Ermon, Stefano

295 publications

AISTATS 2025 $f$-PO: Generalizing Preference Optimization with $f$-Divergence Minimization Jiaqi Han, Mingjian Jiang, Yuxuan Song, Stefano Ermon, Minkai Xu

FnTML 2025 Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Alex Strasser, Haiyang Yu, Yuqing Xie, Xiang Fu, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji

ICLRW 2025 Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space Yangming Li, Chieh-Hsin Lai, Carola-Bibiane Schönlieb, Yuki Mitsufuji, Stefano Ermon

ICCV 2025 CHORDS: Diffusion Sampling Accelerator with Multi-Core Hierarchical ODE Solvers Jiaqi Han, Haotian Ye, Puheng Li, Minkai Xu, James Zou, Stefano Ermon

ICLR 2025 CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion Joshua Kazdan, Hao Sun, Jiaqi Han, Felix Petersen, Frederick Vu, Stefano Ermon

TMLR 2025 Calibrated Probabilistic Forecasts for Arbitrary Sequences Charles Marx, Volodymyr Kuleshov, Stefano Ermon

ICLR 2025 Data Unlearning in Diffusion Models Silas Alberti, Kenan Hasanaliyev, Manav Shah, Stefano Ermon

ICLR 2025 Energy-Based Diffusion Language Models for Text Generation Minkai Xu, Tomas Geffner, Karsten Kreis, Weili Nie, Yilun Xu, Jure Leskovec, Stefano Ermon, Arash Vahdat

ICML 2025 ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers Under Domain Shifts Samar Khanna, Medhanie Irgau, David B. Lobell, Stefano Ermon

NeurIPS 2025 Exploring Diffusion Transformer Designs via Grafting Keshigeyan Chandrasegaran, Michael Poli, Daniel Y Fu, Dongjun Kim, Lea M. Hadzic, Manling Li, Agrim Gupta, Stefano Massaroli, Azalia Mirhoseini, Juan Carlos Niebles, Stefano Ermon, Li Fei-Fei

TMLR 2025 G2D2: Gradient-Guided Discrete Diffusion for Inverse Problem Solving Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Bac Nguyen, Stefano Ermon, Yuki Mitsufuji

NeurIPS 2025 GeoAda: Efficiently Finetune Geometric Diffusion Models with Equivariant Adapters Wanjia Zhao, Jiaqi Han, Siyi Gu, Mingjian Jiang, James Zou, Stefano Ermon

ICLRW 2025 Improving Vector-Quantized Image Modeling with Latent Consistency-Matching Diffusion Bac Nguyen, Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Stefano Ermon, Yuki Mitsufuji

ICML 2025 Inductive Moment Matching Linqi Zhou, Stefano Ermon, Jiaming Song

ICLRW 2025 Neural Nonmyopic Bayesian Optimization in Dynamic Cost Settings Sang T. Truong, Duc Quang Nguyen, Willie Neiswanger, Ryan-Rhys Griffiths, Stefano Ermon, Nick Haber, Sanmi Koyejo

TMLR 2025 Non-Myopic Multi-Objective Bayesian Optimization Syrine Belakaria, Alaleh Ahmadian, Barbara E Engelhardt, Stefano Ermon, Jana Doppa

CVPR 2025 Personalized Preference Fine-Tuning of Diffusion Models Meihua Dang, Anikait Singh, Linqi Zhou, Stefano Ermon, Jiaming Song

NeurIPS 2025 Preference-Guided Diffusion for Multi-Objective Offline Optimization Yashas Annadani, Syrine Belakaria, Stefano Ermon, Stefan Bauer, Barbara E Engelhardt

ICML 2025 Scaling Probabilistic Circuits via Monarch Matrices Honghua Zhang, Meihua Dang, Benjie Wang, Stefano Ermon, Nanyun Peng, Guy Van Den Broeck

ICML 2025 Smooth Interpolation for Improved Discrete Graph Generative Models Yuxuan Song, Juntong Shi, Jingjing Gong, Minkai Xu, Stefano Ermon, Hao Zhou, Wei-Ying Ma

ICLR 2025 TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data Jeremy Andrew Irvin, Emily Ruoyu Liu, Joyce C. Chen, Ines Dormoy, Jinyoung Kim, Samar Khanna, Zhuo Zheng, Stefano Ermon

ICLR 2025 TFG-Flow: Training-Free Guidance in Multimodal Generative Flow Haowei Lin, Shanda Li, Haotian Ye, Yiming Yang, Stefano Ermon, Yitao Liang, Jianzhu Ma

ICLR 2025 TabDiff: A Mixed-Type Diffusion Model for Tabular Data Generation Juntong Shi, Minkai Xu, Harper Hua, Hengrui Zhang, Stefano Ermon, Jure Leskovec

NeurIPS 2025 Training-Free Safe Denoisers for Safe Use of Diffusion Models Mingyu Kim, Dongjun Kim, Amman Yusuf, Stefano Ermon, Mijung Park

ICLRW 2025 Training-Free Safe Denoisers for Safe Use of Diffusion Models Mingyu Kim, Dongjun Kim, Amman Yusuf, Stefano Ermon, Mijung Park

ICML 2025 Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints Dapeng Jiang, Xiangzhe Kong, Jiaqi Han, Mingyu Li, Rui Jiao, Wenbing Huang, Stefano Ermon, Jianzhu Ma, Yang Liu

NeurIPS 2024 Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes Syrine Belakaria, Benjamin Letham, Janardhan Rao Doppa, Barbara Engelhardt, Stefano Ermon, Eytan Bakshy

NeurIPS 2024 Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization Siyi Gu, Minkai Xu, Alexander Powers, Weili Nie, Tomas Geffner, Karsten Kreis, Jure Leskovec, Arash Vahdat, Stefano Ermon

NeurIPSW 2024 CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion Joshua Kazdan, Hao Sun, Jiaqi Han, Felix Petersen, Frederick Vu, Stefano Ermon

ICLR 2024 Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Yutong He, Yuki Mitsufuji, Stefano Ermon

NeurIPS 2024 Convolutional Differentiable Logic Gate Networks Felix Petersen, Hilde Kuehne, Christian Borgelt, Julian Welzel, Stefano Ermon

ICLR 2024 Cross-Modal Contextualized Diffusion Models for Text-Guided Visual Generation and Editing Ling Yang, Zhilong Zhang, Zhaochen Yu, Jingwei Liu, Minkai Xu, Stefano Ermon, Bin Cui

ICLR 2024 Denoising Diffusion Bridge Models Linqi Zhou, Aaron Lou, Samar Khanna, Stefano Ermon

CVPR 2024 Diffusion Model Alignment Using Direct Preference Optimization Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, Nikhil Naik

ICLR 2024 DiffusionSat: A Generative Foundation Model for Satellite Imagery Samar Khanna, Patrick Liu, Linqi Zhou, Chenlin Meng, Robin Rombach, Marshall Burke, David B. Lobell, Stefano Ermon

ICML 2024 Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution Aaron Lou, Chenlin Meng, Stefano Ermon

CVPR 2024 DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling Linqi Zhou, Andy Shih, Chenlin Meng, Stefano Ermon

