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Stefano Ermon     Follow Me 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

• Winter 2022/2023: Probabilistic Graphical Models (CS 228)
• Winter 2021/2022: Probabilistic Graphical Models (CS 228)
• Fall 2021/2022: Deep Generative Models (CS 236)
• Fall 2021/2022: Data for Sustainable Development (CS 325B)
• Winter 2020/2021: Probabilistic Graphical Models (CS 228)
• Winter 2019/2020: Probabilistic Graphical Models (CS 228)
• Fall 2019/2020: Deep Generative Models (CS 236)

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Honors and Awards

• ICLR 2022 Outstanding Paper Award [Press]
• ICLR 2021 Outstanding Paper Award [Press]
• ISSNAF Young Investigator Award [Press]
• Sloan Research Fellowship [Press]
• Microsoft Research Faculty Fellowship [Press]
• Bloomberg Data Science Research Grant [Press]
• AFOSR Young Investigator Award [Press]
• IJCAI Computers and Thought Award [Press]
• ONR Young Investigator Award [Press]
• Hellman Fellowship [Press]
• AWS Machine Learning Research Award
• First Place, World Bank Big Data Innovation Challenge [Press]
• NSF CAREER Award
• Sony Faculty Innovation Award [Press]
• AAAI 2017 Outstanding Paper Award [Press]
• AAAI 2017 Best Student Paper Award (Computational Sustainability Track) [Press]
• 10 World Changing Ideas of 2016, Scientific American [Press]
• Finalist, NVIDIA Global Impact Award [Press]
• UAI 2013 Facebook Best Student Paper Award and Best Paper Award Runner-up
• CP 2010 Best Student Paper Award

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Publications

2023

• Benedikt Boecking, Nicholas Roberts, Willie Neiswanger, Stefano Ermon, Frederic Sala, Artur Dubrawski
  Generative Modeling Helps Weak Supervision (and Vice Versa)
  ICLR-23. To appear in Proc. 11th International Conference on Learning Representations, 2023.
• Xuan Su, Jiaming Song, Chenlin Meng, Stefano Ermon
  Dual Diffusion Implicit Bridges for Image-to-Image Translation
  ICLR-23. To appear in Proc. 11th International Conference on Learning Representations, 2023.
• Divyansh Garg, Joey Hejna, Matthieu Geist, Stefano Ermon
  Extreme Q-Learning: MaxEnt RL without Entropy
  ICLR-23. To appear in Proc. 11th International Conference on Learning Representations, 2023.
• Kuno Kim, Stefano Ermon
  Understanding and Adopting Rational Behavior by Bellman Score Estimation
  ICLR-23. To appear in Proc. 11th International Conference on Learning Representations, 2023.
• Michael Poli, Stefano Massaroli, Stefano Ermon, Bryan Wilder, Eric Horvitz
  Ideal Abstractions for Decision-Focused Learning
  AISTATS-23. To appear in Proc. 26th International Conference on Artificial Intelligence and Statistics, 2023.
• Charles Marx, Youngsuk Park, Hilaf Hasson, Yuyang Wang, Stefano Ermon, Luke Huan
  But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI
  AISTATS-23. To appear in Proc. 26th International Conference on Artificial Intelligence and Statistics, 2023.
• Lantao Yu, Tianhe Yu, Jiaming Song, Willie Neiswanger, Stefano Ermon
  Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching
  AAAI-23. To appear in Proc. 37th AAAI Conference on Artificial Intelligence, 2023.

