Shengjia Zhao - RankMe

58 papers A* 18A 6C 1Journal 28Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	Multi-dimensional Neural Decoding with Orthogonal Representations for Brain-Computer Interfaces. AAAI Kaixi Tian, Shengjia Zhao, Yuhan Zhang, Shan Yu
2024	A*	`conf`	Online Distribution Shift Detection via Recency Prediction. ICRA Rachel Luo, Rohan Sinha, Yixiao Sun, Ali Hindy, Shengjia Zhao, Silvio Savarese, Edward Schmerling, Marco Pavone
2024	J	`jnl`	Sample-efficient safety assurances using conformal prediction. Int. J. Robotics Res. Rachel Luo, Shengjia Zhao, Jonathan Kuck, Boris Ivanovic, Silvio Savarese, Edward Schmerling, Marco Pavone
2022	A*	`conf`	Comparing Distributions by Measuring Differences that Affect Decision Making. ICLR Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon
2022	A*	`conf`	Generalizing Bayesian Optimization with Decision-theoretic Entropies. NeurIPS Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon
2022	J	`jnl`	Generalizing Bayesian Optimization with Decision-theoretic Entropies. CoRR Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon
2022	A	`conf`	Local calibration: metrics and recalibration. UAI Rachel Luo, Aadyot Bhatnagar, Yu Bai, Shengjia Zhao, Huan Wang, Caiming Xiong, Silvio Savarese, Stefano Ermon, Edward Schmerling, Marco Pavone
2022	A*	`conf`	Low-Degree Multicalibration. COLT Parikshit Gopalan, Michael P. Kim, Mihir Singhal, Shengjia Zhao
2022	J	`jnl`	Low-Degree Multicalibration. CoRR Parikshit Gopalan, Michael P. Kim, Mihir Singhal, Shengjia Zhao
2022	A*	`conf`	Modular Conformal Calibration. ICML Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon
2022	J	`jnl`	Modular Conformal Calibration. CoRR Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon
2022	J	`jnl`	Online Distribution Shift Detection via Recency Prediction. CoRR Rachel Luo, Rohan Sinha, Ali Hindy, Shengjia Zhao, Silvio Savarese, Edward Schmerling, Marco Pavone
2022	C	`conf`	Sample-Efficient Safety Assurances Using Conformal Prediction. WAFR Rachel Luo, Shengjia Zhao, Jonathan Kuck, Boris Ivanovic, Silvio Savarese, Edward Schmerling, Marco Pavone
2021	A*	`conf`	Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration. NeurIPS Shengjia Zhao, Michael P. Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon
2021	J	`jnl`	Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration. CoRR Shengjia Zhao, Michael P. Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon
2021	A*	`conf`	Improved Autoregressive Modeling with Distribution Smoothing. ICLR Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon
2021	J	`jnl`	Improved Autoregressive Modeling with Distribution Smoothing. CoRR Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon
2021	J	`jnl`	Localized Calibration: Metrics and Recalibration. CoRR Rachel Luo, Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Silvio Savarese, Yu Bai, Shengjia Zhao, Stefano Ermon
2021	A*	`conf`	Reliable Decisions with Threshold Calibration. NeurIPS Roshni Sahoo, Shengjia Zhao, Alyssa Chen, Stefano Ermon
2021	A	`conf`	Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration. AISTATS Shengjia Zhao, Stefano Ermon
2021	J	`jnl`	Sample-Efficient Safety Assurances using Conformal Prediction. CoRR Rachel Luo, Shengjia Zhao, Jonathan Kuck, Boris Ivanovic, Silvio Savarese, Edward Schmerling, Marco Pavone
2020	A	`conf`	A Framework for Sample Efficient Interval Estimation with Control Variates. AISTATS Shengjia Zhao, Christopher Yeh, Stefano Ermon
2020	J	`jnl`	A Framework for Sample Efficient Interval Estimation with Control Variates. CoRR Shengjia Zhao, Christopher Yeh, Stefano Ermon
2020	J	`jnl`	A Theory of Usable Information Under Computational Constraints. CoRR Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon
2020	A*	`conf`	A Theory of Usable Information under Computational Constraints. ICLR Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon
2020	A*	`conf`	Domain Adaptive Imitation Learning. ICML Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon
2020	A*	`conf`	Individual Calibration with Randomized Forecasting. ICML Shengjia Zhao, Tengyu Ma, Stefano Ermon