ICML 2024 Equivariant Graph Neural Operator for Modeling 3D Dynamics Minkai Xu, Jiaqi Han, Aaron Lou, Jean Kossaifi, Arvind Ramanathan, Kamyar Azizzadenesheli, Jure Leskovec, Stefano Ermon, Anima Anandkumar

ICMLW 2024 ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers Under Domain Shifts Samar Khanna, Medhanie Irgau, David B. Lobell, Stefano Ermon

NeurIPS 2024 Generative Fractional Diffusion Models Gabriel Nobis, Maximilian Springenberg, Marco Aversa, Michael Detzel, Rembert Daems, Roderick Murray-Smith, Shinichi Nakajima, Sebastian Lapuschkin, Stefano Ermon, Tolga Birdal, Manfred Opper, Christoph Knochenhauer, Luis Oala, Wojciech Samek

ICMLW 2024 Generative Fractional Diffusion Models Gabriel Nobis, Maximilian Springenberg, Marco Aversa, Michael Detzel, Rembert Daems, Roderick Murray-Smith, Shinichi Nakajima, Sebastian Lapuschkin, Stefano Ermon, Tolga Birdal, Manfred Opper, Christoph Knochenhauer, Luis Oala, Wojciech Samek

ICLR 2024 GeoLLM: Extracting Geospatial Knowledge from Large Language Models Rohin Manvi, Samar Khanna, Gengchen Mai, Marshall Burke, David B. Lobell, Stefano Ermon

NeurIPS 2024 Geometric Trajectory Diffusion Models Jiaqi Han, Minkai Xu, Aaron Lou, Haotian Ye, Stefano Ermon

CVPR 2024 HIVE: Harnessing Human Feedback for Instructional Visual Editing Shu Zhang, Xinyi Yang, Yihao Feng, Can Qin, Chia-Chih Chen, Ning Yu, Zeyuan Chen, Huan Wang, Silvio Savarese, Stefano Ermon, Caiming Xiong, Ran Xu

AAAI 2024 HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing Jonathan Xu, Amna Elmustafa, Liya Weldegebriel, Emnet Negash, Richard Lee, Chenlin Meng, Stefano Ermon, David B. Lobell

CVPRW 2024 HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing Jonathan Xu, Amna Elmustafa, Liya Weldegebriel, Emnet Negash, Richard Lee, Chenlin Meng, Stefano Ermon, David B. Lobell

ICLR 2024 Language Model Detectors Are Easily Optimized Against Charlotte Nicks, Eric Mitchell, Rafael Rafailov, Archit Sharma, Christopher D Manning, Chelsea Finn, Stefano Ermon

ICML 2024 Large Language Models Are Geographically Biased Rohin Manvi, Samar Khanna, Marshall Burke, David B. Lobell, Stefano Ermon

NeurIPS 2024 MADiff: Offline Multi-Agent Learning with Diffusion Models Zhengbang Zhu, Minghuan Liu, Liyuan Mao, Bingyi Kang, Minkai Xu, Yong Yu, Stefano Ermon, Weinan Zhang

ICLR 2024 Manifold Preserving Guided Diffusion Yutong He, Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Dongjun Kim, Wei-Hsiang Liao, Yuki Mitsufuji, J Zico Kolter, Ruslan Salakhutdinov, Stefano Ermon

ICML 2024 Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs Ling Yang, Zhaochen Yu, Chenlin Meng, Minkai Xu, Stefano Ermon, Bin Cui

ICML 2024 Mechanistic Design and Scaling of Hybrid Architectures Michael Poli, Armin W Thomas, Eric Nguyen, Pragaash Ponnusamy, Björn Deiseroth, Kristian Kersting, Taiji Suzuki, Brian Hie, Stefano Ermon, Christopher Re, Ce Zhang, Stefano Massaroli

NeurIPS 2024 Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms Felix Petersen, Christian Borgelt, Tobias Sutter, Hilde Kuehne, Oliver Deussen, Stefano Ermon

CVPR 2024 On the Scalability of Diffusion-Based Text-to-Image Generation Hao Li, Yang Zou, Ying Wang, Orchid Majumder, Yusheng Xie, R. Manmatha, Ashwin Swaminathan, Zhuowen Tu, Stefano Ermon, Stefano Soatto

NeurIPS 2024 PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon

ICML 2024 Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data Fahim Tajwar, Anikait Singh, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, Aviral Kumar

AISTATS 2024 Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients Chris J. Cundy, Rishi Desai, Stefano Ermon

NeurIPS 2024 Segment Any Change Zhuo Zheng, Yanfei Zhong, Liangpei Zhang, Stefano Ermon

NeurIPS 2024 Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations Nikil Roashan Selvam, Amil Merchant, Stefano Ermon

ICLR 2024 SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking Chris Cundy, Stefano Ermon

ICML 2024 State-Free Inference of State-Space Models: The *Transfer Function* Approach Rom Parnichkun, Stefano Massaroli, Alessandro Moro, Jimmy T.H. Smith, Ramin Hasani, Mathias Lechner, Qi An, Christopher Re, Hajime Asama, Stefano Ermon, Taiji Suzuki, Michael Poli, Atsushi Yamashita

NeurIPS 2024 TFG: Unified Training-Free Guidance for Diffusion Models Haotian Ye, Haowei Lin, Jiaqi Han, Minkai Xu, Sheng Liu, Yitao Liang, Jianzhu Ma, James Zou, Stefano Ermon

NeurIPSW 2024 TabDiff: A Unified Diffusion Model for Multi-Modal Tabular Data Generation Juntong Shi, Minkai Xu, Harper Hua, Hengrui Zhang, Stefano Ermon, Jure Leskovec

NeurIPS 2024 TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning Nemin Wu, Qian Cao, Zhangyu Wang, Zeping Liu, Yanlin Qi, Jielu Zhang, Joshua Ni, Xiaobai Yao, Hongxu Ma, Lan Mu, Stefano Ermon, Tanuja Ganu, Akshay Nambi, Ni Lao, Gengchen Mai

NeurIPS 2024 TrAct: Making First-Layer Pre-Activations Trainable Felix Petersen, Christian Borgelt, Stefano Ermon

AAAI 2024 Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution Tailin Wu, Willie Neiswanger, Hongtao Zheng, Stefano Ermon, Jure Leskovec