2022

• Chenlin Meng, Kristy Choi, Jiaming Song, Stefano Ermon
  Concrete Score Matching: Generalized Score Matching for Discrete Data [PDF]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon
  Generalizing Bayesian Optimization with Decision-theoretic Entropies [PDF]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Andy Shih, Dorsa Sadigh, Stefano Ermon
  Training and Inference on Any-Order Autoregressive Models the Right Way (Oral Presentation) [PDF]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Michael Poli, Winnie Xu, Stefano Massaroli, Chenlin Meng, Kuno Kim, Stefano Ermon
  Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations [PDF]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Michael Poli, Stefano Massaroli, Federico Berto, Jinkyoo Park, Tri Dao, Christopher Ré, Stefano Ermon
  Transform Once: Efficient Operator Learning in Frequency Domain [PDF]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David Lobell, Stefano Ermon
  SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery [PDF]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Divyansh Garg, Skanda Vaidyanath, Kuno Kim, Jiaming Song, Stefano Ermon
  LISA: Learning Interpretable Skill Abstractions from Language [PDF]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Yann Dubois, Stefano Ermon, Tatsunori Hashimoto, Percy Liang
  Improving Self-Supervised Learning by Characterizing Idealized Representations [PDF]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark Boyer, Stefano Ermon, Jeff Schneider, Willie Neiswanger
  Exploration via Planning for Information about the Optimal Trajectory [PDF]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Muyang Li, Ji Lin, Chenlin Meng, Stefano Ermon, Song Han, Jun-Yan Zhu
  Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models [PDF]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Bahjat Kawar, Michael Elad, Stefano Ermon, Jiaming Song
  Denoising Diffusion Restoration Models [PDF]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Tri Dao, Daniel Fu, Stefano Ermon, Atri Rudra, Christopher Ré
  FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness [PDF]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon
  Modular Conformal Calibration [PDF]
  ICML-22. In Proc. 39th International Conference on Machine Learning, 2022.
• Rui Shu, Stefano Ermon
  Bit Prioritization in Variational Autoencoders via Progressive Coding [PDF]
  ICML-22. In Proc. 39th International Conference on Machine Learning, 2022.
• Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon
  A General Recipe for Likelihood-free Bayesian Optimization [PDF]
  ICML-22. In Proc. 39th International Conference on Machine Learning, 2022.
• Chenlin Meng, Linqi Zhou, Kristy Choi, Tri Dao, Stefano Ermon
  ButterflyFlow: Building Invertible Layers with Butterfly Matrices [PDF]
  ICML-22. In Proc. 39th International Conference on Machine Learning, 2022.
• Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, Ramtin Pedarsani
  Imitation Learning by Estimating Expertise of Demonstrators [PDF]
  ICML-22. In Proc. 39th International Conference on Machine Learning, 2022.
• Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon
  SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations [PDF]
  ICLR-22. In Proc. 10th International Conference on Learning Representations, 2022.
• Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon
  Comparing Distributions by Measuring Differences that Affect Decision Making [PDF]
  ICLR-22. In Proc. 10th International Conference on Learning Representations, 2022.
  ICLR Outstanding Paper Award
• Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger
  An Experimental Design Perspective on Exploration in Reinforcement Learning [PDF]
  ICLR-22. In Proc. 10th International Conference on Learning Representations, 2022.
• Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang
  GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation [PDF]
  ICLR-22. In Proc. 10th International Conference on Learning Representations, 2022.
• Yang Song, Liyue Shen, Lei Xing, Stefano Ermon
  Solving Inverse Problems in Medical Imaging with Score-Based Generative Models [PDF]
  ICLR-22. In Proc. 10th International Conference on Learning Representations, 2022.
• Kristy Choi, Chenlin Meng, Yang Song, Stefano Ermon
  Density Ratio Estimation via Infinitesimal Classification [PDF]
  AISTATS-22. In Proc. 25th International Conference on Artificial Intelligence and Statistics, 2022.
• Chenlin Meng, Enci Liu, Willie Neiswanger, Jiaming Song, Marshall Burke, David Lobell, Stefano Ermon
  IS-Count: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling [PDF]
  AAAI-22. In Proc. 36th AAAI Conference on Artificial Intelligence, 2022.

