author = {P. Liang and D. Klein},
    booktitle = {North American Association for Computational Linguistics (NAACL)},
    pages = {611--619},
    title = {Online {EM} for Unsupervised Models},
    year = {2009},
  }
  

  [abstract] [brief] [bib] [paper] [slides] [code]
  

2008

• An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. Percy Liang, Michael I. Jordan. International Conference on Machine Learning (ICML), 2008. Best student paper award.

  Statistical and computational concerns have motivated parameter estimators based on various forms of likelihood, e.g., joint, conditional, and pseudolikelihood. In this paper, we present a unified framework for studying these estimators, which allows us to compare their relative (statistical) efficiencies. Our asymptotic analysis suggests that modeling more of the data tends to reduce variance, but at the cost of being more sensitive to model misspecification. We present experiments validating our analysis.

  Derive general expression for the asymptotic risk of composite likelihood estimators in exponential families.
  This allows us to compare the various estimators.

  @inproceedings{liang08asymptotics,
    author = {P. Liang and M. I. Jordan},
    booktitle = {International Conference on Machine Learning (ICML)},
    pages = {584--591},
    title = {An Asymptotic Analysis of Generative, Discriminative, and Pseudolikelihood Estimators},
    year = {2008},
  }
  

  [abstract] [brief] [bib] [paper] [slides]
  
• Structure compilation: trading structure for features. Percy Liang, Hal Daume, Dan Klein. International Conference on Machine Learning (ICML), 2008.

  Structured models often achieve excellent performance but can be slow at test time. We investigate structure compilation, where we replace structure with features, which are often computationally simpler but unfortunately statistically more complex. We analyze this tradeoff theoretically and empirically on three natural language processing tasks. We also introduce a simple method to transfer predictive power from structure to features via unlabeled data, while incurring a minimal statistical penalty.

  How much do we lose by throwing out edge features in CRFs and adding node features?
  Studies the approximation, estimation, computational aspects of the tradeoff.

  @inproceedings{liang08structure,
    author = {P. Liang and H. Daume and D. Klein},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {Structure Compilation: Trading Structure for Features},
    year = {2008},
  }
  

  [abstract] [brief] [bib] [paper] [slides]
  
• Analyzing the errors of unsupervised learning. Percy Liang, Dan Klein. Human Language Technology and Association for Computational Linguistics (HLT/ACL), 2008.

  We identify four types of errors that unsupervised induction systems make and study each one in turn. Our contributions include (1) using a meta-model to analyze the incorrect biases of a model in a systematic way, (2) providing an efficient and robust method of measuring distance between two parameter settings of a model, and (3) showing that local optima issues which typically plague EM can be somewhat alleviated by increasing the number of training examples. We conduct our analyses on three models: the HMM, the PCFG, and a simple dependency model.

  Error decomposition: approximation, identifiability, estimation, optimization errors.
  Used meta-model to analyze approximation error.
  Empirically observed that more data reduces optimization error.

  @inproceedings{liang08errors,
    author = {P. Liang and D. Klein},
    booktitle = {Human Language Technology and Association for Computational Linguistics (HLT/ACL)},
    title = {Analyzing the Errors of Unsupervised Learning},
    year = {2008},
  }
  

  [abstract] [brief] [bib] [paper] [slides]
  
• Learning bilingual lexicons from monolingual corpora. Aria Haghighi, Percy Liang, Taylor Berg-Kirkpatrick, Dan Klein. Human Language Technology and Association for Computational Linguistics (HLT/ACL), 2008.

  We present a method for learning bilingual translation lexicons from monolingual corpora. Word types in each language are characterized by purely monolingual features, such as context counts and orthographic substrings. Translations are induced using a generative model based on canonical correlation analysis, which explains the monolingual lexicons in terms of latent matchings. We show that high-precision lexicons can be learned in a variety of language pairs and from a range of corpus types.

  By using CCA, can do word alignment without the usual sentence-aligned corpora.