TMLR 2023 Beyond the Imitation Game: Quantifying and Extrapolating the Capabilities of Language Models Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ambrose Slone, Ameet Rahane, Anantharaman S. Iyer, Anders Johan Andreassen, Andrea Madotto, Andrea Santilli, Andreas Stuhlmüller, Andrew M. Dai, Andrew La, Andrew Kyle Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, Asher Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakaş, B. Ryan Roberts, Bao Sheng Loe, Barret Zoph, Bartłomiej Bojanowski, Batuhan Özyurt, Behnam Hedayatnia, Behnam Neyshabur, Benjamin Inden, Benno Stein, Berk Ekmekci, Bill Yuchen Lin, Blake Howald, Bryan Orinion, Cameron Diao, Cameron Dour, Catherine Stinson, Cedrick Argueta, Cesar Ferri, Chandan Singh, Charles Rathkopf, Chenlin Meng, Chitta Baral, Chiyu Wu, Chris Callison-Burch, Christopher Waites, Christian Voigt, Christopher D Manning, Christopher Potts, Cindy Ramirez, Clara E. Rivera, Clemencia Siro, Colin Raffel, Courtney Ashcraft, Cristina Garbacea, Damien Sileo, Dan Garrette, Dan Hendrycks, Dan Kilman, Dan Roth, C. Daniel Freeman, Daniel Khashabi, Daniel Levy, Daniel Moseguí González, Danielle Perszyk, Danny Hernandez, Danqi Chen, Daphne Ippolito, Dar Gilboa, David Dohan, David Drakard, David Jurgens, Debajyoti Datta, Deep Ganguli, Denis Emelin, Denis Kleyko, Deniz Yuret, Derek Chen, Derek Tam, Dieuwke Hupkes, Diganta Misra, Dilyar Buzan, Dimitri Coelho Mollo, Diyi Yang, Dong-Ho Lee, Dylan Schrader, Ekaterina Shutova, Ekin Dogus Cubuk, Elad Segal, Eleanor Hagerman, Elizabeth Barnes, Elizabeth Donoway, Ellie Pavlick, Emanuele Rodolà, Emma Lam, Eric Chu, Eric Tang, Erkut Erdem, Ernie Chang, Ethan A Chi, Ethan Dyer, Ethan Jerzak, Ethan Kim, Eunice Engefu Manyasi, Evgenii Zheltonozhskii, Fanyue Xia, Fatemeh Siar, Fernando Martínez-Plumed, Francesca Happé, Francois Chollet, Frieda Rong, Gaurav Mishra, Genta Indra Winata, Gerard de Melo, Germàn Kruszewski, Giambattista Parascandolo, Giorgio Mariani, Gloria Xinyue Wang, Gonzalo Jaimovitch-Lopez, Gregor Betz, Guy Gur-Ari, Hana Galijasevic, Hannah Kim, Hannah Rashkin, Hannaneh Hajishirzi, Harsh Mehta, Hayden Bogar, Henry Francis Anthony Shevlin, Hinrich Schuetze, Hiromu Yakura, Hongming Zhang, Hugh Mee Wong, Ian Ng, Isaac Noble, Jaap Jumelet, Jack Geissinger, Jackson Kernion, Jacob Hilton, Jaehoon Lee, Jaime Fernández Fisac, James B Simon, James Koppel, James Zheng, James Zou, Jan Kocon, Jana Thompson, Janelle Wingfield, Jared Kaplan, Jarema Radom, Jascha Sohl-Dickstein, Jason Phang, Jason Wei, Jason Yosinski, Jekaterina Novikova, Jelle Bosscher, Jennifer Marsh, Jeremy Kim, Jeroen Taal, Jesse Engel, Jesujoba Alabi, Jiacheng Xu, Jiaming Song, Jillian Tang, Joan Waweru, John Burden, John Miller, John U. Balis, Jonathan Batchelder, Jonathan Berant, Jörg Frohberg, Jos Rozen, Jose Hernandez-Orallo, Joseph Boudeman, Joseph Guerr, Joseph Jones, Joshua B. Tenenbaum, Joshua S. Rule, Joyce Chua, Kamil Kanclerz, Karen Livescu, Karl Krauth, Karthik Gopalakrishnan, Katerina Ignatyeva, Katja Markert, Kaustubh Dhole, Kevin Gimpel, Kevin Omondi, Kory Wallace Mathewson, Kristen Chiafullo, Ksenia Shkaruta, Kumar Shridhar, Kyle McDonell, Kyle Richardson, Laria Reynolds, Leo Gao, Li Zhang, Liam Dugan, Lianhui Qin, Lidia Contreras-Ochando, Louis-Philippe Morency, Luca Moschella, Lucas Lam, Lucy Noble, Ludwig Schmidt, Luheng He, Luis Oliveros-Colón, Luke Metz, Lütfi Kerem Senel, Maarten Bosma, Maarten Sap, Maartje Ter Hoeve, Maheen Farooqi, Manaal Faruqui, Mantas Mazeika, Marco Baturan, Marco Marelli, Marco Maru, Maria Jose Ramirez-Quintana, Marie Tolkiehn, Mario Giulianelli, Martha Lewis, Martin Potthast, Matthew L Leavitt, Matthias Hagen, Mátyás Schubert, Medina Orduna Baitemirova, Melody Arnaud, Melvin McElrath, Michael Andrew Yee, Michael Cohen, Michael Gu, Michael Ivanitskiy, Michael Starritt, Michael Strube, Michał Swędrowski, Michele Bevilacqua, Michihiro Yasunaga, Mihir Kale, Mike Cain, Mimee Xu, Mirac Suzgun, Mitch Walker, Mo Tiwari, Mohit Bansal, Moin Aminnaseri, Mor Geva, Mozhdeh Gheini, Mukund Varma T, Nanyun Peng, Nathan Andrew Chi, Nayeon Lee, Neta Gur-Ari Krakover, Nicholas Cameron, Nicholas Roberts, Nick Doiron, Nicole Martinez, Nikita Nangia, Niklas Deckers, Niklas Muennighoff, Nitish Shirish Keskar, Niveditha S. Iyer, Noah Constant, Noah Fiedel, Nuan Wen, Oliver Zhang, Omar Agha, Omar Elbaghdadi, Omer Levy, Owain Evans, Pablo Antonio Moreno Casares, Parth Doshi, Pascale Fung, Paul Pu Liang, Paul Vicol, Pegah Alipoormolabashi, Peiyuan Liao, Percy Liang, Peter W Chang, Peter Eckersley, Phu Mon Htut, Pinyu Hwang, Piotr Miłkowski, Piyush Patil, Pouya Pezeshkpour, Priti Oli, Qiaozhu Mei, Qing Lyu, Qinlang Chen, Rabin Banjade, Rachel Etta Rudolph, Raefer Gabriel, Rahel Habacker, Ramon Risco, Raphaël Millière, Rhythm Garg, Richard Barnes, Rif A. Saurous, Riku Arakawa, Robbe Raymaekers, Robert Frank, Rohan Sikand, Roman Novak, Roman Sitelew, Ronan Le Bras, Rosanne Liu, Rowan Jacobs, Rui Zhang, Russ Salakhutdinov, Ryan Andrew Chi, Seungjae Ryan Lee, Ryan Stovall, Ryan Teehan, Rylan Yang, Sahib Singh, Saif M. Mohammad, Sajant Anand, Sam Dillavou, Sam Shleifer, Sam Wiseman, Samuel Gruetter, Samuel R. Bowman, Samuel Stern Schoenholz, Sanghyun Han, Sanjeev Kwatra, Sarah A. Rous, Sarik Ghazarian, Sayan Ghosh, Sean Casey, Sebastian Bischoff, Sebastian Gehrmann, Sebastian Schuster, Sepideh Sadeghi, Shadi Hamdan, Sharon Zhou, Shashank Srivastava, Sherry Shi, Shikhar Singh, Shima Asaadi, Shixiang Shane Gu, Shubh Pachchigar, Shubham Toshniwal, Shyam Upadhyay, Shyamolima Shammie Debnath, Siamak Shakeri, Simon Thormeyer, Simone Melzi, Siva Reddy, Sneha Priscilla Makini, Soo-Hwan Lee, Spencer Torene, Sriharsha Hatwar, Stanislas Dehaene, Stefan Divic, Stefano Ermon, Stella Biderman, Stephanie Lin, Stephen Prasad, Steven Piantadosi, Stuart Shieber, Summer Misherghi, Svetlana Kiritchenko, Swaroop Mishra, Tal Linzen, Tal Schuster, Tao Li, Tao Yu, Tariq Ali, Tatsunori Hashimoto, Te-Lin Wu, Théo Desbordes, Theodore Rothschild, Thomas Phan, Tianle Wang, Tiberius Nkinyili, Timo Schick, Timofei Kornev, Titus Tunduny, Tobias Gerstenberg, Trenton Chang, Trishala Neeraj, Tushar Khot, Tyler Shultz, Uri Shaham, Vedant Misra, Vera Demberg, Victoria Nyamai, Vikas Raunak, Vinay Venkatesh Ramasesh, Vinay Uday Prabhu, Vishakh Padmakumar, Vivek Srikumar, William Fedus, William Saunders, William Zhang, Wout Vossen, Xiang Ren, Xiaoyu Tong, Xinran Zhao, Xinyi Wu, Xudong Shen, Yadollah Yaghoobzadeh, Yair Lakretz, Yangqiu Song, Yasaman Bahri, Yejin Choi, Yichi Yang, Sophie Hao, Yifu Chen, Yonatan Belinkov, Yu Hou, Yufang Hou, Yuntao Bai, Zachary Seid, Zhuoye Zhao, Zijian Wang, Zijie J. Wang, Zirui Wang, Ziyi Wu

