Stefano Ermon

Stefano Ermon    

Associate Professor
Department of Computer Science
Stanford University

Office: Gates Building #330
Phone: (650) 498-9942
Email: ermon AT cs.stanford.edu

Group Website       Research Blog

About Me

I am an Associate Professor in the Department of Computer Science at Stanford University. I am affiliated with the Artificial Intelligence Lab. I am also a fellow of the Woods Institute for the Environment. My research is in machine learning and generative AI. I like to develop principled methods motivated by concrete real-world applications and problems of broad societal relevance.

Teaching

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Honors and Awards

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Publications

2024

2023

2022

2021

2020

More Publications

2019

  • Chi-Sing Ho, Neal Jean, Catherine A. Hogan, Lena Blackmon, Stefanie S. Jeffrey, Mark Holodniy, Niaz Banaei, Amr A. E. Saleh, Stefano Ermon, Jennifer Dionne
    Rapid Identification of Pathogenic Bacteria using Raman Spectroscopy and Deep Learning [PDF]
    Nature Communications. In Nature Communications, 30 Oct 2019, Issue 10, Number 4927, DOI: 10.1038/s41467-019-12898-9.
  • Yang Song, Stefano Ermon
    Generative Modeling by Estimating Gradients of the Data Distribution (Oral Presentation) [PDF] [Code]
    NeurIPS-19. In Proc. 33rd Annual Conference on Neural Information Processing Systems, 2019.
  • Aditya Grover, Jiaming Song, Alekh Agarwal, Kenneth Tran, Ashish Kapoor, Eric Horvitz, Stefano Ermon
    Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting [PDF]
    NeurIPS-19. In Proc. 33rd Annual Conference on Neural Information Processing Systems, 2019.
  • Yang Song, Chenlin Meng, Stefano Ermon
    MintNet: Building Invertible Neural Networks with Masked Convolutions [PDF] [Code]
    NeurIPS-19. In Proc. 33rd Annual Conference on Neural Information Processing Systems, 2019.
  • Lantao Yu, Tianhe Yu, Chelsea Finn, Stefano Ermon
    Meta-Inverse Reinforcement Learning with Probabilistic Context Variables [PDF] [Code]
    NeurIPS-19. In Proc. 33rd Annual Conference on Neural Information Processing Systems, 2019.
  • Jonathan Kuck, Tri Dao, Hamid Rezatofighi, Ashish Sabharwal, Stefano Ermon
    Approximating the Permanent by Sampling from Adaptive Partitions [PDF]
    NeurIPS-19. In Proc. 33rd Annual Conference on Neural Information Processing Systems, 2019.
  • Sawyer Birnbaum, Volodymyr Kuleshov, Zayd Enam, Pang Wei Koh, Stefano Ermon
    Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations [PDF]
    NeurIPS-19. In Proc. 33rd Annual Conference on Neural Information Processing Systems, 2019.
  • Carla Gomes, Thomas Dietterich, Christopher Barrett, Jon Conrad, Bistra Dilkina, Stefano Ermon, Fei Fang, Andrew Farnsworth, Alan Fern, Xiaoli Fern, Daniel Fink, Douglas Fisher, Alexander Flecker, Daniel Freund, Angela Fuller, John Gregoire, John Hopcroft, Steve Kelling, Zico Kolter, Warren Powell, Nicole Sintov, John Selker, Bart Selman, Daniel Sheldon, David Shmoys, Milind Tambe, Weng-Keen Wong, Christopher Wood, Xiaojian Wu, Yexiang Xue, Amulya Yadav, Abdul-Aziz Yakubu, Mary Lou Zeeman
    Computational Sustainability: Computing for a Better World and a Sustainable Future [PDF]
    CACM. In Communications of the ACM, September 2019, Vol. 62 No. 9, Pages 56-65.
  • Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon
    Sliced Score Matching: A Scalable Approach to Density and Score Estimation [PDF] [Code]
    UAI-19. In Proc. 35th Conference on Uncertainty in Artificial Intelligence, 2019.
  • Jonathan Kuck, Tri Dao, Shengjia Zhao, Burak Bartan, Ashish Sabharwal, Stefano Ermon
    Adaptive Hashing for Model Counting [PDF] [Code]
    UAI-19. In Proc. 35th Conference on Uncertainty in Artificial Intelligence, 2019.
  • Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang, David Lobell, Marshall Burke, Stefano Ermon
    Learning to Interpret Satellite Images using Wikipedia [PDF] [Code]
    IJCAI-19. In Proc. 28th International Joint Conference on Artificial Intelligence, 2019.
  • Michael Xie, Stefano Ermon
    Reparameterizable Subset Sampling via Continuous Relaxations [PDF] [Code]
