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Ying Jin
Ying Jin
PhD student, Department of Statistics, Stanford University
Email confirmado em stanford.edu - Página inicial
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Is pessimism provably efficient for offline rl?
Y Jin, Z Yang, Z Wang
International Conference on Machine Learning, 5084-5096, 2021
962021
Contemporary symbolic regression methods and their relative performance
W La Cava, P Orzechowski, B Burlacu, FO de França, M Virgolin, Y Jin, ...
Proceedings of the Neural Information Processing Systems (Track on Datasets …, 2021
262021
Bayesian symbolic regression
Y Jin, W Fu, J Kang, J Guo, J Guo
arXiv preprint arXiv:1910.08892, 2019
212019
Sensitivity analysis of individual treatment effects: A robust conformal inference approach
Y Jin, Z Ren, EJ Candès
arXiv preprint arXiv:2111.12161, 2021
62021
Sensitivity Analysis under the -Sensitivity Models: Definition, Estimation and Inference
Y Jin, Z Ren, Z Zhou
arXiv preprint arXiv:2203.04373, 2022
12022
Conditional and transductive inference about populations with fixed attributes
Y Jin, D Rothenhäusler
arXiv preprint arXiv:2104.04565, 2021
1*2021
Towards Optimal Variance Reduction in Online Controlled Experiments
Y Jin, S Ba
arXiv preprint arXiv:2110.13406, 2021
2021
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Artigos 1–7