AISTATS 2023 But Are You Sure? an Uncertainty-Aware Perspective on Explainable AI Charles Marx, Youngsuk Park, Hilaf Hasson, Yuyang Wang, Stefano Ermon, Luke Huan

ICML 2023 CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations Gengchen Mai, Ni Lao, Yutong He, Jiaming Song, Stefano Ermon

NeurIPS 2023 Calibration by Distribution Matching: Trainable Kernel Calibration Metrics Charlie Marx, Sofian Zalouk, Stefano Ermon

ICML 2023 Deep Latent State Space Models for Time-Series Generation Linqi Zhou, Michael Poli, Winnie Xu, Stefano Massaroli, Stefano Ermon

NeurIPS 2023 Direct Preference Optimization: Your Language Model Is Secretly a Reward Model Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, Chelsea Finn

ICMLW 2023 Direct Preference Optimization: Your Language Model Is Secretly a Reward Model Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D Manning, Chelsea Finn

ICLR 2023 Dual Diffusion Implicit Bridges for Image-to-Image Translation Xuan Su, Jiaming Song, Chenlin Meng, Stefano Ermon

ICCV 2023 End-to-End Diffusion Latent Optimization Improves Classifier Guidance Bram Wallace, Akash Gokul, Stefano Ermon, Nikhil Naik

NeurIPS 2023 Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation Yuxuan Song, Jingjing Gong, Minkai Xu, Ziyao Cao, Yanyan Lan, Stefano Ermon, Hao Zhou, Wei-Ying Ma

ICLR 2023 Extreme Q-Learning: MaxEnt RL Without Entropy Divyansh Garg, Joey Hejna, Matthieu Geist, Stefano Ermon

ICML 2023 FP-Diffusion: Improving Score-Based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon

NeurIPS 2023 GEO-Bench: Toward Foundation Models for Earth Monitoring Alexandre Lacoste, Nils Lehmann, Pau Rodriguez, Evan Sherwin, Hannah Kerner, Björn Lütjens, Jeremy Irvin, David Dao, Hamed Alemohammad, Alexandre Drouin, Mehmet Gunturkun, Gabriel Huang, David Vazquez, Dava Newman, Yoshua Bengio, Stefano Ermon, Xiaoxiang Zhu

ICLR 2023 Generative Modeling Helps Weak Supervision (and Vice Versa) Benedikt Boecking, Nicholas Roberts, Willie Neiswanger, Stefano Ermon, Frederic Sala, Artur Dubrawski

ICML 2023 Geometric Latent Diffusion Models for 3D Molecule Generation Minkai Xu, Alexander S Powers, Ron O. Dror, Stefano Ermon, Jure Leskovec

ICML 2023 GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration Naoki Murata, Koichi Saito, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon

ICCV 2023 GlueGen: Plug and Play Multi-Modal Encoders for X-to-Image Generation Can Qin, Ning Yu, Chen Xing, Shu Zhang, Zeyuan Chen, Stefano Ermon, Yun Fu, Caiming Xiong, Ran Xu

NeurIPS 2023 Holistic Evaluation of Text-to-Image Models Tony Lee, Michihiro Yasunaga, Chenlin Meng, Yifan Mai, Joon Sung Park, Agrim Gupta, Yunzhi Zhang, Deepak Narayanan, Hannah Teufel, Marco Bellagente, Minguk Kang, Taesung Park, Jure Leskovec, Jun-Yan Zhu, Fei-Fei Li, Jiajun Wu, Stefano Ermon, Percy Liang

ICML 2023 Hyena Hierarchy: Towards Larger Convolutional Language Models Michael Poli, Stefano Massaroli, Eric Nguyen, Daniel Y Fu, Tri Dao, Stephen Baccus, Yoshua Bengio, Stefano Ermon, Christopher Re

NeurIPS 2023 HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution Eric Nguyen, Michael Poli, Marjan Faizi, Armin Thomas, Michael Wornow, Callum Birch-Sykes, Stefano Massaroli, Aman Patel, Clayton Rabideau, Yoshua Bengio, Stefano Ermon, Christopher Ré, Stephen Baccus

AISTATS 2023 Ideal Abstractions for Decision-Focused Learning Michael Poli, Stefano Massaroli, Stefano Ermon, Bryan Wilder, Eric Horvitz

NeurIPSW 2023 Language Model Detectors Are Easily Optimized Against Charlotte Nicks, Eric Mitchell, Rafael Rafailov, Archit Sharma, Christopher Manning, Chelsea Finn, Stefano Ermon

NeurIPS 2023 Laughing Hyena Distillery: Extracting Compact Recurrences from Convolutions Stefano Massaroli, Michael Poli, Dan Fu, Hermann Kumbong, Rom Parnichkun, David Romero, Aman Timalsina, Quinn McIntyre, Beidi Chen, Atri Rudra, Ce Zhang, Christopher Ré, Stefano Ermon, Yoshua Bengio

ICML 2023 Long Horizon Temperature Scaling Andy Shih, Dorsa Sadigh, Stefano Ermon

TMLR 2023 MERMAIDE: Learning to Align Learners Using Model-Based Meta-Learning Arundhati Banerjee, Soham Rajesh Phade, Stefano Ermon, Stephan Zheng

NeurIPSW 2023 MERMAIDE: Learning to Align Learners Using Model-Based Meta-Learning Arundhati Banerjee, Soham Phade, Stefano Ermon, Stephan Zheng

LoG 2023 MUDiff: Unified Diffusion for Complete Molecule Generation Chenqing Hua, Sitao Luan, Minkai Xu, Zhitao Ying, Jie Fu, Stefano Ermon, Doina Precup