    IJCAI-19. In Proc. 28th International Joint Conference on Artificial Intelligence, 2019.
  • Evan Sheehan, Chenlin Meng, Matthew Tan, Burak Uzkent, Neal Jean, David Lobell, Marshall Burke, Stefano Ermon
    Predicting Economic Development using Geolocated Wikipedia Articles [PDF] [Code]
    KDD-19. In Proc. 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2019.
  • Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon
    Neural Joint-Source Channel Coding [PDF] [Code]
    ICML-19. In Proc. 36th International Conference on Machine Learning, 2019.
  • Aditya Grover, Aaron Zweig, Stefano Ermon
    Iterative Deep Generative Modeling of Large Graphs [PDF] [Code]
    ICML-19. In Proc. 36th International Conference on Machine Learning, 2019.
  • Lantao Yu, Jiaming Song, Stefano Ermon
    Multi-Agent Adversarial Inverse Reinforcement Learning [PDF] [Code]
    ICML-19. In Proc. 36th International Conference on Machine Learning, 2019.
  • Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon
    Calibrated Model-Based Deep Reinforcement Learning [PDF] [Code]
    ICML-19. In Proc. 36th International Conference on Machine Learning, 2019.
  • Hongyu Ren, Shengjia Zhao, Stefano Ermon
    Adaptive Antithetic Sampling for Variance Reduction [PDF]
    ICML-19. In Proc. 36th International Conference on Machine Learning, 2019.
  • Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon
    Stochastic Optimization of Sorting Networks via Continuous Relaxations [PDF] [Code]
    ICLR-19. In Proc. 7th International Conference on Learning Representations, 2019.
  • Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon
    Learning Neural PDE Solvers with Convergence Guarantees [PDF] [Code]
    ICLR-19. In Proc. 7th International Conference on Learning Representations, 2019.
  • Mike Wu, Noah Goodman, Stefano Ermon
    Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference [PDF] [Code]
    AISTATS-19. In Proc. 22nd International Conference on Artificial Intelligence and Statistics, 2019.
  • Aditya Grover, Stefano Ermon
    Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization [PDF] [Code]
    AISTATS-19. In Proc. 22nd International Conference on Artificial Intelligence and Statistics, 2019.
  • Rui Shu, Hung Bui, Jay Whang, Stefano Ermon
    Training Variational Autoencoders with Buffered Stochastic Variational Inference [PDF]
    AISTATS-19. In Proc. 22nd International Conference on Artificial Intelligence and Statistics, 2019.
  • Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon
    Learning Controllable Fair Representations [PDF] [Code]
    AISTATS-19. In Proc. 22nd International Conference on Artificial Intelligence and Statistics, 2019.
  • Wenjie Hu, Jay Harshadbhai Patel, Zoe-Alanah Robert, Paul Novosad, Samuel Asher, Zhongyi Tang, Marshall Burke, David Lobell, Stefano Ermon
    Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery [PDF]
    AIES-19. In Proc. 1st AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, 2019.
  • Yuwei Mao, Xuelong Wang, Sihao Xia, Kai Zhang, Chenxi Wei, Seongmin Bak, Zulipiya Shadike, Xuejun Liu, Yang Yang, Rong Xu, Piero Pianetta, Stefano Ermon, Eli Stavitski, Kejie Zhao, Zhengrui Xu, Feng Lin, Xiao-Qing Yang, Enyuan Hu, Yijin Liu
    High-Voltage Charging-Induced Strain, Heterogeneity, and Micro-Cracks in Secondary Particles of a Nickel-Rich Layered Cathode Material [PDF]
    Advanced Functional Materials. In Advanced Functional Materials, 2019, Vol. 29 No. 18, Pages 1900247.
  • Jian Wei Khor, Neal Jean, Eric S Luxenberg, Stefano Ermon, Sindy K Y Tang
    Using Machine Learning to Discover Shape Descriptors for Predicting Emulsion Stability in a Microfluidic Channel [PDF]
    Soft Matter. In Soft Matter, 2019, Vol. 15 No. 6, Pages 1361-1372.
  • Neal Jean, Sherrie Wang, Anshul Samar, George Azzari, David Lobell, Stefano Ermon
    Tile2Vec: Unsupervised representation learning for spatially distributed data [PDF] [Code]
    AAAI-19. In Proc. 33rd AAAI Conference on Artificial Intelligence, 2019.
  • Shengjia Zhao, Jiaming Song, Stefano Ermon
    InfoVAE: Balancing Learning and Inference in Variational Autoencoders [PDF] [Code]
    AAAI-19. In Proc. 33rd AAAI Conference on Artificial Intelligence, 2019.

