Shengjia Zhao

Publication Activity (10 Years)

Years Active: 2015-2022
Publications (10 Years): 54

Top Topics

Generative Model

Imitation Learning

Interval Estimation

Top Venues

Publications

Parikshit Gopalan, Michael P. Kim, Mihir Singhal, Shengjia Zhao
Low-Degree Multicalibration. CoRR (2022)
Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon
Modular Conformal Calibration. ICML (2022)
Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon
Generalizing Bayesian Optimization with Decision-theoretic Entropies. NeurIPS (2022)
Rachel Luo, Aadyot Bhatnagar, Yu Bai, Shengjia Zhao, Huan Wang, Caiming Xiong, Silvio Savarese, Stefano Ermon, Edward Schmerling, Marco Pavone
Local calibration: metrics and recalibration. UAI (2022)
Rachel Luo, Shengjia Zhao, Jonathan Kuck, Boris Ivanovic, Silvio Savarese, Edward Schmerling, Marco Pavone
Sample-Efficient Safety Assurances Using Conformal Prediction. WAFR (2022)
Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon
Comparing Distributions by Measuring Differences that Affect Decision Making. ICLR (2022)
Rachel Luo, Rohan Sinha, Ali Hindy, Shengjia Zhao, Silvio Savarese, Edward Schmerling, Marco Pavone
Online Distribution Shift Detection via Recency Prediction. CoRR (2022)
Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon
Generalizing Bayesian Optimization with Decision-theoretic Entropies. CoRR (2022)
Parikshit Gopalan, Michael P. Kim, Mihir Singhal, Shengjia Zhao
Low-Degree Multicalibration. COLT (2022)
Shengjia Zhao, Michael P. Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration. CoRR (2021)
Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon
Improved Autoregressive Modeling with Distribution Smoothing. ICLR (2021)
Rachel Luo, Shengjia Zhao, Jonathan Kuck, Boris Ivanovic, Silvio Savarese, Edward Schmerling, Marco Pavone
Sample-Efficient Safety Assurances using Conformal Prediction. CoRR (2021)
Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon
Improved Autoregressive Modeling with Distribution Smoothing. CoRR (2021)
Shengjia Zhao, Stefano Ermon
Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration. AISTATS (2021)
Roshni Sahoo, Shengjia Zhao, Alyssa Chen, Stefano Ermon
Reliable Decisions with Threshold Calibration. NeurIPS (2021)
Shengjia Zhao, Michael P. Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration. NeurIPS (2021)
Rachel Luo, Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Silvio Savarese, Yu Bai, Shengjia Zhao, Stefano Ermon
Localized Calibration: Metrics and Recalibration. CoRR (2021)
Rachel Luo, Shengjia Zhao, Jiaming Song, Jonathan Kuck, Stefano Ermon, Silvio Savarese
Privacy Preserving Recalibration under Domain Shift. CoRR (2020)
Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon
A Theory of Usable Information Under Computational Constraints. CoRR (2020)
Shengjia Zhao, Christopher Yeh, Stefano Ermon
A Framework for Sample Efficient Interval Estimation with Control Variates. CoRR (2020)
Shengjia Zhao, Christopher Yeh, Stefano Ermon
A Framework for Sample Efficient Interval Estimation with Control Variates. AISTATS (2020)
Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon
Permutation Invariant Graph Generation via Score-Based Generative Modeling. CoRR (2020)
Shengjia Zhao, Tengyu Ma, Stefano Ermon
Individual Calibration with Randomized Forecasting. ICML (2020)
Shengjia Zhao, Stefano Ermon
Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration. CoRR (2020)
Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon
Permutation Invariant Graph Generation via Score-Based Generative Modeling. AISTATS (2020)
Shengjia Zhao, Tengyu Ma, Stefano Ermon
Individual Calibration with Randomized Forecasting. CoRR (2020)
Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon
A Theory of Usable Information under Computational Constraints. ICLR (2020)
Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon
Domain Adaptive Imitation Learning. ICML (2020)
Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon
Learning Neural PDE Solvers with Convergence Guarantees. ICLR (Poster) (2019)
Kun Ho Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon
Cross Domain Imitation Learning. CoRR (2019)
Y. Alex Kolchinski, Sharon Zhou, Shengjia Zhao, Mitchell L. Gordon, Stefano Ermon
Approximating Human Judgment of Generated Image Quality. CoRR (2019)
Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon
Learning Neural PDE Solvers with Convergence Guarantees. CoRR (2019)
Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon
Learning Controllable Fair Representations. AISTATS (2019)
Shengjia Zhao, Jiaming Song, Stefano Ermon
InfoVAE: Balancing Learning and Inference in Variational Autoencoders. AAAI (2019)
Hongyu Ren, Shengjia Zhao, Stefano Ermon
Adaptive Antithetic Sampling for Variance Reduction. ICML (2019)
Rui Shu, Hung H. Bui, Shengjia Zhao, Mykel J. Kochenderfer, Stefano Ermon
Amortized Inference Regularization. CoRR (2018)
Rui Shu, Hung H. Bui, Shengjia Zhao, Mykel J. Kochenderfer, Stefano Ermon
Amortized Inference Regularization. NeurIPS (2018)
Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah D. Goodman, Stefano Ermon
Bias and Generalization in Deep Generative Models: An Empirical Study. NeurIPS (2018)
Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon
Learning Controllable Fair Representations. CoRR (2018)
Shengjia Zhao, Jiaming Song, Stefano Ermon
The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models. CoRR (2018)
Shengjia Zhao, Jiaming Song, Stefano Ermon
A Lagrangian Perspective on Latent Variable Generative Models. UAI (2018)
Rui Shu, Shengjia Zhao, Mykel J. Kochenderfer
Rethinking Style and Content Disentanglement in Variational Autoencoders. ICLR (Workshop) (2018)
Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah D. Goodman, Stefano Ermon
Bias and Generalization in Deep Generative Models: An Empirical Study. CoRR (2018)
Shengjia Zhao, Jiaming Song, Stefano Ermon
InfoVAE: Information Maximizing Variational Autoencoders. CoRR (2017)
Shengjia Zhao, Jiaming Song, Stefano Ermon
Learning Hierarchical Features from Deep Generative Models. ICML (2017)
Jiaming Song, Shengjia Zhao, Stefano Ermon
A-NICE-MC: Adversarial Training for MCMC. NIPS (2017)
Jiaming Song, Russell Stewart, Shengjia Zhao, Stefano Ermon
On the Limits of Learning Representations with Label-Based Supervision. CoRR (2017)
Shengjia Zhao, Jiaming Song, Stefano Ermon
Learning Hierarchical Features from Generative Models. CoRR (2017)
Jiaming Song, Shengjia Zhao, Stefano Ermon
A-NICE-MC: Adversarial Training for MCMC. CoRR (2017)
Shengjia Zhao, Jiaming Song, Stefano Ermon
Towards Deeper Understanding of Variational Autoencoding Models. CoRR (2017)
Jiaming Song, Shengjia Zhao, Stefano Ermon
Generative Adversarial Learning of Markov Chains. ICLR (Workshop) (2017)
Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon
Adaptive Concentration Inequalities for Sequential Decision Problems. NIPS (2016)
Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, Stefano Ermon
Closing the Gap Between Short and Long XORs for Model Counting. AAAI (2016)
Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, Stefano Ermon
Closing the Gap Between Short and Long XORs for Model Counting. CoRR (2015)