NeurIPSW 2023 Multi-Head CLIP: Improving CLIP with Diverse Representations and Flat Minima Mo Zhou, Xiong Zhou, Li Erran Li, Stefano Ermon, Rong Ge

AAAI 2023 Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching Lantao Yu, Tianhe Yu, Jiaming Song, Willie Neiswanger, Stefano Ermon

CVPR 2023 On Distillation of Guided Diffusion Models Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans

ICMLW 2023 On the Equivalence of Consistency-Type Models: Consistency Models, Consistent Diffusion Models, and Fokker-Planck Regularization Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Naoki Murata, Yuki Mitsufuji, Stefano Ermon

NeurIPS 2023 Parallel Sampling of Diffusion Models Andy Shih, Suneel Belkhale, Stefano Ermon, Dorsa Sadigh, Nima Anari

ICMLW 2023 Parallel Sampling of Diffusion Models Andy Shih, Suneel Belkhale, Stefano Ermon, Dorsa Sadigh, Nima Anari

ICML 2023 Reflected Diffusion Models Aaron Lou, Stefano Ermon

NeurIPSW 2023 Scalable Deep Potentials as Implicit Hierarchical Semi-Separable Operators Michael Poli, Stefano Massaroli, Christopher Re, Stefano Ermon

NeurIPS 2023 Scaling Riemannian Diffusion Models Aaron Lou, Minkai Xu, Adam Farris, Stefano Ermon

NeurIPSW 2023 The Role of Linguistic Priors in Measuring Compositional Generalization of Vision-Language Models Chenwei Wu, Patrick Haffner, Li Erran Li, Stefano Ermon, Rong Ge

ICLR 2023 Understanding and Adopting Rational Behavior by Bellman Score Estimation Kuno Kim, Stefano Ermon

NeurIPS 2023 UniControl: A Unified Diffusion Model for Controllable Visual Generation in the Wild Can Qin, Shu Zhang, Ning Yu, Yihao Feng, Xinyi Yang, Yingbo Zhou, Huan Wang, Juan Carlos Niebles, Caiming Xiong, Silvio Savarese, Stefano Ermon, Yun Fu, Ran Xu

AISTATS 2022 Density Ratio Estimation via Infinitesimal Classification Kristy Choi, Chenlin Meng, Yang Song, Stefano Ermon

ICML 2022 A General Recipe for Likelihood-Free Bayesian Optimization Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon

ICLR 2022 An Experimental Design Perspective on Model-Based Reinforcement Learning Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger

ICML 2022 Bit Prioritization in Variational Autoencoders via Progressive Coding Rui Shu, Stefano Ermon

NeurIPSW 2022 But Are You Sure? Quantifying Uncertainty in Model Explanations Charles Thomas Marx, Youngsuk Park, Hilaf Hasson, Bernie Wang, Stefano Ermon, Luke Huan

ICML 2022 ButterflyFlow: Building Invertible Layers with Butterfly Matrices Chenlin Meng, Linqi Zhou, Kristy Choi, Tri Dao, Stefano Ermon

ICLR 2022 Comparing Distributions by Measuring Differences That Affect Decision Making Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon

NeurIPS 2022 Concrete Score Matching: Generalized Score Matching for Discrete Data Chenlin Meng, Kristy Choi, Jiaming Song, Stefano Ermon

NeurIPS 2022 Denoising Diffusion Restoration Models Bahjat Kawar, Michael Elad, Stefano Ermon, Jiaming Song

ICLRW 2022 Denoising Diffusion Restoration Models Bahjat Kawar, Michael Elad, Stefano Ermon, Jiaming Song

CVPRW 2022 Efficient Conditional Pre-Training for Transfer Learning Shuvam Chakraborty, Burak Uzkent, Kumar Ayush, Kumar Tanmay, Evan Sheehan, Stefano Ermon

NeurIPS 2022 Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models Muyang Li, Ji Lin, Chenlin Meng, Stefano Ermon, Song Han, Jun-Yan Zhu

L4DC 2022 Experience Replay with Likelihood-Free Importance Weights Samarth Sinha, Jiaming Song, Animesh Garg, Stefano Ermon

NeurIPS 2022 Exploration via Planning for Information About the Optimal Trajectory Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark Boyer, Stefano Ermon, Jeff G. Schneider, Willie Neiswanger

NeurIPS 2022 FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, Christopher Ré

NeurIPS 2022 Generalizing Bayesian Optimization with Decision-Theoretic Entropies Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon

ICLR 2022 GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang

AAAI 2022 IS-Count: Large-Scale Object Counting from Satellite Images with Covariate-Based Importance Sampling Chenlin Meng, Enci Liu, Willie Neiswanger, Jiaming Song, Marshall Burke, David B. Lobell, Stefano Ermon

ICML 2022 Imitation Learning by Estimating Expertise of Demonstrators Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, Ramtin Pedarsani

NeurIPS 2022 Improving Self-Supervised Learning by Characterizing Idealized Representations Yann Dubois, Stefano Ermon, Tatsunori B Hashimoto, Percy Liang

NeurIPSW 2022 JPEG Artifact Correction Using Denoising Diffusion Restoration Models Bahjat Kawar, Jiaming Song, Stefano Ermon, Michael Elad

NeurIPS 2022 LISA: Learning Interpretable Skill Abstractions from Language Divyansh Garg, Skanda Vaidyanath, Kuno Kim, Jiaming Song, Stefano Ermon

NeurIPSW 2022 LMPriors: Pre-Trained Language Models as Task-Specific Priors Kristy Choi, Chris Cundy, Sanjari Srivastava, Stefano Ermon

UAI 2022 Local Calibration: Metrics and Recalibration Rachel Luo, Aadyot Bhatnagar, Yu Bai, Shengjia Zhao, Huan Wang, Caiming Xiong, Silvio Savarese, Stefano Ermon, Edward Schmerling, Marco Pavone

ICML 2022 Modular Conformal Calibration Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon

NeurIPSW 2022 On Distillation of Guided Diffusion Models Chenlin Meng, Ruiqi Gao, Diederik P Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans

NeurIPSW 2022 Regularizing Score-Based Models with Score Fokker-Planck Equations Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon

NeurIPSW 2022 Relaxing the Kolmogorov Structure Function for Realistic Computational Constraints Yoonho Lee, Chelsea Finn, Stefano Ermon

ICLR 2022 SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon

NeurIPS 2022 SatMAE: Pre-Training Transformers for Temporal and Multi-Spectral Satellite Imagery Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David Lobell, Stefano Ermon

NeurIPS 2022 Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations Michael Poli, Winnie Xu, Stefano Massaroli, Chenlin Meng, Kuno Kim, Stefano Ermon

ICLR 2022 Solving Inverse Problems in Medical Imaging with Score-Based Generative Models Yang Song, Liyue Shen, Lei Xing, Stefano Ermon

NeurIPS 2022 Training and Inference on Any-Order Autoregressive Models the Right Way Andy Shih, Dorsa Sadigh, Stefano Ermon

NeurIPS 2022 Transform Once: Efficient Operator Learning in Frequency Domain Michael Poli, Stefano Massaroli, Federico Berto, Jinkyoo Park, Tri Dao, Christopher Ré, Stefano Ermon