2018

  • Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon
    Multi-Agent Generative Adversarial Imitation Learning [PDF] [Code]
    NeurIPS-18. In Proc. 32nd Annual Conference on Neural Information Processing Systems, 2018.
  • Rui Shu, Hung Bui, Shengjia Zhao, Mykel Kochenderfer, Stefano Ermon
    Amortized Inference Regularization [PDF]
    NeurIPS-18. In Proc. 32nd Annual Conference on Neural Information Processing Systems, 2018.
  • Yang Song, Rui Shu, Nate Kushman, Stefano Ermon
    Constructing Unrestricted Adversarial Examples with Generative Models [PDF] [Code]
    NeurIPS-18. In Proc. 32nd Annual Conference on Neural Information Processing Systems, 2018.
  • Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah Goodman, Stefano Ermon
    Bias and Generalization in Deep Generative Models: An Empirical Study [PDF] [Code]
    NeurIPS-18. In Proc. 32nd Annual Conference on Neural Information Processing Systems, 2018.
  • Neal Jean, Michael Xie, Stefano Ermon
    Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance [PDF] [Code]
    NeurIPS-18. In Proc. 32nd Annual Conference on Neural Information Processing Systems, 2018.
  • Aditya Grover, Tudor Achim, Stefano Ermon
    Streamlining Variational Inference for Constraint Satisfaction Problems [PDF] [Code]
    NeurIPS-18. In Proc. 32nd Annual Conference on Neural Information Processing Systems, 2018.
  • Shengjia Zhao, Jiaming Song, Stefano Ermon
    The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models [PDF] [Code]
    UAI-18. In Proc. 34th Conference on Uncertainty in Artificial Intelligence, 2018.
  • Stephan Eissman, Daniel Levy, Rui Shu, Stefan Bartzsch, Stefano Ermon
    Bayesian Optimization and Attribute Adjustment [PDF]
    UAI-18. In Proc. 34th Conference on Uncertainty in Artificial Intelligence, 2018.
  • Barak Oshri, Annie Hu, Peter Adelson, Xiao Chen, Pascaline Dupas, Jeremy Weinstein, Marshall Burke, David Lobell, Stefano Ermon
    Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning [PDF]
    KDD-18. In Proc. 24th ACM SIGKDD Conference, 2018.
  • Yang Song, Jiaming Song, Stefano Ermon
    Accelerating Natural Gradient with Higher-Order Invariance [PDF] [Code]
    ICML-18. In Proc. 35th International Conference on Machine Learning, 2018.
  • Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon
    Accurate Uncertainties for Deep Learning Using Calibrated Regression [PDF]
    ICML-18. In Proc. 35th International Conference on Machine Learning, 2018.
  • Manik Dhar, Aditya Grover, Stefano Ermon
    Modeling Sparse Deviations for Compressed Sensing using Generative Models [PDF] [Code]
    ICML-18. In Proc. 35th International Conference on Machine Learning, 2018.
  • Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon
    Adversarial Constraint Learning for Structured Prediction [PDF] [Code]
    IJCAI-18. In Proc. 27th International Joint Conference on Artificial Intelligence, 2018.
  • Lijie Fan, Wenbing Huang, Chuang Gan, Stefano Ermon, Boqing Gong, Junzhou Huang
    End-to-End Motion Representations Learning for Video Understanding [PDF]
    CVPR-18. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2018.
  • Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman
    PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples [PDF]
    ICLR-18. In Proc. 6th International Conference on Learning Representations, 2018.
  • Rui Shu, Hirokazu Narui, Hung Bui, Stefano Ermon
    A DIRT-T Approach to Unsupervised Domain Adaptation [PDF] [Code]
    ICLR-18. In Proc. 6th International Conference on Learning Representations, 2018.
  • Aditya Grover, Ramki Gummadi, Miguel Lazaro-Gredilla, Dale Schuurmans, Stefano Ermon
    Variational Rejection Sampling [PDF]
    AISTATS-18. In Proc. 21st International Conference on Artificial Intelligence and Statistics, 2018.
  • Aditya Grover, Todor Markov, Norman Jin, Peter Attia, Nick Perkins, Bryan Cheong, Michael Chen, Zi Yang, Stephen Harris, William Chueh, Stefano Ermon
    Best arm identification in multi-armed bandits with delayed and partial feedback [PDF]
    AISTATS-18. In Proc. 21st International Conference on Artificial Intelligence and Statistics, 2018.
  • Aditya Grover, Manik Dhar, Stefano Ermon
    Flow-GAN: Combining maximum likelihood and adversarial learning in generative models [PDF] [Code]
    AAAI-18. In Proc. 32nd AAAI Conference on Artificial Intelligence, February 2018.
  • Aditya Grover, Stefano Ermon
    Boosted Generative Models [PDF] [Code]
    AAAI-18. In Proc. 32nd AAAI Conference on Artificial Intelligence, February 2018.
  • Jonathan Kuck, Stefano Ermon
    Approximate Inference via Weighted Rademacher Complexity [PDF]
    AAAI-18. In Proc. 32nd AAAI Conference on Artificial Intelligence, February 2018.
  • Daniel Levy, Stefano Ermon
    Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces [PDF]
    AAAI-18. In Proc. 32nd AAAI Conference on Artificial Intelligence, February 2018.
  • Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon
    Learning with weak supervision from physics and data-driven constraints [PDF]
    AI Magazine. In AI Magazine, Spring 2018, Vol 39, No 1, pp. 27-38.

