Publications
Uncertainty Quantification and Decision Making under Uncertainty
Modular Conformal Calibration
Shengjia Zhao*, Charles Marx*, Willie Neiswanger, Stefano Ermon (ICML’22)Low-Degree Multicalibration
Parikshit Gopalan, Michael Kim, Mihir Singhal, Shengjia Zhao (COLT’22)Sample-Efficient Safety Assurances using Conformal Prediction
Rachel Luo, Shengjia Zhao, Jonathan Kuck, Boris Ivanovic, Silvio Savarese, Edward Schmerling, Marco Pavone (WAFR’22)Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration
Shengjia Zhao, Michael P Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon [arXiv] (Neurips’2021)Reliable Decisions with Threshold Calibration
Roshni Sahoo, Shengjia Zhao, Alyssa Chen, Stefano Ermon (Neurips’2021)Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration
Shengjia Zhao, Stefano Ermon [arXiv] (AISTATS’2021 Oral 3.1%)Individual Calibration with Randomized Forecast
Shengjia Zhao, Tengyu Ma, Stefano Ermon [arXiv] (ICML’2020)A framework for Sample Efficient Interval Estimation with Control Variates
Shengjia Zhao, Christopher Yeh, Stefano Ermon [arXiv] (AISTATS’2020)Adaptive Concentration Inequalities for Sequential Decision Problems
Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon [pdf] (NeurIPS’2016)
Information Theory and Decision Theory
Comparing Distributions by Measuring Differences that Affect Decision Making
Shengjia Zhao*, Abhishek Sinha*, Yutong He*, Aidan Perreault, Jiaming Song, Stefano Ermon [openreview] (ICLR’22 Outstanding paper award 0.15%)Generalizing Bayesian Optimization with a Decision-theoretic Uncertainty Measure
Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon [In submission]A Theory of Usable Information under Computational Constraints
Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon [arXiv] (ICLR’2020 Oral 1.9%)
Generative Models
Improved Autoregressive Modeling with Distribution Smoothing
Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon (ICLR’2021 Oral 1.8%) openreviewPermutation Invariant Graph Generation via Score-Based Generative Modeling
Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon [arXiv] (AISTATS’2020)InfoVAE: Balancing Learning and Inference in Variational Autoencoders
Shengjia Zhao, Jiaming Song, Stefano Ermon [arXiv] (AAAI’2019)Bias and Generalization in Deep Generative Models: An Empirical Study
Shengjia Zhao*, Hongyu Ren*, Arianna Yuan, Jiaming Song, Noah Goodman, Stefano Ermon [arXiv] (NeurIPS’2018 Spotlight 3%)A Lagrangian Perspective on Latent Variable Generative Models
Shengjia Zhao, Jiaming Song, Stefano Ermon [arXiv] (UAI’2018 Oral 8.6%)Learning Hierarchical Features from Generative Models
Shengjia Zhao, Jiaming Song, Stefano Ermon [arXiv] (ICML’2017)
Improving Classical Algorithms with Learning
Adaptive Antithetic Sampling for Variance Reduction
Hongyu Ren*, Shengjia Zhao*, Stefano Ermon [paper] (ICML’2019)Learning Neural PDE Solvers with Convergence Guarantees
Jun-Ting Hsieh*, Shengjia Zhao*, Lucia Mirabella, Stefano Ermon [arXiv] (ICLR’2019)A-NICE-MC: Adversarial Training for MCMC
Jiaming Song, Shengjia Zhao, Stefano Ermon [arXiv] [code] (NeurIPS’2017)
Miscellaneous Topics
Privacy Preserving Recalibration under Domain Shift
Rachel Luo, Shengjia Zhao, Jiaming Song, Jonathan Kuck, Stefano Ermon, Silvio Savarese [arXiv]Cross domain imitation learning
Kun Ho Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon [arXiv] (ICML’2020)Adaptive hashing for model counting
Jonathan Kuck, Tri Dao, Shengjia Zhao, Burak Burtan, Ashish Sabharwal, Stefano Ermon [paper] (UAI’2020)Learning Controllable Fair Representations
Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon [paper] (AISTATS’2019)Amortized Inference Regularization
Rui Shu, Hung H Bai, Shengjia Zhao, Stefano Ermon [arXiv] (NeurIPS’2018)Closing the Gap Between Short and Long XORs for Model Counting
Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, Stefano Ermon [arXiv] (AAAI’2016)