ICMLW 2022 Transform Once: Efficient Operator Learning in Frequency Domain Michael Poli, Stefano Massaroli, Federico Berto, Jinkyoo Park, Tri Dao, Christopher Re, Stefano Ermon

AISTATS 2021 Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration Shengjia Zhao, Stefano Ermon

ICML 2021 Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon

ICLR 2021 Anytime Sampling for Autoregressive Models via Ordered Autoencoding Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon

NeurIPS 2021 BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery Chris Cundy, Aditya Grover, Stefano Ermon

ICML 2021 Bayesian Algorithm Execution: Estimating Computable Properties of Black-Box Functions Using Mutual Information Willie Neiswanger, Ke Alexander Wang, Stefano Ermon

NeurIPS 2021 CSDI: Conditional Score-Based Diffusion Models for Probabilistic Time Series Imputation Yusuke Tashiro, Jiaming Song, Yang Song, Stefano Ermon

NeurIPS 2021 Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration Shengjia Zhao, Michael Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon

NeurIPS 2021 D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation Abhishek Sinha, Jiaming Song, Chenlin Meng, Stefano Ermon

ICLR 2021 Denoising Diffusion Implicit Models Jiaming Song, Chenlin Meng, Stefano Ermon

AAAI 2021 Efficient Poverty Mapping from High Resolution Remote Sensing Images Kumar Ayush, Burak Uzkent, Kumar Tanmay, Marshall Burke, David B. Lobell, Stefano Ermon

NeurIPS 2021 Estimating High Order Gradients of the Data Distribution by Denoising Chenlin Meng, Yang Song, Wenzhe Li, Stefano Ermon

ICLR 2021 Evaluating the Disentanglement of Deep Generative Models Through Manifold Topology Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Gunnar E. Carlsson, Stefano Ermon

UAI 2021 Featurized Density Ratio Estimation Kristy Choi, Madeline Liao, Stefano Ermon

ICCV 2021 Geography-Aware Self-Supervised Learning Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David Lobell, Stefano Ermon

ICLRW 2021 Hybrid Mutual Information Lower-Bound Estimators for Representation Learning Abhishek Sinha, Jiaming Song, Stefano Ermon

NeurIPS 2021 HyperSPNs: Compact and Expressive Probabilistic Circuits Andy Shih, Dorsa Sadigh, Stefano Ermon

NeurIPS 2021 IQ-Learn: Inverse Soft-Q Learning for Imitation Divyansh Garg, Shuvam Chakraborty, Chris Cundy, Jiaming Song, Stefano Ermon

NeurIPS 2021 Imitation with Neural Density Models Kuno Kim, Akshat Jindal, Yang Song, Jiaming Song, Yanan Sui, Stefano Ermon

ICLR 2021 Improved Autoregressive Modeling with Distribution Smoothing Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon

NeurIPS 2021 Improving Compositionality of Neural Networks by Decoding Representations to Inputs Mike Wu, Noah Goodman, Stefano Ermon

NeurIPS 2021 Maximum Likelihood Training of Score-Based Diffusion Models Yang Song, Conor Durkan, Iain Murray, Stefano Ermon

ECML-PKDD 2021 Multi-Agent Imitation Learning with Copulas Hongwei Wang, Lantao Yu, Zhangjie Cao, Stefano Ermon

ICLR 2021 Negative Data Augmentation Abhishek Sinha, Kumar Ayush, Jiaming Song, Burak Uzkent, Hongxia Jin, Stefano Ermon

ICLR 2021 On the Critical Role of Conventions in Adaptive Human-AI Collaboration Andy Shih, Arjun Sawhney, Jovana Kondic, Stefano Ermon, Dorsa Sadigh

NeurIPS 2021 PiRank: Scalable Learning to Rank via Differentiable Sorting Robin Swezey, Aditya Grover, Bruno Charron, Stefano Ermon

AAAI 2021 Predicting Livelihood Indicators from Community-Generated Street-Level Imagery Jihyeon Janel Lee, Dylan Grosz, Burak Uzkent, Sicheng Zeng, Marshall Burke, David B. Lobell, Stefano Ermon

NeurIPS 2021 Pseudo-Spherical Contrastive Divergence Lantao Yu, Jiaming Song, Yang Song, Stefano Ermon

NeurIPS 2021 Reliable Decisions with Threshold Calibration Roshni Sahoo, Shengjia Zhao, Alyssa Chen, Stefano Ermon

ICML 2021 Reward Identification in Inverse Reinforcement Learning Kuno Kim, Shivam Garg, Kirankumar Shiragur, Stefano Ermon

ICLR 2021 Score-Based Generative Modeling Through Stochastic Differential Equations Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole

NeurIPSW 2021 Solving Inverse Problems in Medical Imaging with Score-Based Generative Models Yang Song, Liyue Shen, Lei Xing, Stefano Ermon

NeurIPS 2021 Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis Yutong He, Dingjie Wang, Nicholas Lai, William Zhang, Chenlin Meng, Marshall Burke, David Lobell, Stefano Ermon

ICML 2021 Temporal Predictive Coding for Model-Based Planning in Latent Space Tung D Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon

AISTATS 2020 A Framework for Sample Efficient Interval Estimation with Control Variates Shengjia Zhao, Christopher Yeh, Stefano Ermon

ICLR 2020 A Theory of Usable Information Under Computational Constraints Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon

AAAI 2020 AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon

NeurIPS 2020 Autoregressive Score Matching Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon

NeurIPS 2020 Belief Propagation Neural Networks Jonathan Kuck, Shuvam Chakraborty, Hao Tang, Rachel Luo, Jiaming Song, Ashish Sabharwal, Stefano Ermon

ICML 2020 Bridging the Gap Between F-GANs and Wasserstein GANs Jiaming Song, Stefano Ermon

WACV 2020 Cloud Removal from Satellite Images Using Spatiotemporal Generator Networks Vishnu Sarukkai, Anirudh Jain, Burak Uzkent, Stefano Ermon

NeurIPS 2020 Diversity Can Be Transferred: Output Diversification for White- and Black-Box Attacks Yusuke Tashiro, Yang Song, Stefano Ermon

ICML 2020 Domain Adaptive Imitation Learning Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon

NeurIPS 2020 Efficient Learning of Generative Models via Finite-Difference Score Matching Tianyu Pang, Kun Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu

WACV 2020 Efficient Object Detection in Large Images Using Deep Reinforcement Learning Burak Uzkent, Christopher Yeh, Stefano Ermon

ICML 2020 Fair Generative Modeling via Weak Supervision Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, Stefano Ermon

CVPRW 2020 Farm Parcel Delineation Using Spatio-Temporal Convolutional Networks Han Lin Aung, Burak Uzkent, Marshall Burke, David B. Lobell, Stefano Ermon

UAI 2020 Flexible Approximate Inference via Stratified Normalizing Flows Chris Cundy, Stefano Ermon

AISTATS 2020 Gaussianization Flows Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon

IJCAI 2020 Generating Interpretable Poverty Maps Using Object Detection in Satellite Images Kumar Ayush, Burak Uzkent, Marshall Burke, David B. Lobell, Stefano Ermon

NeurIPS 2020 HiPPO: Recurrent Memory with Optimal Polynomial Projections Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, Christopher Ré