2017

  • William Gent, Kipil Lim, Yufeng Liang, Qinghao Li, Taylor Barnes, Sung-Jin Ahn, Kevin Stone, Mitchell McIntire, Jihyun Hong, Jay Hyok Song, Yiyang Li, Apurva Mehta, Stefano Ermon, Tolek Tyliszczak, Arthur Kilcoyne, David Vine, Jin-Hwan Park, Seok-Gwang Doo, Michael Toney, Wanli Yang, David Prendergast, and William Chueh
    Coupling Between Oxygen Redox and Cation Migration Explains Unusual Electrochemistry in Lithium-Rich Layered Oxides [PDF]
    Nature Communications. In Nature Communications, DOI: 10.1038/s41467-017-02041-x, December 2017.
  • Volodymyr Kuleshov, Stefano Ermon
    Neural Variational Inference and Learning in Undirected Graphical Models [PDF]
    NIPS-17. In Proc. 31st Annual Conference on Neural Information Processing Systems, December 2017.
  • Jiaming Song, Shengjia Zhao, Stefano Ermon
    A-NICE-MC: Adversarial Training for MCMC [PDF] [Code]
    NIPS-17. In Proc. 31st Annual Conference on Neural Information Processing Systems, December 2017.
  • Yunzhu Li, Jiaming Song, Stefano Ermon
    InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations [PDF] [Code]
    NIPS-17. In Proc. 31st Annual Conference on Neural Information Processing Systems, December 2017.
  • Stephen Mussmann, Daniel Levy, Stefano Ermon
    Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search [PDF]
    UAI-17. In Proc. 33rd Conference on Uncertainty in Artificial Intelligence, August 2017.
  • Volodymyr Kuleshov, Stefano Ermon
    Deep Hybrid Models: Bridging Discriminative and Generative Approaches [PDF]
    UAI-17. In Proc. 33rd Conference on Uncertainty in Artificial Intelligence, August 2017.
  • Shengjia Zhao, Jiaming Song, Stefano Ermon
    Learning Hierarchical Features from Generative Models [PDF] [Code]
    ICML-17. In Proc. 34th International Conference on Machine Learning, August 2017.
  • Russell Stewart, Stefano Ermon
    Supervising Neural Networks with Physics and other Domain Knowledge [PDF] [Code]
    AAAI-17. In Proc. 31st AAAI Conference on Artificial Intelligence, February 2017.
    AAAI Outstanding Paper Award
  • Volodymyr Kuleshov, Stefano Ermon
    Online Uncertainty Estimation Against an Adversary [PDF] [Code]
    AAAI-17. In Proc. 31st AAAI Conference on Artificial Intelligence, February 2017.
  • Jiaxuan You, Xiaocheng Li, Melvin Low, David Lobell, Stefano Ermon
    Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data [PDF] [Code]
    AAAI-17. In Proc. 31st AAAI Conference on Artificial Intelligence, February 2017.
    Best Student Paper Award (CompSust Track)
  • Colin Wei, Stefano Ermon
    General Bounds on Satisfiability Thresholds for Random CSPs via Fourier Analysis [PDF]
    AAAI-17. In Proc. 31st AAAI Conference on Artificial Intelligence, February 2017.
  • Siamak Yousefi, Hirokazu Narui, Sankalp Dayal, Stefano Ermon, Shahrokh Valaee
    A Survey on Behavior Recognition Using WiFi Channel State Information [PDF] [Code]
    In IEEE Communications Magazine, 55 (10), 98-104, 2017.
  • Biagio Cosenza, Juan Durillo, Stefano Ermon, Ben Juurlink
    Autotuning Stencil Computations with Structural Ordinal Regression Learning [PDF]
    IPDPS-17. In IEEE International Parallel and Distributed Processing Symposium, February 2017.

2016

  • Xiaoyue Duan, Feifei Yang, Erin Antono, Wenge Yang, Piero Pianetta, Stefano Ermon, Apurva Mehta, Yijin Liu