2021

• Christopher Yeh, Chenlin Meng, Sherrie Wang, Anne Driscoll, Erik Rozi, Patrick Liu, Jihyeon Lee, Marshall Burke, David Lobell, Stefano Ermon
  SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning [Website]
  NeurIPS-21 (Datasets & Benchmarks Track). In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Lantao Yu, Jiaming Song, Yang Song, Stefano Ermon
  Pseudo-Spherical Contrastive Divergence [PDF]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Chris Cundy, Aditya Grover, Stefano Ermon
  Scalable Variational Approaches for Bayesian Causal Discovery
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Roshni Sahoo, Shengjia Zhao, Alyssa Chen, Stefano Ermon
  Reliable Decisions with Threshold Calibration [PDF]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Chenlin Meng, Yang Song, Wenzhe Li, Stefano Ermon
  Estimating High Order Gradients of the Data Distribution by Denoising [PDF]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Mike Wu, Noah Goodman, Stefano Ermon
  Improving Compositionality of Neural Networks by Decoding Representations to Inputs [PDF]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Divyansh Garg, Shuvam Chakraborty, Chris Cundy, Jiaming Song, Stefano Ermon
  IQ-Learn: Inverse soft-Q Learning for Imitation (Spotlight Presentation) [PDF]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Yusuke Tashiro, Jiaming Song, Yang Song, Stefano Ermon
  CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation [PDF]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Yutong He, Dingjie Wang, Nicholas Lai, William Zhang, Chenlin Meng, Marshall Burke, David Lobell, Stefano Ermon
  Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis [PDF]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Shengjia Zhao, Michael P. Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon
  Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration [PDF]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Abhishek Sinha, Jiaming Song, Chenlin Meng, Stefano Ermon
  D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation [PDF]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Yang Song, Conor Durkan, Iain Murray, Stefano Ermon
  Maximum Likelihood Training of Score-Based Diffusion Models (Spotlight Presentation) [PDF]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Andy Shih, Dorsa Sadigh, Stefano Ermon
  HyperSPNs: Compact and Expressive Probabilistic Circuits [PDF]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Robin Swezey, Aditya Grover, Bruno Charron, Stefano Ermon
  PiRank: Scalable Learning To Rank via Differentiable Sorting [PDF]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Kuno Kim, Akshat Jindal, Yang Song, Jiaming Song, Yanan Sui, Stefano Ermon
  Imitation with Neural Density Models [PDF]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David Lobell, Stefano Ermon
  Geography-Aware Self-Supervised Learning [PDF]
  ICCV-21. In Proc. 18th International Conference on Computer Vision, 2021.
• Kristy Choi, Madeline Liao, Stefano Ermon
  Featurized Density Ratio Estimation [PDF]
  UAI-21. In Proc. 37th Conference on Uncertainty in Artificial Intelligence, 2021.
• Willie Neiswanger, Ke Alexander Wang, Stefano Ermon
  Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information [PDF]
  ICML-21. In Proc. 38th International Conference on Machine Learning, 2021.
• Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon
  Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving [PDF]
  ICML-21. In Proc. 38th International Conference on Machine Learning, 2021.
• Kuno Kim, Shivam Garg, Kirankumar Shiragur, Stefano Ermon
  Reward Identification in Inverse Reinforcement Learning [PDF]
  ICML-21. In Proc. 38th International Conference on Machine Learning, 2021.
• Tung Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon
  Temporal Predictive Coding For Model-Based Planning In Latent Space [PDF]
  ICML-21. In Proc. 38th International Conference on Machine Learning, 2021.
• Jihyeon Lee, Nina Brooks, Fahim Tajwar, Marshall Burke, Stefano Ermon, David Lobell, Debashish Biswas, Stephen Luby
  Scalable Deep Learning to Identify Brick Kilns and Aid Regulatory Capacity [PDF]
  PNAS. In Proceedings of the National Academy of Sciences, 27 Apr 2021, 118 (17). DOI: 10.1073/pnas.2018863118.
• Marshall Burke, Anne Driscoll, David Lobell, Stefano Ermon
  Using Satellite Imagery to Understand and Promote Sustainable Development [PDF]
  Science. In Science, 19 Mar 2021, Vol. 371, No. 6535. DOI: 10.1126/science.abe8628.
• Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
  Score-Based Generative Modeling through Stochastic Differential Equations [PDF]
  ICLR-21. In Proc. 9th International Conference on Learning Representations, 2021.
  ICLR Outstanding Paper Award
• Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon
  Improved Autoregressive Modeling with Distribution Smoothing (Oral Presentation) [PDF]
  ICLR-21. In Proc. 9th International Conference on Learning Representations, 2021.
• Jiaming Song, Chenlin Meng, Stefano Ermon
  Denoising Diffusion Implicit Models [PDF]
  ICLR-21. In Proc. 9th International Conference on Learning Representations, 2021.
• Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon
  Anytime Sampling for Autoregressive Models via Ordered Autoencoding [PDF]
  ICLR-21. In Proc. 9th International Conference on Learning Representations, 2021.
• Abhishek Sinha, Kumar Ayush, Jiaming Song, Burak Uzkent, Hongxia Jin, Stefano Ermon
  Negative Data Augmentation [PDF]
  ICLR-21. In Proc. 9th International Conference on Learning Representations, 2021.
• Andy Shih, Arjun Sawhney, Jovana Kondic, Stefano Ermon, Dorsa Sadigh
  On the Critical Role of Conventions in Adaptive Human-AI Collaboration [PDF]
  ICLR-21. In Proc. 9th International Conference on Learning Representations, 2021.
• Shengjia Zhao, Stefano Ermon
  Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration [PDF]
  AISTATS-21. In Proc. 24th International Conference on Artificial Intelligence and Statistics, 2021.
• Jihyeon Lee, Dylan Grosz, Sicheng Zeng, Burak Uzkent, Marshall Burke, David Lobell, Stefano Ermon
  Predicting Livelihood Indicators from Crowdsourced Street Level Images [PDF]
  AAAI-21. In Proc. 35th AAAI Conference on Artificial Intelligence, 2021.
• Kumar Ayush, Burak Uzkent, Marshall Burke, David Lobell, Stefano Ermon
  Efficient Poverty Mapping from High Resolution Remote Sensing Images [PDF]
  AAAI-21. In Proc. 35th AAAI Conference on Artificial Intelligence, 2021.

