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Equivariant Neural Network for Factor Graphs


Sep 29, 2021
Fan-Yun Sun, Jonathan Kuck, Hao Tang, Stefano Ermon


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On the Opportunities and Risks of Foundation Models


Aug 18, 2021
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Kohd, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, Aditi Raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

* Authored by the Center for Research on Foundation Models (CRFM) at the Stanford Institute for Human-Centered Artificial Intelligence (HAI) 

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SDEdit: Image Synthesis and Editing with Stochastic Differential Equations


Aug 02, 2021
Chenlin Meng, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon

* https://chenlin9.github.io/SDEdit/ 

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Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration


Jul 12, 2021
Shengjia Zhao, Michael P. Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon


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Multi-Agent Imitation Learning with Copulas


Jul 10, 2021
Hongwei Wang, Lantao Yu, Zhangjie Cao, Stefano Ermon

* ECML-PKDD 2021. First two authors contributed equally 

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CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation


Jul 07, 2021
Yusuke Tashiro, Jiaming Song, Yang Song, Stefano Ermon


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Featurized Density Ratio Estimation


Jul 05, 2021
Kristy Choi, Madeline Liao, Stefano Ermon

* First two authors contributed equally 

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IQ-Learn: Inverse soft-Q Learning for Imitation


Jun 23, 2021
Divyansh Garg, Shuvam Chakraborty, Chris Cundy, Jiaming Song, Stefano Ermon


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Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis


Jun 22, 2021
Yutong He, Dingjie Wang, Nicholas Lai, William Zhang, Chenlin Meng, Marshall Burke, David B. Lobell, Stefano Ermon


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Temporal Predictive Coding For Model-Based Planning In Latent Space


Jun 14, 2021
Tung Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon

* International Conference on Machine Learning 

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D2C: Diffusion-Denoising Models for Few-shot Conditional Generation


Jun 12, 2021
Abhishek Sinha, Jiaming Song, Chenlin Meng, Stefano Ermon


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Improving Compositionality of Neural Networks by Decoding Representations to Inputs


Jun 01, 2021
Mike Wu, Noah Goodman, Stefano Ermon

* 9 pages content; 2 pages appendix 

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Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information


Apr 19, 2021
Willie Neiswanger, Ke Alexander Wang, Stefano Ermon


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On the Critical Role of Conventions in Adaptive Human-AI Collaboration


Apr 07, 2021
Andy Shih, Arjun Sawhney, Jovana Kondic, Stefano Ermon, Dorsa Sadigh

* 9th International Conference on Learning Representations (ICLR 2021) 

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Improved Autoregressive Modeling with Distribution Smoothing


Mar 28, 2021
Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon

* ICLR 2021 (Oral) 

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Anytime Sampling for Autoregressive Models via Ordered Autoencoding


Feb 23, 2021
Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon

* Accepted by ICLR 2021 

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Localized Calibration: Metrics and Recalibration


Feb 22, 2021
Rachel Luo, Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Silvio Savarese, Yu Bai, Shengjia Zhao, Stefano Ermon


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Neural Network Compression for Noisy Storage Devices


Feb 15, 2021
Berivan Isik, Kristy Choi, Xin Zheng, Tsachy Weissman, Stefano Ermon, H. -S. Philip Wong, Armin Alaghi

* 19 pages, 9 figures 

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Negative Data Augmentation


Feb 09, 2021
Abhishek Sinha, Kumar Ayush, Jiaming Song, Burak Uzkent, Hongxia Jin, Stefano Ermon

* Accepted at ICLR 2021 

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Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients


Jan 02, 2021
Chris Cundy, Stefano Ermon

* 8 pages; figure/table formatting fixed 

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PiRank: Learning To Rank via Differentiable Sorting


Dec 12, 2020
Robin Swezey, Aditya Grover, Bruno Charron, Stefano Ermon


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Efficient Conditional Pre-training for Transfer Learning


Dec 10, 2020
Shuvam Chakraborty, Burak Uzkent, Kumar Ayush, Kumar Tanmay, Evan Sheehan, Stefano Ermon


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Geography-Aware Self-Supervised Learning


Dec 02, 2020
Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David Lobell, Stefano Ermon


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Score-Based Generative Modeling through Stochastic Differential Equations


Nov 26, 2020
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole


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Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration


Nov 15, 2020
Shengjia Zhao, Stefano Ermon


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Autoregressive Score Matching


Oct 24, 2020
Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon

* NeurIPS 2020 

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