    Unsupervised Data Mining in Nanoscale X-ray Spectro-Microscopic Study of NdFeB Magnet [PDF]
    Scientific Reports. In Scientific Reports, 6, 34406 (2016).
  • Neal Jean, Marshall Burke, Michael Xie, Matthew Davis, David Lobell, Stefano Ermon
    Combining Satellite Imagery and Machine Learning to Predict Poverty [PDF] [Project Website] [Commentary] [Nature Research Highlights] [Code]
    Science. In Science, 353(6301), 790-794, 2016.
  • Aditya Grover, Stefano Ermon
    Variational Bayes on Monte Carlo Steroids [PDF]
    NIPS-16. In Proc. 30th Annual Conference on Neural Information Processing Systems, December 2016.
  • Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon
    Adaptive Concentration Inequalities for Sequential Decision Problems [PDF] [Code]
    NIPS-16. In Proc. 30th Annual Conference on Neural Information Processing Systems, December 2016.
  • Jonathan Ho, Stefano Ermon
    Generative Adversarial Imitation Learning [PDF] [Code]
    NIPS-16. In Proc. 30th Annual Conference on Neural Information Processing Systems, December 2016.
  • Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla Gomes, Bart Selman
    Solving Marginal MAP Problems with NP Oracles and Parity Constraints [PDF]
    NIPS-16. In Proc. 30th Annual Conference on Neural Information Processing Systems, December 2016.
  • Mitchell McIntire, Daniel Ratner, Stefano Ermon
    Sparse Gaussian Processes for Bayesian Optimization [PDF] [Code]
    UAI-16. In Proc. 32nd Conference on Uncertainty in Artificial Intelligence, June 2016.
  • Jonathan Ho, Jayesh Gupta, Stefano Ermon
    Model-Free Imitation Learning with Policy Optimization [PDF]
    ICML-16. In Proc. 33rd International Conference on Machine Learning, June 2016.
  • Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla Gomes, Bart Selman
    Variable Elimination in the Fourier Domain [PDF]
    ICML-16. In Proc. 33rd International Conference on Machine Learning, June 2016.
  • Steve Mussmann, Stefano Ermon
    Learning and Inference via Maximum Inner Product Search [PDF]
    ICML-16. In Proc. 33rd International Conference on Machine Learning, June 2016.
  • Tudor Achim, Ashish Sabharwal, Stefano Ermon
    Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference [PDF]
    ICML-16. In Proc. 33rd International Conference on Machine Learning, June 2016.
  • Lun-Kai Hsu, Tudor Achim, Stefano Ermon
    Tight Variational Bounds via Random Projections and I-Projections [PDF]
    AISTATS-16. In Proc. 19th International Conference on Artificial Intelligence and Statistics, May 2016.
  • Michael Xie, Neal Jean, Marshall Burke, David Lobell, Stefano Ermon
    Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping [PDF] [Stanford Report] [NYTimes]
    AAAI-16. In Proc. 30th AAAI Conference on Artificial Intelligence, February 2016.
  • Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, Stefano Ermon
    Closing the Gap Between Short and Long XORs for Model Counting [PDF] [Code]
    AAAI-16. In Proc. 30th AAAI Conference on Artificial Intelligence, February 2016.
  • Carolyn Kim, Ashish Sabharwal, Stefano Ermon
    Exact Sampling with Integer Linear Programs and Random Perturbations [PDF] [Code]
    AAAI-16. In Proc. 30th AAAI Conference on Artificial Intelligence, February 2016.