2020	J	`jnl`	Individual Calibration with Randomized Forecasting. CoRR Shengjia Zhao, Tengyu Ma, Stefano Ermon
2020	A	`conf`	Permutation Invariant Graph Generation via Score-Based Generative Modeling. AISTATS Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon
2020	J	`jnl`	Permutation Invariant Graph Generation via Score-Based Generative Modeling. CoRR Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon
2020	J	`jnl`	Privacy Preserving Recalibration under Domain Shift. CoRR Rachel Luo, Shengjia Zhao, Jiaming Song, Jonathan Kuck, Stefano Ermon, Silvio Savarese
2020	J	`jnl`	Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration. CoRR Shengjia Zhao, Stefano Ermon
2019	A*	`conf`	Adaptive Antithetic Sampling for Variance Reduction. ICML Hongyu Ren, Shengjia Zhao, Stefano Ermon
2019	J	`jnl`	Approximating Human Judgment of Generated Image Quality. CoRR Y. Alex Kolchinski, Sharon Zhou, Shengjia Zhao, Mitchell L. Gordon, Stefano Ermon
2019	J	`jnl`	Cross Domain Imitation Learning. CoRR Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon
2019	A*	`conf`	InfoVAE: Balancing Learning and Inference in Variational Autoencoders. AAAI Shengjia Zhao, Jiaming Song, Stefano Ermon
2019	A	`conf`	Learning Controllable Fair Representations. AISTATS Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon
2019	—	`conf`	Learning Neural PDE Solvers with Convergence Guarantees. ICLR (Poster) Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon
2019	J	`jnl`	Learning Neural PDE Solvers with Convergence Guarantees. CoRR Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon
2018	A	`conf`	A Lagrangian Perspective on Latent Variable Generative Models. UAI Shengjia Zhao, Jiaming Song, Stefano Ermon
2018	A*	`conf`	Amortized Inference Regularization. NeurIPS Rui Shu, Hung H. Bui, Shengjia Zhao, Mykel J. Kochenderfer, Stefano Ermon
2018	J	`jnl`	Amortized Inference Regularization. CoRR Rui Shu, Hung H. Bui, Shengjia Zhao, Mykel J. Kochenderfer, Stefano Ermon
2018	A*	`conf`	Bias and Generalization in Deep Generative Models: An Empirical Study. NeurIPS Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah D. Goodman, Stefano Ermon
2018	J	`jnl`	Bias and Generalization in Deep Generative Models: An Empirical Study. CoRR Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah D. Goodman, Stefano Ermon
2018	J	`jnl`	Learning Controllable Fair Representations. CoRR Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon
2018	—	`conf`	Rethinking Style and Content Disentanglement in Variational Autoencoders. ICLR (Workshop) Rui Shu, Shengjia Zhao, Mykel J. Kochenderfer
2018	J	`jnl`	The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models. CoRR Shengjia Zhao, Jiaming Song, Stefano Ermon
2017	—	`conf`	A-NICE-MC: Adversarial Training for MCMC. NIPS Jiaming Song, Shengjia Zhao, Stefano Ermon
2017	J	`jnl`	A-NICE-MC: Adversarial Training for MCMC. CoRR Jiaming Song, Shengjia Zhao, Stefano Ermon
2017	—	`conf`	Generative Adversarial Learning of Markov Chains. ICLR (Workshop) Jiaming Song, Shengjia Zhao, Stefano Ermon
2017	J	`jnl`	InfoVAE: Information Maximizing Variational Autoencoders. CoRR Shengjia Zhao, Jiaming Song, Stefano Ermon
2017	A*	`conf`	Learning Hierarchical Features from Deep Generative Models. ICML Shengjia Zhao, Jiaming Song, Stefano Ermon
2017	J	`jnl`	Learning Hierarchical Features from Generative Models. CoRR Shengjia Zhao, Jiaming Song, Stefano Ermon
2017	J	`jnl`	On the Limits of Learning Representations with Label-Based Supervision. CoRR Jiaming Song, Russell Stewart, Shengjia Zhao, Stefano Ermon
2017	J	`jnl`	Towards Deeper Understanding of Variational Autoencoding Models. CoRR Shengjia Zhao, Jiaming Song, Stefano Ermon
2016	—	`conf`	Adaptive Concentration Inequalities for Sequential Decision Problems. NIPS Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon
2016	A*	`conf`	Closing the Gap Between Short and Long XORs for Model Counting. AAAI Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, Stefano Ermon
2015	J	`jnl`	Closing the Gap Between Short and Long XORs for Model Counting. CoRR Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, Stefano Ermon