Shengjia Zhao

According to our database, Shengjia Zhao authored at least 38 papers between 2016 and 2022.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2022

Online Distribution Shift Detection via Recency Prediction.

Rachel Luo, Rohan Sinha, Ali Hindy, Shengjia Zhao, Silvio Savarese, Edward Schmerling, Marco Pavone

CoRR, 2022

Sample-Efficient Safety Assurances Using Conformal Prediction.

Rachel Luo, Shengjia Zhao, Jonathan Kuck, Boris Ivanovic, Silvio Savarese, Edward Schmerling, Marco Pavone

Proceedings of the Algorithmic Foundations of Robotics XV, 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

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Generalizing Bayesian Optimization with Decision-theoretic Entropies.

Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Modular Conformal Calibration.

Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon

Proceedings of the International Conference on Machine Learning, 2022

Comparing Distributions by Measuring Differences that Affect Decision Making.

Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon

Proceedings of the Tenth International Conference on Learning Representations, 2022

Low-Degree Multicalibration.

Parikshit Gopalan, Michael P. Kim, Mihir Singhal, Shengjia Zhao

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

Localized Calibration: Metrics and Recalibration.

Rachel Luo, Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Silvio Savarese, Yu Bai, Shengjia Zhao, Stefano Ermon

CoRR, 2021

Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration.

Shengjia Zhao, Michael P. Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Reliable Decisions with Threshold Calibration.

Roshni Sahoo, Shengjia Zhao, Alyssa Chen, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Improved Autoregressive Modeling with Distribution Smoothing.

Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon

Proceedings of the 9th International Conference on Learning Representations, 2021

Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration.

Shengjia Zhao, Stefano Ermon

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Privacy Preserving Recalibration under Domain Shift.

Rachel Luo, Shengjia Zhao, Jiaming Song, Jonathan Kuck, Stefano Ermon, Silvio Savarese

CoRR, 2020

Individual Calibration with Randomized Forecasting.

Shengjia Zhao, Tengyu Ma, Stefano Ermon

Proceedings of the 37th International Conference on Machine Learning, 2020

Domain Adaptive Imitation Learning.

Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon

Proceedings of the 37th International Conference on Machine Learning, 2020

A Theory of Usable Information under Computational Constraints.

Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon

Proceedings of the 8th International Conference on Learning Representations, 2020

A Framework for Sample Efficient Interval Estimation with Control Variates.

Shengjia Zhao, Christopher Yeh, Stefano Ermon

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Permutation Invariant Graph Generation via Score-Based Generative Modeling.

Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Approximating Human Judgment of Generated Image Quality.

Y. Alex Kolchinski, Sharon Zhou, Shengjia Zhao, Mitchell L. Gordon, Stefano Ermon

CoRR, 2019

Cross Domain Imitation Learning.

Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon

CoRR, 2019

Adaptive Antithetic Sampling for Variance Reduction.

Hongyu Ren, Shengjia Zhao, Stefano Ermon

Proceedings of the 36th International Conference on Machine Learning, 2019

Learning Neural PDE Solvers with Convergence Guarantees.

Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon

Proceedings of the 7th International Conference on Learning Representations, 2019

Learning Controllable Fair Representations.

Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

InfoVAE: Balancing Learning and Inference in Variational Autoencoders.

Shengjia Zhao, Jiaming Song, Stefano Ermon

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models.

Shengjia Zhao, Jiaming Song, Stefano Ermon

CoRR, 2018

A Lagrangian Perspective on Latent Variable Generative Models.

Shengjia Zhao, Jiaming Song, Stefano Ermon

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Bias and Generalization in Deep Generative Models: An Empirical Study.

Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah D. Goodman, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Amortized Inference Regularization.

Rui Shu, Hung H. Bui, Shengjia Zhao, Mykel J. Kochenderfer, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Rethinking Style and Content Disentanglement in Variational Autoencoders.

Rui Shu, Shengjia Zhao, Mykel J. Kochenderfer

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

InfoVAE: Information Maximizing Variational Autoencoders.

Shengjia Zhao, Jiaming Song, Stefano Ermon

CoRR, 2017

Towards Deeper Understanding of Variational Autoencoding Models.

Shengjia Zhao, Jiaming Song, Stefano Ermon

CoRR, 2017

Learning Hierarchical Features from Generative Models.

Shengjia Zhao, Jiaming Song, Stefano Ermon

CoRR, 2017

On the Limits of Learning Representations with Label-Based Supervision.

Jiaming Song, Russell Stewart, Shengjia Zhao, Stefano Ermon

CoRR, 2017

A-NICE-MC: Adversarial Training for MCMC.

Jiaming Song, Shengjia Zhao, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Learning Hierarchical Features from Deep Generative Models.

Shengjia Zhao, Jiaming Song, Stefano Ermon

Proceedings of the 34th International Conference on Machine Learning, 2017

Generative Adversarial Learning of Markov Chains.

Jiaming Song, Shengjia Zhao, Stefano Ermon

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Adaptive Concentration Inequalities for Sequential Decision Problems.

Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Closing the Gap Between Short and Long XORs for Model Counting.

Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, Stefano Ermon

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016