2015

  • Stefan Hadjis, Stefano Ermon
    Importance sampling over sets: a new probabilistic inference scheme. [PDF] [Code]
    UAI-15. In Proc. 31st Conference on Uncertainty in Artificial Intelligence, July 2015.
  • Michael Zhu, Stefano Ermon
    A Hybrid Approach for Probabilistic Inference using Random Projections. [PDF]
    ICML-15. In Proc. 32nd International Conference on Machine Learning, July 2015.
  • Yexiang Xue, Stefano Ermon, Carla Gomes, Bart Selman
    Uncovering Hidden Structure through Parallel Problem Decomposition for the Set Basis Problem with Application to Materials Discovery. [PDF]
    IJCAI-15. In Proc. International Joint Conference on Artificial Intelligence, July 2015.
  • Stefano Ermon, Yexiang Xue, Russell Toth, Bistra Dilkina, Richard Bernstein, Theodoros Damoulas, Patrick Clark, Steve DeGloria, Andrew Mude, Christopher Barrett, and Carla Gomes
    Learning Large Scale Dynamic Discrete Choice Models of Spatio-Temporal Preferences with Application to Migratory Pastoralism in East Africa. [PDF]
    AAAI-15. In Proc. 29th AAAI Conference on Artificial Intelligence, January 2015.
  • Stefano Ermon, Ronan Le Bras, Santosh Suram, John M. Gregoire, Carla Gomes, Bart Selman, and Robert B. van Dover
    Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery. [PDF]
    AAAI-15. In Proc. 29th AAAI Conference on Artificial Intelligence, January 2015.

2014

  • Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
    Designing Fast Absorbing Markov Chains [PDF]
    AAAI-14. In Proc. 28th AAAI Conference on Artificial Intelligence, July 2014.
  • Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
    Low-density Parity Constraints for Hashing-Based Discrete Integration [PDF] [Code]
    ICML-14. In Proc. 31st International Conference on Machine Learning, June 2014.