  @inproceedings{haghighi08lexicon,
    author = {A. Haghighi and P. Liang and T. Berg-Kirkpatrick and D. Klein},
    booktitle = {Human Language Technology and Association for Computational Linguistics (HLT/ACL)},
    title = {Learning Bilingual Lexicons from Monolingual Corpora},
    year = {2008},
  }
  

  [abstract] [brief] [bib] [paper] [code]
  
• Agreement-Based learning. Percy Liang, Dan Klein, Michael I. Jordan. Advances in Neural Information Processing Systems (NeurIPS), 2008.

  The learning of probabilistic models with many hidden variables and non-decomposable dependencies is an important and challenging problem. In contrast to traditional approaches based on approximate inference in a single intractable model, our approach is to train a set of tractable submodels by encouraging them to agree on the hidden variables. This allows us to capture non-decomposable aspects of the data while still maintaining tractability. We propose an objective function for our approach, derive EM-style algorithms for parameter estimation, and demonstrate their effectiveness on three challenging real-world learning tasks.

  Setting: unsupervised learning.
  Alternative to approximate inference: make two tractable models and train them to agree.
  Advantage: maintain existing tractable inference procedures as black-boxes.

  @inproceedings{liang08agreement,
    author = {P. Liang and D. Klein and M. I. Jordan},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Agreement-Based Learning},
    year = {2008},
  }
  

  [abstract] [brief] [bib] [paper] [poster]
  
• A probabilistic approach to language change. Alexandre Bouchard-Côté, Percy Liang, Tom Griffiths, Dan Klein. Advances in Neural Information Processing Systems (NeurIPS), 2008.

  We present a probabilistic approach to language change in which word forms are represented by phoneme sequences that undergo stochastic edits along the branches of a phylogenetic tree. This framework combines the advantages of the classical comparative method with the robustness of corpus-based probabilistic models. We use this framework to explore the consequences of two different schemes for defining probabilistic models of phonological change, evaluating these schemes by reconstructing ancient word forms of Romance languages. The result is an efficient inference procedure for automatically inferring ancient word forms from modern languages, which can be generalized to support inferences about linguistic phylogenies.

  Feature-based generative model of phonemes of words in a phylogeny of languages.

  @inproceedings{bouchard08language,
    author = {A. Bouchard-Côté and P. Liang and T. Griffiths and D. Klein},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {A Probabilistic Approach to Language Change},
    year = {2008},
  }
  

  [abstract] [brief] [bib] [paper] [poster]
  

2007

• Structured Bayesian nonparametric models with variational inference (tutorial). Percy Liang, Dan Klein. Association for Computational Linguistics (ACL), 2007.

  @inproceedings{liang07tutorial,
    author = {P. Liang and D. Klein},
    booktitle = {Association for Computational Linguistics (ACL)},
    title = {Structured {B}ayesian Nonparametric Models with Variational Inference (tutorial)},
    year = {2007},
  }
  

  [bib] [paper] [slides]
  
• A permutation-augmented sampler for Dirichlet process mixture models. Percy Liang, Michael I. Jordan, Ben Taskar. International Conference on Machine Learning (ICML), 2007.

  We introduce a new inference algorithm for Dirichlet process mixture models. While Gibbs sampling and variational methods focus on local moves, the new algorithm makes more global moves. This is done by introducing a permutation of the data points as an auxiliary variable. The algorithm is a blocked sampler which alternates between sampling the clustering and sampling the permutation. The key to the efficiency of this approach is that it is possible to use dynamic programming to consider all exponentially many clusterings consistent with a given permutation. We also show that random projections can be used to effectively sample the permutation. The result is a stochastic hill-climbing algorithm that yields burn-in times significantly smaller than those of collapsed Gibbs sampling.

  Task: clustering.
  Idea: conditioned on a permutation of the data points, one can consider all possible clusterings of those data points which are consistent with the permutation using dynamic programming.
  Idea: if data are ordered, can cluster using dynamic programming.
  Let this ordering be a random auxiliary variable and we get a sampler.

  @inproceedings{liang07permdp,
    author = {P. Liang and M. I. Jordan and B. Taskar},
    booktitle = {International Conference on Machine Learning (ICML)},
    title = {A permutation-augmented sampler for {D}irichlet process mixture models},
    year = {2007},
  }
  

  [abstract] [brief] [bib] [paper] [slides]
  
• The infinite PCFG using hierarchical Dirichlet processes. Percy Liang, Slav Petrov, Michael I. Jordan, Dan Klein. Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP/CoNLL), 2007.

