About Me
I am a 5-th year PhD student at Stanford Computer Science advised by Stefano Ermon. Before coming to Stanford, I completed my undergrad at Tsinghua University.
Research Interests
(Ranked by the amount of time I spend thinking about each topic, as of Jan 2021)
- Individual risk, interpretations for probability and forecast, calibration
- Variational definitions of information and machine learning quantities
- Deep generative models, high dimensional distribution modeling, representation learning
- Active learning and online learning
- Information theory
Contact: sjzhao at stanford dot edu
Publications by Topics
Uncertainty Quantification and Trustworthy Machine Learning
Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration
Shengjia Zhao, Stefano Ermon [arXiv] (AISTATS’2021 Oral)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)
Information Theory and Decision Theory
H-divergence: A Decision-Theoretic Probability Discrepancy Measure
Shengjia Zhao*, Abhishek Sinha*, Yutong He*, Aidan Perreault, Jiaming Song, Stefano ErmonA Theory of Usable Information under Computational Constraints
Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon [arXiv] (ICLR’2020 Oral)
Generative Models
Improved Autoregressive Modeling with Distribution Smoothing
Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon (ICLR’2021 Oral) 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)A Lagrangian Perspective on Latent Variable Generative Models
Shengjia Zhao, Jiaming Song, Stefano Ermon [arXiv] (UAI’2018 Oral)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)Adaptive Concentration Inequalities for Sequential Decision Problems
Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon [pdf] (NeurIPS’2016)Closing the Gap Between Short and Long XORs for Model Counting
Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, Stefano Ermon [arXiv] (AAAI’2016)
Awards and Fellowships
- JP Morgan PhD Fellowship (2019)
- Qualcomm Innovation Fellowship (QInF) (2018)
- Qualcomm Scholarship (2016)
- Google Excellence Scholarship (2015)
Teaching and Services
- Reviewer: NeurIPS (2017, 2019, 2020), ICLR (2019, 2020, 2021), ICML (2019, 2020)
- Organizer: Information Theory and Machine Learning (ITML) Workshop (NeurIPS’2019)
- Teaching: CS228 Head TA (2019 and 2021)