2013

  • Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
    Embed and Project: Discrete Sampling with Universal Hashing [PDF] [Code]
    NIPS-13. In Proc. 27th Annual Conference on Neural Information Processing Systems, December 2013.
  • Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
    Optimization With Parity Constraints: From Binary Codes to Discrete Integration [PDF] [Slides] [Poster] [Code]
    UAI-13. In Proc. 29th Conference on Uncertainty in Artificial Intelligence, July 2013.
    Best Student Paper Award. Best Paper Award Runner-up.
  • Stefano Ermon, Yexiang Xue, Carla Gomes, and Bart Selman.
    Learning Policies For Battery Usage Optimization in Electric Vehicles.
    Machine Learning. In Machine Learning: Volume 92, Issue 1, Page 177-194, 2013.
  • Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
    Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization [PDF] [Slides] [Code]
    ICML-13. In Proc. 30th International Conference on Machine Learning, June 2013.

2012

  • Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
    Density Propagation and Improved Bounds on the Partition Function. [PDF] [Poster]
    NIPS-12. In Proc. 26th Annual Conference on Neural Information Processing Systems, December 2012.
  • Stefano Ermon, Carla Gomes, and Bart Selman
    Uniform Solution Sampling Using a Constraint Solver As an Oracle [PDF] [Slides] [Code]
    UAI-12. In Proc. 28th Conference on Uncertainty in Artificial Intelligence, August 2012.
  • Liaoruo Wang, Stefano Ermon, and John Hopcroft
    Feature-Enhanced Probabilistic Models for Diffusion Network Inference. [PDF] [Slides] [Code]
    ECML-PKDD-12. In Proc. of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 2012.
  • Stefano Ermon, Yexiang Xue, Carla Gomes, and Bart Selman
    Learning Policies For Battery Usage Optimization in Electric Vehicles [PDF] [Slides] [Dataset]
    ECML-PKDD-12. In Proc. of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 2012.
  • Stefano Ermon, Ronan Le Bras, Carla Gomes, Bart Selman, and Bruce van Dover
    SMT-Aided Combinatorial Materials Discovery [PDF] [Code]
    SAT-12. In Proc. 15th International Conference on Theory and Applications of Satisfiability Testing, June 2012.
  • Stefano Ermon, Carla Gomes, Bart Selman, and Alexander Vladimirsky
    Probabilistic Planning With Non-linear Utility Functions and Worst Case Guarantees [PDF]
    AAMAS-12. In Proc. 11th International Conference on Autonomous Agents and Multiagent Systems, June 2012.

2011

  • Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
    Accelerated Adaptive Markov Chain for Partition Function Computation [PDF] [Code] [Data]
    NIPS-11. In Proc. 25th Annual Conference on Neural Information Processing Systems, December 2011.
  • Stefano Ermon, Carla Gomes, and Bart Selman
    A Flat Histogram Method for Computing the Density of States of Combinatorial Problems [PDF]
    IJCAI-11. In Proc. 22nd International Joint Conference on Artificial Intelligence, July 2011. .
  • Stefano Ermon, Jon Conrad, Carla Gomes, and Bart Selman
    Risk-Sensitive Policies for Sustainable Renewable Resource Allocation [PDF]
    IJCAI-11. In Proc. 22nd International Joint Conference on Artificial Intelligence, July 2011.
  • Stefano Ermon, Carla Gomes, and Bart Selman
    A Message Passing Approach to Multiagent Gaussian Inference for Dynamic Processes (Short Paper) [PDF]
    AAMAS-11. In Proc. 10th International Conference on Autonomous Agents and Multiagent Systems, May 2011.

2010

  • Stefano Ermon, Carla Gomes, and Bart Selman
    Computing the Density of States of Boolean Formulas [PDF] [Slides] [Code] [Data]
    CP-10. In Proc. 16th International Conference on Principles and Practice of Constraint Programming, September 2010.
    Best Student Paper Award
  • Stefano Ermon, Jon Conrad, Carla Gomes, and Bart Selman
    Playing Games against Nature: Optimal Policies for Renewable Resource Allocation [PDF]
    UAI-10. In Proc. 26th Conference on Uncertainty in Artificial Intelligence, July 2010.
  • Stefano Ermon, Carla Gomes, and Bart Selman
    Collaborative Multiagent Gaussian Inference in a Dynamic Environment Using Belief Propagation (Short Paper) [PDF]
    AAMAS-10. In Proc. 9th International Conference on Autonomous Agents and Multiagent Systems, May 2010.

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