NeurIPS 2020 Improved Techniques for Training Score-Based Generative Models Yang Song, Stefano Ermon

ICML 2020 Individual Calibration with Randomized Forecasting Shengjia Zhao, Tengyu Ma, Stefano Ermon

NeurIPS 2020 MOPO: Model-Based Offline Policy Optimization Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y Zou, Sergey Levine, Chelsea Finn, Tengyu Ma

AAAI 2020 Meta-Amortized Variational Inference and Learning Mike Wu, Kristy Choi, Noah D. Goodman, Stefano Ermon

NeurIPS 2020 Multi-Label Contrastive Predictive Coding Jiaming Song, Stefano Ermon

NeurIPSW 2020 Noisy Neural Network Compression for Analog Storage Devices Berivan Isik, Kristy Choi, Xin Zheng, H.-S. Philip Wong, Stefano Ermon, Tsachy Weissman, Armin Alaghi

AISTATS 2020 Permutation Invariant Graph Generation via Score-Based Generative Modeling Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon

ICML 2020 Predictive Coding for Locally-Linear Control Rui Shu, Tung Nguyen, Yinlam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung Bui

NeurIPS 2020 Probabilistic Circuits for Variational Inference in Discrete Graphical Models Andy Shih, Stefano Ermon

ICML 2020 Training Deep Energy-Based Models with F-Divergence Minimization Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon

ICLR 2020 Understanding the Limitations of Variational Mutual Information Estimators Jiaming Song, Stefano Ermon

ICLR 2020 Weakly Supervised Disentanglement with Guarantees Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, Ben Poole

ICML 2019 Adaptive Antithetic Sampling for Variance Reduction Hongyu Ren, Shengjia Zhao, Stefano Ermon

UAI 2019 Adaptive Hashing for Model Counting Jonathan Kuck, Tri Dao, Shengjia Zhao, Burak Bartan, Ashish Sabharwal, Stefano Ermon

ICLRW 2019 AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon

NeurIPS 2019 Approximating the Permanent by Sampling from Adaptive Partitions Jonathan Kuck, Tri Dao, Hamid Rezatofighi, Ashish Sabharwal, Stefano Ermon

NeurIPS 2019 Bias Correction of Learned Generative Models Using Likelihood-Free Importance Weighting Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric J Horvitz, Stefano Ermon

ICLRW 2019 Bias Correction of Learned Generative Models via Likelihood-Free Importance Weighting Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric Horvitz, Stefano Ermon

ICML 2019 Calibrated Model-Based Deep Reinforcement Learning Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon

AISTATS 2019 Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference Mike Wu, Noah Goodman, Stefano Ermon

NeurIPS 2019 Generative Modeling by Estimating Gradients of the Data Distribution Yang Song, Stefano Ermon

ICML 2019 Graphite: Iterative Generative Modeling of Graphs Aditya Grover, Aaron Zweig, Stefano Ermon

AAAI 2019 InfoVAE: Balancing Learning and Inference in Variational Autoencoders Shengjia Zhao, Jiaming Song, Stefano Ermon

AISTATS 2019 Learning Controllable Fair Representations Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon

ICLR 2019 Learning Neural PDE Solvers with Convergence Guarantees Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon

IJCAI 2019 Learning to Interpret Satellite Images Using Wikipedia Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang, Marshall Burke, David B. Lobell, Stefano Ermon

NeurIPS 2019 Meta-Inverse Reinforcement Learning with Probabilistic Context Variables Lantao Yu, Tianhe Yu, Chelsea Finn, Stefano Ermon

NeurIPS 2019 MintNet: Building Invertible Neural Networks with Masked Convolutions Yang Song, Chenlin Meng, Stefano Ermon

ICML 2019 Multi-Agent Adversarial Inverse Reinforcement Learning Lantao Yu, Jiaming Song, Stefano Ermon

ICML 2019 Neural Joint Source-Channel Coding Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon

IJCAI 2019 Reparameterizable Subset Sampling via Continuous Relaxations Sang Michael Xie, Stefano Ermon

CVPRW 2019 Semantic Segmentation of Crop Type in Africa: A Novel Dataset and Analysis of Deep Learning Methods Rose M. Rustowicz, Robin Cheong, Lijing Wang, Stefano Ermon, Marshall Burke, David B. Lobell

UAI 2019 Sliced Score Matching: A Scalable Approach to Density and Score Estimation Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon

ICLR 2019 Stochastic Optimization of Sorting Networks via Continuous Relaxations Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon

NeurIPS 2019 Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. Sawyer Birnbaum, Volodymyr Kuleshov, Zayd Enam, Pang Wei W Koh, Stefano Ermon

AAAI 2019 Tile2Vec: Unsupervised Representation Learning for Spatially Distributed Data Neal Jean, Sherrie Wang, Anshul Samar, George Azzari, David B. Lobell, Stefano Ermon

AISTATS 2019 Training Variational Autoencoders with Buffered Stochastic Variational Inference Rui Shu, Hung Bui, Jay Whang, Stefano Ermon

AISTATS 2019 Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization Aditya Grover, Stefano Ermon

ICLR 2018 A DIRT-T Approach to Unsupervised Domain Adaptation Rui Shu, Hung Bui, Hirokazu Narui, Stefano Ermon

UAI 2018 A Lagrangian Perspective on Latent Variable Generative Models Shengjia Zhao, Jiaming Song, Stefano Ermon

ICML 2018 Accelerating Natural Gradient with Higher-Order Invariance Yang Song, Jiaming Song, Stefano Ermon

ICML 2018 Accurate Uncertainties for Deep Learning Using Calibrated Regression Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon

IJCAI 2018 Adversarial Constraint Learning for Structured Prediction Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon

NeurIPS 2018 Amortized Inference Regularization Rui Shu, Hung H Bui, Shengjia Zhao, Mykel J Kochenderfer, Stefano Ermon

AAAI 2018 Approximate Inference via Weighted Rademacher Complexity Jonathan Kuck, Ashish Sabharwal, Stefano Ermon

UAI 2018 Bayesian Optimization and Attribute Adjustment Stephan Eismann, Daniel Levy, Rui Shu, Stefan Bartzsch, Stefano Ermon

AISTATS 2018 Best Arm Identification in Multi-Armed Bandits with Delayed Feedback Aditya Grover, Todor M. Markov, Peter M. Attia, Norman Jin, Nicolas Perkins, Bryan Cheong, Michael H. Chen, Zi Yang, Stephen J. Harris, William C. Chueh, Stefano Ermon

NeurIPS 2018 Bias and Generalization in Deep Generative Models: An Empirical Study Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah Goodman, Stefano Ermon

AAAI 2018 Boosted Generative Models Aditya Grover, Stefano Ermon

NeurIPS 2018 Constructing Unrestricted Adversarial Examples with Generative Models Yang Song, Rui Shu, Nate Kushman, Stefano Ermon

AAAI 2018 Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces Daniel Levy, Stefano Ermon

AAAI 2018 Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models Aditya Grover, Manik Dhar, Stefano Ermon

ICML 2018 Modeling Sparse Deviations for Compressed Sensing Using Generative Models Manik Dhar, Aditya Grover, Stefano Ermon

NeurIPS 2018 Multi-Agent Generative Adversarial Imitation Learning Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon

