Zhao, Shengjia

27 publications

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

COLT 2022 Low-Degree Multicalibration Parikshit Gopalan, Michael P Kim, Mihir A Singhal, Shengjia Zhao

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

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

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

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

ICML 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, Stefano Ermon

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

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

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

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

NeurIPS 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 Goodman, Stefano Ermon

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

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

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

NeurIPS 2016 Adaptive Concentration Inequalities for Sequential Decision Problems Shengjia Zhao, Enze Zhou, 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