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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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Imitation with Neural Density Models

Oct 19, 2020
Kuno Kim, Akshat Jindal, Yang Song, Jiaming Song, Yanan Sui, Stefano Ermon


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Denoising Diffusion Implicit Models

Oct 06, 2020
Jiaming Song, Chenlin Meng, Stefano Ermon


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Privacy Preserving Recalibration under Domain Shift

Aug 21, 2020
Rachel Luo, Shengjia Zhao, Jiaming Song, Jonathan Kuck, Stefano Ermon, Silvio Savarese


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Multi-label Contrastive Predictive Coding

Jul 20, 2020
Jiaming Song, Stefano Ermon


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Belief Propagation Neural Networks

Jul 01, 2020
Jonathan Kuck, Shuvam Chakraborty, Hao Tang, Rachel Luo, Jiaming Song, Ashish Sabharwal, Stefano Ermon


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Experience Replay with Likelihood-free Importance Weights

Jun 23, 2020
Samarth Sinha, Jiaming Song, Animesh Garg, Stefano Ermon


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Robust and On-the-fly Dataset Denoising for Image Classification

Apr 09, 2020
Jiaming Song, Lunjia Hu, Michael Auli, Yann Dauphin, Tengyu Ma


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Training Deep Energy-Based Models with f-Divergence Minimization

Mar 06, 2020
Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon


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Gaussianization Flows

Mar 04, 2020
Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon

* AISTATS 2020 

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Permutation Invariant Graph Generation via Score-Based Generative Modeling

Mar 02, 2020
Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon

* 14 pages, AISTATS 2020 

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A Theory of Usable Information Under Computational Constraints

Feb 25, 2020
Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon

* ICLR 2020 (Talk) 

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Bridging the Gap Between $f$-GANs and Wasserstein GANs

Oct 22, 2019
Jiaming Song, Stefano Ermon


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Unsupervised Out-of-Distribution Detection with Batch Normalization

Oct 21, 2019
Jiaming Song, Yang Song, Stefano Ermon


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Understanding the Limitations of Variational Mutual Information Estimators

Oct 14, 2019
Jiaming Song, Stefano Ermon


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Cross Domain Imitation Learning

Sep 30, 2019
Kun Ho Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon


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Multi-Agent Adversarial Inverse Reinforcement Learning

Jul 30, 2019
Lantao Yu, Jiaming Song, Stefano Ermon

* ICML 2019 

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Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting

Jun 23, 2019
Aditya Grover, Jiaming Song, Alekh Agarwal, Kenneth Tran, Ashish Kapoor, Eric Horvitz, Stefano Ermon


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Calibrated Model-Based Deep Reinforcement Learning

Jun 19, 2019
Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon

* Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4314-4323, 2019 

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Learning Controllable Fair Representations

Dec 11, 2018
Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon


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Bias and Generalization in Deep Generative Models: An Empirical Study

Nov 08, 2018
Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah Goodman, Stefano Ermon


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Multi-Agent Generative Adversarial Imitation Learning

Jul 26, 2018
Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon


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The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models

Jul 08, 2018
Shengjia Zhao, Jiaming Song, Stefano Ermon


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Accelerating Natural Gradient with Higher-Order Invariance

Jun 07, 2018
Yang Song, Jiaming Song, Stefano Ermon

* ICML 2018 

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Adversarial Constraint Learning for Structured Prediction

May 31, 2018
Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon

* To appear at IJCAI 2018 

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InfoVAE: Information Maximizing Variational Autoencoders

May 30, 2018
Shengjia Zhao, Jiaming Song, Stefano Ermon


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A-NICE-MC: Adversarial Training for MCMC

Mar 14, 2018
Jiaming Song, Shengjia Zhao, Stefano Ermon

* NIPS 2017 

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An Empirical Analysis of Proximal Policy Optimization with Kronecker-factored Natural Gradients

Jan 17, 2018
Jiaming Song, Yuhuai Wu


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InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations

Nov 14, 2017
Yunzhu Li, Jiaming Song, Stefano Ermon

* 14 pages, NIPS 2017 

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