2020

• Jiaming Song, Stefano Ermon
  Multi-label Contrastive Predictive Coding (Oral Presentation) [PDF]
  NeurIPS-20. In Proc. 34th Annual Conference on Neural Information Processing Systems, 2020.
• Jonathan Kuck, Shuvam Chakraborty, Hao Tang, Rachel Luo, Jiaming Song, Ashish Sabharwal, Stefano Ermon
  Belief Propagation Neural Networks [PDF]
  NeurIPS-20. In Proc. 34th Annual Conference on Neural Information Processing Systems, 2020.
• Yang Song, Stefano Ermon
  Improved Techniques for Training Score-Based Generative Models [PDF]
  NeurIPS-20. In Proc. 34th Annual Conference on Neural Information Processing Systems, 2020.
• Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon
  Autoregressive Score Matching [PDF]
  NeurIPS-20. In Proc. 34th Annual Conference on Neural Information Processing Systems, 2020.
• Andy Shih, Stefano Ermon
  Probabilistic Circuits for Variational Inference in Discrete Graphical Models [PDF]
  NeurIPS-20. In Proc. 34th Annual Conference on Neural Information Processing Systems, 2020.
• Yusuke Tashiro, Yang Song, Stefano Ermon
  Diversity can be Transferred: Output Diversification for White- and Black-box Attacks [PDF]
  NeurIPS-20. In Proc. 34th Annual Conference on Neural Information Processing Systems, 2020.
• Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, Christopher Ré
  HiPPO: Recurrent Memory with Optimal Polynomial Projections (Spotlight Presentation) [PDF]
  NeurIPS-20. In Proc. 34th Annual Conference on Neural Information Processing Systems, 2020.
• Tianyu Pang, Kun Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu
  Efficient Learning of Generative Models via Finite-Difference Score Matching [PDF]
  NeurIPS-20. In Proc. 34th Annual Conference on Neural Information Processing Systems, 2020.
• Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Zou, Sergey Levine, Chelsea Finn, Tengyu Ma
  MOPO: Model-based Offline Policy Optimization [PDF]
  NeurIPS-20. In Proc. 34th Annual Conference on Neural Information Processing Systems, 2020.
• Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon
  Training Deep Energy-Based Models with f-Divergence Minimization [PDF]
  ICML-20. In Proc. 37th International Conference on Machine Learning, 2020.
• Shengjia Zhao, Tengyu Ma, Stefano Ermon
  Individual Calibration with Randomized Forecasting [PDF]
  ICML-20. In Proc. 37th International Conference on Machine Learning, 2020.
• Jiaming Song, Stefano Ermon
  Bridging the Gap Between f-GANs and Wasserstein GANs [PDF]
  ICML-20. In Proc. 37th International Conference on Machine Learning, 2020.
• Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon
  Domain Adaptive Imitation Learning [PDF]
  ICML-20. In Proc. 37th International Conference on Machine Learning, 2020.
• Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, Stefano Ermon
  Fair Generative Modeling via Weak Supervision [PDF]
  ICML-20. In Proc. 37th International Conference on Machine Learning, 2020.
• Rui Shu, Tung Nguyen, Yinlam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung Bui
  Predictive Coding for Locally-Linear Control [PDF]
  ICML-20. In Proc. 37th International Conference on Machine Learning, 2020.
• Christopher Yeh, Anthony Perez, Anne Driscoll, George Azzari, Zhongyi Tang, David Lobell, Stefano Ermon, Marshall Burke
