Suivre
Shengjia Zhao
Shengjia Zhao
OpenAI
Adresse e-mail validée de stanford.edu - Page d'accueil
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Gpt-4 technical report
OpenAI
arXiv preprint arXiv:2303.08774, 2023
1037*2023
InfoVAE: Balancing Learning and Inference in Variational Autoencoders
S Zhao, J Song, S Ermon
Proceedings of the AAAI Conference on Artificial Intelligence, 2019
727*2019
Towards deeper understanding of variational autoencoding models
S Zhao, J Song, S Ermon
arXiv preprint arXiv:1702.08658, 2017
1872017
Learning controllable fair representations
J Song, P Kalluri, A Grover, S Zhao, S Ermon
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
1822019
Learning hierarchical features from generative models
S Zhao, J Song, S Ermon
Proceedings of the 34th International Conference on Machine Learning, PMLR …, 2017
160*2017
Permutation Invariant Graph Generation via Score-Based Generative Modeling
C Niu, Y Song, J Song, S Zhao, A Grover, S Ermon
International Conference on Artificial Intelligence and Statistics, 2020
1472020
A theory of usable information under computational constraints
Y Xu, S Zhao, J Song, R Stewart, S Ermon
International Conference on Learning Representations, 2019
1292019
A-nice-mc: Adversarial training for mcmc
J Song, S Zhao, S Ermon
Proceedings of the 31st International Conference on Neural Information …, 2017
1262017
Learning neural PDE solvers with convergence guarantees
JT Hsieh, S Zhao, S Eismann, L Mirabella, S Ermon
International Conference on Learning Representations, 2018
1252018
Bias and generalization in deep generative models: An empirical study
S Zhao, H Ren, A Yuan, J Song, N Goodman, S Ermon
Proceedings of the 32nd International Conference on Neural Information …, 2018
1222018
Amortized inference regularization
R Shu, HH Bui, S Zhao, MJ Kochenderfer, S Ermon
Proceedings of the 32nd International Conference on Neural Information …, 2018
992018
The information autoencoding family: A lagrangian perspective on latent variable generative models
S Zhao, J Song, S Ermon
Proc. 34th Conference on Uncertainty in Artificial Intelligence, 2018
782018
Individual Calibration with Randomized Forecasting
S Zhao, T Ma, S Ermon
International Conference on Machine Learning, 2020
652020
Adaptive concentration inequalities for sequential decision problems
S Zhao, E Zhou, A Sabharwal, S Ermon
Advances in Neural Information Processing Systems 29, 2016
532016
Domain adaptive imitation learning
K Kim, Y Gu, J Song, S Zhao, S Ermon
International Conference on Machine Learning, 5286-5295, 2020
462020
Sample-efficient safety assurances using conformal prediction
R Luo, S Zhao, J Kuck, B Ivanovic, S Savarese, E Schmerling, M Pavone
International Workshop on the Algorithmic Foundations of Robotics, 149-169, 2022
422022
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration
S Zhao, MP Kim, R Sahoo, T Ma, S Ermon
Proceedings of the 35th International Conference on Neural Information …, 2021
402021
Low-Degree Multicalibration
P Gopalan, MP Kim, M Singhal, S Zhao
2022 Conference on Learning Theory, 2022
302022
Closing the gap between short and long xors for model counting
S Zhao, S Chaturapruek, A Sabharwal, S Ermon
Proceedings of the AAAI Conference on Artificial Intelligence, 2015
252015
Reliable Decisions with Threshold Calibration
R Sahoo, S Zhao, A Chen, S Ermon
Proceedings of the 35th International Conference on Neural Information …, 2021
232021
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