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Lexing Ying
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Publications
- 2023
- [c21]Jikai Jin, Yiping Lu, José H. Blanchet, Lexing Ying:
Minimax Optimal Kernel Operator Learning via Multilevel Training. ICLR 2023 - [c20]Jose H. Blanchet, Haoxuan Chen, Yiping Lu, Lexing Ying:
When can Regression-Adjusted Control Variate Help? Rare Events, Sobolev Embedding and Minimax Optimality. NeurIPS 2023 - [i73]Jose H. Blanchet, Haoxuan Chen, Yiping Lu, Lexing Ying:
When can Regression-Adjusted Control Variates Help? Rare Events, Sobolev Embedding and Minimax Optimality. CoRR abs/2305.16527 (2023) - 2022
- [c18]Yiping Lu, Haoxuan Chen, Jianfeng Lu, Lexing Ying, Jose H. Blanchet:
Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality. ICLR 2022 - [c15]Yiping Lu, José H. Blanchet, Lexing Ying:
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent. NeurIPS 2022 - [i57]Yiping Lu, Jose H. Blanchet, Lexing Ying:
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent. CoRR abs/2205.07331 (2022) - [i53]Jikai Jin, Yiping Lu, Jose H. Blanchet, Lexing Ying:
Minimax Optimal Kernel Operator Learning via Multilevel Training. CoRR abs/2209.14430 (2022) - [i50]Yiping Lu, Jiajin Li, Lexing Ying, Jose H. Blanchet:
Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls. CoRR abs/2211.15241 (2022) - 2021
- [i36]Yiping Lu, Haoxuan Chen, Jianfeng Lu, Lexing Ying, Jose H. Blanchet:
Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality. CoRR abs/2110.06897 (2021)
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