  We present a nonparametric Bayesian model of tree structures based on the hierarchical Dirichlet process (HDP). Our HDP-PCFG model allows the complexity of the grammar to grow as more training data is available. In addition to presenting a fully Bayesian model for the PCFG, we also develop an efficient variational inference procedure. On synthetic data, we recover the correct grammar without having to specify its complexity in advance. We also show that our techniques can be applied to full-scale parsing applications by demonstrating its effectiveness in learning state-split grammars.

  A PCFG with an infinite number of states.
  Learning: variational inference.

  @inproceedings{liang07infpcfg,
    author = {P. Liang and S. Petrov and M. I. Jordan and D. Klein},
    booktitle = {Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP/CoNLL)},
    title = {The Infinite {PCFG} using Hierarchical {D}irichlet Processes},
    year = {2007},
  }
  

  [abstract] [brief] [bib] [paper] [slides]
  
• A probabilistic approach to diachronic phonology. Alexandre Bouchard-Côté, Percy Liang, Tom Griffiths, Dan Klein. Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP/CoNLL), 2007.

  We present a probabilistic model of diachronic phonology in which individual word forms undergo stochastic edits along the branches of a phylogenetic tree. Our approach allows us to achieve three goals with a single unified model: (1) reconstruction of both ancient and modern word forms, (2) discovery of general phonological changes, and (3) selection among different phylogenies. We learn our model using a Monte Carlo EM algorithm and present quantitative results validating the model.

  Generative model of phonemes of words in a phylogeny of languages

  @inproceedings{bouchard07diachronic,
    author = {A. Bouchard-Côté and P. Liang and T. Griffiths and D. Klein},
    booktitle = {Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP/CoNLL)},
    title = {A Probabilistic Approach to Diachronic Phonology},
    year = {2007},
  }
  

  [abstract] [brief] [bib] [paper]
  

2006

• An end-to-end discriminative approach to machine translation. Percy Liang, Alexandre Bouchard-Côté, Dan Klein, Ben Taskar. International Conference on Computational Linguistics and Association for Computational Linguistics (COLING/ACL), 2006.

  We present a perceptron-style discriminative approach to machine translation in which large feature sets can be exploited. Unlike discriminative reranking approaches, our system can take advantage of learned features in all stages of decoding. We first discuss several challenges to error-driven discriminative approaches. In particular, we explore different ways of updating parameters given a training example. We find that making frequent but smaller updates is preferable to making fewer but larger updates. Then, we discuss an array of features and show both how they quantitatively increase BLEU score and how they qualitatively interact on specific examples. One particular feature we investigate is a novel way to introduce learning into the initial phrase extraction process, which has previously been entirely heuristic.

  Task: machine translation.
  Idea: treat machine translation as a structured classification task (learn a map from input sentence to output sentence). Use a Perceptron-like algorithm: decode and update towards maximum BLEU scoring translation on the n-best list.

  @inproceedings{liang06discrimative,
    author = {P. Liang and A. Bouchard-Côté and D. Klein and B. Taskar},
    booktitle = {International Conference on Computational Linguistics and Association for Computational Linguistics (COLING/ACL)},
    title = {An End-to-End Discriminative Approach to Machine Translation},
    year = {2006},
  }
  

  [abstract] [brief] [bib] [paper] [slides]
  
• Alignment by agreement. Percy Liang, Ben Taskar, Dan Klein. North American Association for Computational Linguistics (NAACL), 2006.

  We present an unsupervised approach to symmetric word alignment in which two simple asymmetric models are trained jointly to maximize a combination of data likelihood and agreement between the models. Compared to the standard practice of intersecting predictions of independently-trained models, joint training provides a 32% reduction in AER. Moreover, a simple and efficient pair of HMM aligners provides a 29% reduction in AER over symmetrized IBM model 4 predictions.

  Task: unsupervised word alignment.
  Idea: Jointly train two HMM models (one in each direction) to encourage agreement. Uses a simple EM-like algorithm for training.
  Result: performance competitive with supervised methods (4.9 AER on Hansards).