  Using Publicly Available Satellite Imagery and Deep Learning to Understand Economic Well-Being in Africa [PDF]
  Nature Communications. In Nature Communications, 11, 2583, 2020.
• Chris Cundy, Stefano Ermon
  Flexible Approximate Inference via Stratified Normalizing Flows [PDF]
  UAI-20. In Proc. 36th Conference on Uncertainty in Artificial Intelligence, 2020.
• Kumar Ayush, Burak Uzkent, Marshall Burke, David Lobell, Stefano Ermon
  Generating Interpretable Poverty Maps using Object Detection in Satellite Images [PDF]
  IJCAI-20. In Proc. 29th International Joint Conference on Artificial Intelligence, 2020.
• Peter M. Attia, Aditya Grover, Norman Jin, Kristen A. Severson, Todor M. Markov, Yang-Hung Liao, Michael H. Chen, Bryan Cheong, Nicholas Perkins, Zi Yang, Patrick K. Herring, Muratahan Aykol, Stephen J. Harris, Richard D. Braatz, Stefano Ermon, William C. Chueh
  Closed-loop Optimization of Fast-Charging Protocols for Batteries with Machine Learning [PDF] [News]
  Nature. In Nature, 578, 397-402, 2020.
• Joseph Duris, Dylan Kennedy, Adi Hanuka, Jane Shtalenkova, Auralee Edelen, Panagiotis Baxevanis, Adam Egger, Tyler Cope, Mitchell McIntire, Stefano Ermon, Daniel Ratner
  Bayesian Optimization of a Free-Electron Laser [PDF]
  Physical Review Letters. In Physical Review Letters, 124, 124801, 2020.
• Burak Uzkent, Stefano Ermon
  Learning When and Where to Zoom with Deep Reinforcement Learning [PDF]
  CVPR-20. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2020.
• Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon
  Gaussianization Flows [PDF]
  AISTATS-20. In Proc. 23rd International Conference on Artificial Intelligence and Statistics, 2020.
• Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon
  Permutation Invariant Graph Generation via Score-Based Generative Modeling [PDF] [Code]
  AISTATS-20. In Proc. 23rd International Conference on Artificial Intelligence and Statistics, 2020.
• Shengjia Zhao, Christopher Yeh, Stefano Ermon
  A Framework for Sample Efficient Interval Estimation with Control Variates [PDF]
  AISTATS-20. In Proc. 23rd International Conference on Artificial Intelligence and Statistics, 2020.
• Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon
  A Theory of Usable Information under Computational Constraints [PDF]
  ICLR-20. In Proc. 8th International Conference on Learning Representations, 2020.
• Jiaming Song, Stefano Ermon
  Understanding the Limitations of Variational Mutual Information Estimators [PDF]
  ICLR-20. In Proc. 8th International Conference on Learning Representations, 2020.
• Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, Ben Poole
  Weakly Supervised Disentanglement with Guarantees [PDF]
  ICLR-20. In Proc. 8th International Conference on Learning Representations, 2020.
• Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon
  AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows [PDF]
  AAAI-20. In Proc. 34th AAAI Conference on Artificial Intelligence, 2020.
• Mike Wu, Kristy Choi, Noah Goodman, Stefano Ermon
  Meta-Amortized Variational Inference and Learning [PDF]
  AAAI-20. In Proc. 34th AAAI Conference on Artificial Intelligence, 2020.

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.

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