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

Adaptive Concentration Inequalities for Sequential Decision Problems. - Shengjia Zhao; Enze Zhou; Ashish Sabharwal; Stefano Ermon (NIPS, 2016)

Generative Adversarial Learning of Markov Chains. - Jiaming Song; Shengjia Zhao; Stefano Ermon (ICLR (Workshop), 2017)

Learning Hierarchical Features from Deep Generative Models. - Shengjia Zhao; Jiaming Song; Stefano Ermon (ICML, 2017)

A-NICE-MC: Adversarial Training for MCMC. - Jiaming Song; Shengjia Zhao; Stefano Ermon (NIPS, 2017)

On the Limits of Learning Representations with Label-Based Supervision. - Jiaming Song; Russell Stewart; Shengjia Zhao; Stefano Ermon (CoRR, 2017)

Learning Hierarchical Features from Generative Models. - Shengjia Zhao; Jiaming Song; Stefano Ermon (CoRR, 2017)

Towards Deeper Understanding of Variational Autoencoding Models. - Shengjia Zhao; Jiaming Song; Stefano Ermon (CoRR, 2017)

InfoVAE: Information Maximizing Variational Autoencoders. - Shengjia Zhao; Jiaming Song; Stefano Ermon (CoRR, 2017)

Rethinking Style and Content Disentanglement in Variational Autoencoders. - Rui Shu; Shengjia Zhao; Mykel J. Kochenderfer (ICLR (Workshop), 2018)

Amortized Inference Regularization. - Rui Shu; Hung H. Bui; Shengjia Zhao; Mykel J. Kochenderfer; Stefano Ermon (NeurIPS, 2018)

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

A Lagrangian Perspective on Latent Variable Generative Models. - Shengjia Zhao; Jiaming Song; Stefano Ermon (UAI, 2018)

The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models. - Shengjia Zhao; Jiaming Song; Stefano Ermon (CoRR, 2018)

InfoVAE: Balancing Learning and Inference in Variational Autoencoders. - Shengjia Zhao; Jiaming Song; Stefano Ermon (AAAI, 2019)

Learning Controllable Fair Representations. - Jiaming Song; Pratyusha Kalluri; Aditya Grover; Shengjia Zhao; Stefano Ermon (AISTATS, 2019)

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

Adaptive Antithetic Sampling for Variance Reduction. - Hongyu Ren; Shengjia Zhao; Stefano Ermon (ICML, 2019)

Cross Domain Imitation Learning. - Kuno Kim; Yihong Gu; Jiaming Song; Shengjia Zhao; Stefano Ermon (CoRR, 2019)

Approximating Human Judgment of Generated Image Quality. - Y. Alex Kolchinski; Sharon Zhou; Shengjia Zhao; Mitchell L. Gordon; Stefano Ermon (CoRR, 2019)

Permutation Invariant Graph Generation via Score-Based Generative Modeling. - Chenhao Niu; Yang Song 011; Jiaming Song; Shengjia Zhao; Aditya Grover; Stefano Ermon (AISTATS, 2020)

A Framework for Sample Efficient Interval Estimation with Control Variates. - Shengjia Zhao; Christopher Yeh; Stefano Ermon (AISTATS, 2020)

A Theory of Usable Information under Computational Constraints. - Yilun Xu; Shengjia Zhao; Jiaming Song; Russell Stewart; Stefano Ermon (ICLR, 2020)

Domain Adaptive Imitation Learning. - Kuno Kim; Yihong Gu; Jiaming Song; Shengjia Zhao; Stefano Ermon (ICML, 2020)

Individual Calibration with Randomized Forecasting. - Shengjia Zhao; Tengyu Ma 001; Stefano Ermon (ICML, 2020)

Privacy Preserving Recalibration under Domain Shift. - Rachel Luo; Shengjia Zhao; Jiaming Song; Jonathan Kuck; Stefano Ermon; Silvio Savarese (CoRR, 2020)

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

Improved Autoregressive Modeling with Distribution Smoothing. - Chenlin Meng; Jiaming Song; Yang Song 011; Shengjia Zhao; Stefano Ermon (ICLR, 2021)

Reliable Decisions with Threshold Calibration. - Roshni Sahoo; Shengjia Zhao; Alyssa Chen; Stefano Ermon (NeurIPS, 2021)

Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration. - Shengjia Zhao; Michael P. Kim; Roshni Sahoo; Tengyu Ma 001; Stefano Ermon (NeurIPS, 2021)

Localized Calibration: Metrics and Recalibration. - Rachel Luo; Aadyot Bhatnagar; Huan Wang 014; Caiming Xiong; Silvio Savarese; Yu Bai 017; Shengjia Zhao; Stefano Ermon (CoRR, 2021)

Low-Degree Multicalibration. - Parikshit Gopalan; Michael P. Kim; Mihir Singhal; Shengjia Zhao (COLT, 2022)

Comparing Distributions by Measuring Differences that Affect Decision Making. - Shengjia Zhao; Abhishek Sinha; Yutong He; Aidan Perreault; Jiaming Song; Stefano Ermon (ICLR, 2022)

Modular Conformal Calibration. - Charles Marx; Shengjia Zhao; Willie Neiswanger; Stefano Ermon (ICML, 2022)

Generalizing Bayesian Optimization with Decision-theoretic Entropies. - Willie Neiswanger; Lantao Yu; Shengjia Zhao; Chenlin Meng; Stefano Ermon (NeurIPS, 2022)

Local calibration: metrics and recalibration. - Rachel Luo; Aadyot Bhatnagar; Yu Bai 017; Shengjia Zhao; Huan Wang 014; Caiming Xiong; Silvio Savarese; Stefano Ermon; Edward Schmerling; Marco Pavone 001 (UAI, 2022)

Online Distribution Shift Detection via Recency Prediction. - Rachel Luo; Rohan Sinha; Ali Hindy; Shengjia Zhao; Silvio Savarese; Edward Schmerling; Marco Pavone 001 (CoRR, 2022)

Online Distribution Shift Detection via Recency Prediction. - Rachel Luo; Rohan Sinha; Yixiao Sun; Ali Hindy; Shengjia Zhao; Silvio Savarese; Edward Schmerling; Marco Pavone 001 (ICRA, 2024)

Sample-efficient safety assurances using conformal prediction. - Rachel Luo; Shengjia Zhao; Jonathan Kuck; Boris Ivanovic; Silvio Savarese; Edward Schmerling; Marco Pavone 001 (Int. J. Robotics Res., 2024)