ICLR 2018 PixelDefend: Leveraging Generative Models to Understand and Defend Against Adversarial Examples Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman

NeurIPS 2018 Semi-Supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance Neal Jean, Sang Michael Xie, Stefano Ermon

NeurIPS 2018 Streamlining Variational Inference for Constraint Satisfaction Problems Aditya Grover, Tudor Achim, Stefano Ermon

AISTATS 2018 Variational Rejection Sampling Aditya Grover, Ramki Gummadi, Miguel Lázaro-Gredilla, Dale Schuurmans, Stefano Ermon

NeurIPS 2017 A-NICE-MC: Adversarial Training for MCMC Jiaming Song, Shengjia Zhao, Stefano Ermon

ICLR 2017 Audio Super-Resolution Using Neural Networks Volodymyr Kuleshov, S. Zayd Enam, Stefano Ermon

AAAI 2017 Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data Jiaxuan You, Xiaocheng Li, Melvin Low, David B. Lobell, Stefano Ermon

AAAI 2017 Estimating Uncertainty Online Against an Adversary Volodymyr Kuleshov, Stefano Ermon

UAI 2017 Fast Amortized Inference and Learning in Log-Linear Models with Randomly Perturbed Nearest Neighbor Search Stephen Mussmann, Daniel Levy, Stefano Ermon

AAAI 2017 General Bounds on Satisfiability Thresholds for Random CSPs via Fourier Analysis Colin Wei, Stefano Ermon

ICLR 2017 Generative Adversarial Learning of Markov Chains Jiaming Song, Shengjia Zhao, Stefano Ermon

UAI 2017 Hybrid Deep Discriminative/Generative Models for Semi-Supervised Learning Volodymyr Kuleshov, Stefano Ermon

NeurIPS 2017 InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations Yunzhu Li, Jiaming Song, Stefano Ermon

AAAI 2017 Label-Free Supervision of Neural Networks with Physics and Domain Knowledge Russell Stewart, Stefano Ermon

ICML 2017 Learning Hierarchical Features from Deep Generative Models Shengjia Zhao, Jiaming Song, Stefano Ermon

CVPRW 2017 Monitoring Ethiopian Wheat Fungus with Satellite Imagery and Deep Feature Learning Reid Pryzant, Stefano Ermon, David B. Lobell

NeurIPS 2017 Neural Variational Inference and Learning in Undirected Graphical Models Volodymyr Kuleshov, Stefano Ermon

NeurIPS 2016 Adaptive Concentration Inequalities for Sequential Decision Problems Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon

ICML 2016 Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference Tudor Achim, Ashish Sabharwal, Stefano Ermon

AAAI 2016 Closing the Gap Between Short and Long XORs for Model Counting Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, Stefano Ermon

AAAI 2016 Exact Sampling with Integer Linear Programs and Random Perturbations Carolyn Kim, Ashish Sabharwal, Stefano Ermon

NeurIPS 2016 Generative Adversarial Imitation Learning Jonathan Ho, Stefano Ermon

ICML 2016 Learning and Inference via Maximum Inner Product Search Stephen Mussmann, Stefano Ermon

ICML 2016 Model-Free Imitation Learning with Policy Optimization Jonathan Ho, Jayesh Gupta, Stefano Ermon

NeurIPS 2016 Solving Marginal MAP Problems with NP Oracles and Parity Constraints Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla P. Gomes, Bart Selman

UAI 2016 Sparse Gaussian Processes for Bayesian Optimization Mitchell McIntire, Daniel Ratner, Stefano Ermon

AISTATS 2016 Tight Variational Bounds via Random Projections and I-Projections Lun-Kai Hsu, Tudor Achim, Stefano Ermon

AAAI 2016 Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping Sang Michael Xie, Neal Jean, Marshall Burke, David B. Lobell, Stefano Ermon

ICML 2016 Variable Elimination in the Fourier Domain Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla, Bart Selman

NeurIPS 2016 Variational Bayes on Monte Carlo Steroids Aditya Grover, Stefano Ermon

ICML 2015 A Hybrid Approach for Probabilistic Inference Using Random Projections Michael Zhu, Stefano Ermon

UAI 2015 Importance Sampling over Sets: A New Probabilistic Inference Scheme Stefan Hadjis, Stefano Ermon

AAAI 2015 Learning Large-Scale Dynamic Discrete Choice Models of Spatio-Temporal Preferences with Application to Migratory Pastoralism in East Africa Stefano Ermon, Yexiang Xue, Russell Toth, Bistra Dilkina, Richard Bernstein, Theodoros Damoulas, Patrick E. Clark, Steve DeGloria, Andrew Mude, Christopher Barrett, Carla P. Gomes

AAAI 2015 Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery Stefano Ermon, Ronan Le Bras, Santosh K. Suram, John M. Gregoire, Carla P. Gomes, Bart Selman, Robert Bruce van Dover

IJCAI 2015 Uncovering Hidden Structure Through Parallel Problem Decomposition for the Set Basis Problem: Application to Materials Discovery Yexiang Xue, Stefano Ermon, Carla P. Gomes, Bart Selman

AAAI 2014 Designing Fast Absorbing Markov Chains Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman

ICML 2014 Low-Density Parity Constraints for Hashing-Based Discrete Integration Stefano Ermon, Carla Gomes, Ashish Sabharwal, Bart Selman

AAAI 2014 Uncovering Hidden Structure Through Parallel Problem Decomposition Yexiang Xue, Stefano Ermon, Carla P. Gomes, Bart Selman

NeurIPS 2013 Embed and Project: Discrete Sampling with Universal Hashing Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman

MLJ 2013 Learning Policies for Battery Usage Optimization in Electric Vehicles Stefano Ermon, Yexiang Xue, Carla P. Gomes, Bart Selman

UAI 2013 Optimization with Parity Constraints: From Binary Codes to Discrete Integration Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman

ICML 2013 Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization Stefano Ermon, Carla Gomes, Ashish Sabharwal, Bart Selman

NeurIPS 2012 Density Propagation and Improved Bounds on the Partition Function Stefano Ermon, Ashish Sabharwal, Bart Selman, Carla P. Gomes

ECML-PKDD 2012 Feature-Enhanced Probabilistic Models for Diffusion Network Inference Liaoruo Wang, Stefano Ermon, John E. Hopcroft

ECML-PKDD 2012 Learning Policies for Battery Usage Optimization in Electric Vehicles Stefano Ermon, Yexiang Xue, Carla P. Gomes, Bart Selman

UAI 2012 Uniform Solution Sampling Using a Constraint Solver as an Oracle Stefano Ermon, Carla P. Gomes, Bart Selman

IJCAI 2011 A Flat Histogram Method for Computing the Density of States of Combinatorial Problems Stefano Ermon, Carla P. Gomes, Bart Selman

NeurIPS 2011 Accelerated Adaptive Markov Chain for Partition Function Computation Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman

IJCAI 2011 Risk-Sensitive Policies for Sustainable Renewable Resource Allocation Stefano Ermon, Jon Conrad, Carla P. Gomes, Bart Selman

UAI 2010 Playing Games Against Nature: Optimal Policies for Renewable Resource Allocation Stefano Ermon, Jon Conrad, Carla P. Gomes, Bart Selman