  @inproceedings{liang06alignment,
    author = {P. Liang and B. Taskar and D. Klein},
    booktitle = {North American Association for Computational Linguistics (NAACL)},
    pages = {104--111},
    title = {Alignment by Agreement},
    year = {2006},
  }
  

  [abstract] [brief] [bib] [paper] [slides] [code]
  

2005

• Semi-Supervised learning for natural language. Percy Liang. Massachusetts Institute of Technology, 2005.

  Task: named-entity recognition and Chinese word segmentation
  Idea: create features based on unlabeled data to use in Perceptron learning in Markov or semi-Markov models

  @mastersthesis{liang05meng,
    author = {P. Liang},
    school = {Massachusetts Institute of Technology},
    title = {Semi-Supervised Learning for Natural Language},
    year = {2005},
  }
  

  [brief] [bib] [paper] [errata]
  
• A data structure for maintaining acyclicity in hypergraphs. Percy Liang, Nathan Srebro. Massachusetts Institute of Technology Technical Report, 2005.

  We introduce the first definition of hyperacyclicity for hypergraphs, a generalization of acyclicity in graphs.
  We provide a dynamic data structure for maintaining hyperacyclicity, a generalization of Tarjan's Union-Find algorithm.

  @techreport{liang05hypercycle,
    author = {P. Liang and N. Srebro},
    institution = {Massachusetts Institute of Technology},
    title = {A Data Structure for Maintaining Acyclicity in Hypergraphs},
    year = {2005},
  }
  

  [brief] [bib] [paper] [code]
  
• Linear programming in bounded tree-width Markov networks. Percy Liang, Nathan Srebro. Mathematical Programing for Data Mining and Machine Learning Workshop at McMaster University, 2005.

  @inproceedings{liang05mcmaster,
    author = {P. Liang and N. Srebro},
    booktitle = {Mathematical Programing for Data Mining and Machine Learning Workshop at McMaster University},
    title = {Linear Programming in Bounded Tree-width {M}arkov Networks},
    year = {2005},
  }
  

  [bib] [slides] [code]
  
• Efficient geometric algorithms for parsing in two dimensions. Percy Liang, Mukund Narasimhan, Michael Shilman, Paul Viola. International Conference on Document Analysis and Recognition (ICDAR), 2005.

  In parsing sequences using dynamic programming, the subproblems are continguous subsequences (quadratic in number of terminals). In parsing documents or images, the subproblems would be subsets of the terminals (exponential in number of terminals). We introduce (and unify) several ways to constrain these subsets using the geometric structure of the terminals.

  @inproceedings{liang05geometric,
    author = {P. Liang and M. Narasimhan and M. Shilman and P. Viola},
    booktitle = {International Conference on Document Analysis and Recognition (ICDAR)},
    title = {Efficient Geometric Algorithms for Parsing in Two Dimensions},
    year = {2005},
  }
  

  [brief] [bib] [paper]
  

2004

• Methods and experiments with bounded tree-width Markov networks. Percy Liang, Nathan Srebro. Massachusetts Institute of Technology Technical Report, 2004.

  Use a greedy procedure to find the maximum likelihood (or MDL) bounded tree-width Markov network (for tree-width 1, equivalent to Chow-Liu maximum spanning trees).

  @techreport{liang04markov,
    author = {P. Liang and N. Srebro},
    institution = {Massachusetts Institute of Technology},
    title = {Methods and Experiments With Bounded Tree-width {M}arkov Networks},
    year = {2004},
  }
  

  [brief] [bib] [paper] [code]
  

2003

• How much of a hypertree can be captured by windmills? Percy Liang, Nathan Srebro. Massachusetts Institute of Technology Technical Report, 2003.

  Use linear programming to find worst case inputs to a dynamic program in order to explore the tightness of a bound for approximating maximum weight hypertrees with windmill farms.

  @techreport{liang03maxwmfarm,
    author = {P. Liang and N. Srebro},
    institution = {Massachusetts Institute of Technology},
    title = {How Much Of A Hypertree Can Be Captured By Windmills?},
    year = {2003},
  }
  

  [brief] [bib] [paper] [code]