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ORCIDShengjia Zhao
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2020 – today
- 2024
[j1]Rachel Luo
, Shengjia Zhao, Jonathan Kuck, Boris Ivanovic, Silvio Savarese, Edward Schmerling, Marco Pavone:
Sample-efficient safety assurances using conformal prediction. Int. J. Robotics Res. 43(9): 1409-1424 (2024)- [c29]Rachel Luo, Rohan Sinha, Yixiao Sun, Ali Hindy, Shengjia Zhao, Silvio Savarese, Edward Schmerling, Marco Pavone:
Online Distribution Shift Detection via Recency Prediction. ICRA 2024: 16251-16263 - 2022
- [c28]Parikshit Gopalan, Michael P. Kim, Mihir Singhal, Shengjia Zhao:
Low-Degree Multicalibration. COLT 2022: 3193-3234 - [c27]Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon:
Comparing Distributions by Measuring Differences that Affect Decision Making. ICLR 2022 - [c26]Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon:
Modular Conformal Calibration. ICML 2022: 15180-15195 - [c25]Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon:
Generalizing Bayesian Optimization with Decision-theoretic Entropies. NeurIPS 2022 - [c24]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: 1286-1295 - [c23]Rachel Luo, Shengjia Zhao, Jonathan Kuck, Boris Ivanovic, Silvio Savarese, Edward Schmerling, Marco Pavone:
Sample-Efficient Safety Assurances Using Conformal Prediction. WAFR 2022: 149-169 - [i27]Parikshit Gopalan, Michael P. Kim, Mihir Singhal, Shengjia Zhao:
Low-Degree Multicalibration. CoRR abs/2203.01255 (2022) - [i26]Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon:
Modular Conformal Calibration. CoRR abs/2206.11468 (2022) - [i25]Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon:
Generalizing Bayesian Optimization with Decision-theoretic Entropies. CoRR abs/2210.01383 (2022) - [i24]Rachel Luo, Rohan Sinha, Ali Hindy, Shengjia Zhao, Silvio Savarese, Edward Schmerling, Marco Pavone:
Online Distribution Shift Detection via Recency Prediction. CoRR abs/2211.09916 (2022) - 2021
- [c22]Shengjia Zhao, Stefano Ermon:
Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration. AISTATS 2021: 2683-2691 - [c21]Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon:
Improved Autoregressive Modeling with Distribution Smoothing. ICLR 2021 - [c20]Roshni Sahoo, Shengjia Zhao, Alyssa Chen, Stefano Ermon:
Reliable Decisions with Threshold Calibration. NeurIPS 2021: 1831-1844 - [c19]Shengjia Zhao, Michael P. Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon:
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration. NeurIPS 2021: 22313-22324 - [i23]Rachel Luo, Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Silvio Savarese, Yu Bai, Shengjia Zhao, Stefano Ermon:
Localized Calibration: Metrics and Recalibration. CoRR abs/2102.10809 (2021) - [i22]Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon:
Improved Autoregressive Modeling with Distribution Smoothing. CoRR abs/2103.15089 (2021) - [i21]Shengjia Zhao, Michael P. Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon:
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration. CoRR abs/2107.05719 (2021) - [i20]Rachel Luo, Shengjia Zhao, Jonathan Kuck, Boris Ivanovic, Silvio Savarese, Edward Schmerling, Marco Pavone:
Sample-Efficient Safety Assurances using Conformal Prediction. CoRR abs/2109.14082 (2021) - 2020
- [c18]Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon:
Permutation Invariant Graph Generation via Score-Based Generative Modeling. AISTATS 2020: 4474-4484 - [c17]Shengjia Zhao, Christopher Yeh, Stefano Ermon:
A Framework for Sample Efficient Interval Estimation with Control Variates. AISTATS 2020: 4583-4592 - [c16]Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon:
A Theory of Usable Information under Computational Constraints. ICLR 2020 - [c15]Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon:
Domain Adaptive Imitation Learning. ICML 2020: 5286-5295 - [c14]Shengjia Zhao, Tengyu Ma, Stefano Ermon:
Individual Calibration with Randomized Forecasting. ICML 2020: 11387-11397 - [i19]Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon:
A Theory of Usable Information Under Computational Constraints. CoRR abs/2002.10689 (2020) - [i18]Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon:
Permutation Invariant Graph Generation via Score-Based Generative Modeling. CoRR abs/2003.00638 (2020) - [i17]Shengjia Zhao, Christopher Yeh, Stefano Ermon:
A Framework for Sample Efficient Interval Estimation with Control Variates. CoRR abs/2006.10287 (2020) - [i16]Shengjia Zhao, Tengyu Ma, Stefano Ermon:
Individual Calibration with Randomized Forecasting. CoRR abs/2006.10288 (2020) - [i15]Rachel Luo, Shengjia Zhao, Jiaming Song, Jonathan Kuck, Stefano Ermon, Silvio Savarese:
Privacy Preserving Recalibration under Domain Shift. CoRR abs/2008.09643 (2020) - [i14]Shengjia Zhao, Stefano Ermon:
Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration. CoRR abs/2011.07476 (2020)
2010 – 2019
- 2019
- [c13]Shengjia Zhao, Jiaming Song, Stefano Ermon:
InfoVAE: Balancing Learning and Inference in Variational Autoencoders. AAAI 2019: 5885-5892 - [c12]Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon:
Learning Controllable Fair Representations. AISTATS 2019: 2164-2173 - [c11]Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon:
Learning Neural PDE Solvers with Convergence Guarantees. ICLR (Poster) 2019 - [c10]Hongyu Ren, Shengjia Zhao, Stefano Ermon:
Adaptive Antithetic Sampling for Variance Reduction. ICML 2019: 5420-5428 - [i13]Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon:
Learning Neural PDE Solvers with Convergence Guarantees. CoRR abs/1906.01200 (2019) - [i12]Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon:
Cross Domain Imitation Learning. CoRR abs/1910.00105 (2019) - [i11]Y. Alex Kolchinski, Sharon Zhou, Shengjia Zhao, Mitchell L. Gordon, Stefano Ermon:
Approximating Human Judgment of Generated Image Quality. CoRR abs/1912.12121 (2019) - 2018
- [c9]Rui Shu, Shengjia Zhao, Mykel J. Kochenderfer:
Rethinking Style and Content Disentanglement in Variational Autoencoders. ICLR (Workshop) 2018 - [c8]Rui Shu, Hung H. Bui, Shengjia Zhao, Mykel J. Kochenderfer, Stefano Ermon:
Amortized Inference Regularization. NeurIPS 2018: 4398-4407 - [c7]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: 10815-10824 - [c6]Shengjia Zhao, Jiaming Song, Stefano Ermon:
A Lagrangian Perspective on Latent Variable Generative Models. UAI 2018: 1031-1041 - [i10]Rui Shu, Hung H. Bui, Shengjia Zhao, Mykel J. Kochenderfer, Stefano Ermon:
Amortized Inference Regularization. CoRR abs/1805.08913 (2018) - [i9]Shengjia Zhao, Jiaming Song, Stefano Ermon:
The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models. CoRR abs/1806.06514 (2018) - [i8]Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah D. Goodman, Stefano Ermon:
Bias and Generalization in Deep Generative Models: An Empirical Study. CoRR abs/1811.03259 (2018) - [i7]Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon:
Learning Controllable Fair Representations. CoRR abs/1812.04218 (2018) - 2017
- [c5]Jiaming Song, Shengjia Zhao, Stefano Ermon:
Generative Adversarial Learning of Markov Chains. ICLR (Workshop) 2017 - [c4]Shengjia Zhao, Jiaming Song, Stefano Ermon:
Learning Hierarchical Features from Deep Generative Models. ICML 2017: 4091-4099 - [c3]Jiaming Song, Shengjia Zhao, Stefano Ermon:
A-NICE-MC: Adversarial Training for MCMC. NIPS 2017: 5140-5150 - [i6]Shengjia Zhao, Jiaming Song, Stefano Ermon:
Learning Hierarchical Features from Generative Models. CoRR abs/1702.08396 (2017) - [i5]Shengjia Zhao, Jiaming Song, Stefano Ermon:
Towards Deeper Understanding of Variational Autoencoding Models. CoRR abs/1702.08658 (2017) - [i4]Jiaming Song, Russell Stewart, Shengjia Zhao, Stefano Ermon:
On the Limits of Learning Representations with Label-Based Supervision. CoRR abs/1703.02156 (2017) - [i3]Shengjia Zhao, Jiaming Song, Stefano Ermon:
InfoVAE: Information Maximizing Variational Autoencoders. CoRR abs/1706.02262 (2017) - [i2]Jiaming Song, Shengjia Zhao, Stefano Ermon:
A-NICE-MC: Adversarial Training for MCMC. CoRR abs/1706.07561 (2017) - 2016
- [c2]Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, Stefano Ermon:
Closing the Gap Between Short and Long XORs for Model Counting. AAAI 2016: 3322-3329 - [c1]Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon:
Adaptive Concentration Inequalities for Sequential Decision Problems. NIPS 2016: 1343-1351 - 2015
- [i1]Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, Stefano Ermon:
Closing the Gap Between Short and Long XORs for Model Counting. CoRR abs/1512.08863 (2015)
Coauthor Index
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