Blanchet, Jose

63 publications

NeurIPS 2025 Estimation of Treatment Effects in Extreme and Unobserved Data Jiyuan Tan, Vasilis Syrgkanis, Jose Blanchet

NeurIPS 2025 Improved Confidence Regions and Optimal Algorithms for Online and Offline Linear MNL Bandits Yuxuan Han, Jose Blanchet, Zhengyuan Zhou

NeurIPS 2025 Multi-Agent Learning Under Uncertainty: Recurrence vs. Concentration Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos, Jose Blanchet

AISTATS 2025 Optimal Downsampling for Imbalanced Classification with Generalized Linear Models Yan Chen, Jose Blanchet, Krzysztof Dembczynski, Laura Fee Nern, Aaron Eliasib Flores

NeurIPS 2025 Quantum Speedup of Non-Linear Monte Carlo Problems Jose Blanchet, Yassine Hamoudi, Mario Szegedy, Guanyang Wang

NeurIPS 2025 Robust Equilibria in Continuous Games: From Strategic to Dynamic Robustness Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos, Jose Blanchet

AISTATS 2025 ScoreFusion: Fusing Score-Based Generative Models via Kullback–Leibler Barycenters Hao Liu, Tony Junze Ye, Jose Blanchet, Nian Si

AISTATS 2025 Statistical Learning of Distributionally Robust Stochastic Control in Continuous State Spaces Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou

ICML 2025 Tightening Causal Bounds via Covariate-Aware Optimal Transport Sirui Lin, Zijun Gao, Jose Blanchet, Peter Glynn

NeurIPS 2024 An Efficient High-Dimensional Gradient Estimator for Stochastic Differential Equations Shengbo Wang, Jose Blanchet, Peter Glynn

NeurIPS 2024 Automatic Outlier Rectification via Optimal Transport Jose Blanchet, Jiajin Li, Markus Pelger, Greg Zanotti

NeurIPS 2024 Consistency of Neural Causal Partial Identification Jiyuan Tan, Jose Blanchet, Vasilis Syrgkanis

NeurIPS 2024 Deep Learning for Computing Convergence Rates of Markov Chains Yanlin Qu, Jose Blanchet, Peter Glynn

UAI 2024 Distributionally Robust Optimization as a Scalable Framework to Characterize Extreme Value Distributions Patrick Kuiper, Ali Hasan, Wenhao Yang, Yuting Ng, Hoda Bidkhori, Jose Blanchet, Vahid Tarokh

ICMLW 2024 Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm Miao Lu, Han Zhong, Tong Zhang, Jose Blanchet

NeurIPS 2024 Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms Miao Lu, Han Zhong, Tong Zhang, Jose Blanchet

AISTATS 2024 Feasible $q$-Learning for Average Reward Reinforcement Learning Ying Jin, Ramki Gummadi, Zhengyuan Zhou, Jose Blanchet

ICLR 2024 Optimal Sample Complexity for Average Reward Markov Decision Processes Shengbo Wang, Jose Blanchet, Peter Glynn

ICML 2024 Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty Kaizhao Liu, Jose Blanchet, Lexing Ying, Yiping Lu

NeurIPS 2024 Provably Mitigating Overoptimization in RLHF: Your SFT Loss Is Implicitly an Adversarial Regularizer Zhihan Liu, Miao Lu, Shenao Zhang, Boyi Liu, Hongyi Guo, Yingxiang Yang, Jose Blanchet, Zhaoran Wang

ICMLW 2024 Provably Mitigating Overoptimization in RLHF: Your SFT Loss Is Implicitly an Adversarial Regularizer Zhihan Liu, Miao Lu, Shenao Zhang, Boyi Liu, Hongyi Guo, Yingxiang Yang, Jose Blanchet, Zhaoran Wang

JMLR 2024 Sample Complexity of Variance-Reduced Distributionally Robust Q-Learning Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou

ICML 2024 Single-Trajectory Distributionally Robust Reinforcement Learning Zhipeng Liang, Xiaoteng Ma, Jose Blanchet, Jun Yang, Jiheng Zhang, Zhengyuan Zhou

ICML 2024 Stability Evaluation Through Distributional Perturbation Analysis Jose Blanchet, Peng Cui, Jiajin Li, Jiashuo Liu

NeurIPSW 2024 Stability Evaluation of Large Language Models via Distributional Perturbation Analysis Jiashuo Liu, Jiajin Li, Peng Cui, Jose Blanchet

ICLR 2023 A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data Jiajin Li, Jianheng Tang, Lemin Kong, Huikang Liu, Jia Li, Anthony Man-Cho So, Jose Blanchet

AISTATS 2023 A Finite Sample Complexity Bound for Distributionally Robust Q-Learning Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou

NeurIPSW 2023 Accelerated Sampling of Rare Events Using a Neural Network Bias Potential Xinru Hua, Rasool Ahmad, Jose Blanchet, Wei Cai

NeurIPS 2023 Double Pessimism Is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage Jose Blanchet, Miao Lu, Tong Zhang, Han Zhong

JMLR 2023 Dropout Training Is Distributionally Robust Optimal José Blanchet, Yang Kang, José Luis Montiel Olea, Viet Anh Nguyen, Xuhui Zhang

ICLR 2023 Minimax Optimal Kernel Operator Learning via Multilevel Training Jikai Jin, Yiping Lu, Jose Blanchet, Lexing Ying

NeurIPS 2023 Payoff-Based Learning with Matrix Multiplicative Weights in Quantum Games Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos, Jose Blanchet

NeurIPSW 2023 Representation Learning for Extremes Ali Hasan, Yuting Ng, Jose Blanchet, Vahid Tarokh

NeurIPS 2023 Universal Gradient Descent Ascent Method for Nonconvex-Nonconcave Minimax Optimization Taoli Zheng, Linglingzhi Zhu, Anthony Man-Cho So, Jose Blanchet, Jiajin Li

AISTATS 2023 Wasserstein Distributionally Robust Linear-Quadratic Estimation Under Martingale Constraints Kyriakos Lotidis, Nicholas Bambos, Jose Blanchet, Jiajin Li

NeurIPS 2023 When Can Regression-Adjusted Control Variate Help? Rare Events, Sobolev Embedding and Minimax Optimality Jose Blanchet, Haoxuan Chen, Yiping Lu, Lexing Ying

AISTATS 2022 A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality Xuhui Zhang, Jose Blanchet, Soumyadip Ghosh, Mark S. Squillante

ICML 2022 Distributionally Robust $q$-Learning Zijian Liu, Qinxun Bai, Jose Blanchet, Perry Dong, Wei Xu, Zhengqing Zhou, Zhengyuan Zhou

ICLR 2022 Machine Learning for Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality Yiping Lu, Haoxuan Chen, Jianfeng Lu, Lexing Ying, Jose Blanchet

NeurIPSW 2022 Minimax Optimal Kernel Operator Learning via Multilevel Training Jikai Jin, Yiping Lu, Jose Blanchet, Lexing Ying

UAI 2022 Modeling Extremes with $d$-Max-Decreasing Neural Networks Ali Hasan, Khalil Elkhalil, Yuting Ng, João M. Pereira, Sina Farsiu, Jose Blanchet, Vahid Tarokh

JMLR 2022 No Weighted-Regret Learning in Adversarial Bandits with Delays Ilai Bistritz, Zhengyuan Zhou, Xi Chen, Nicholas Bambos, Jose Blanchet

NeurIPS 2022 Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent Yiping Lu, Jose Blanchet, Lexing Ying

NeurIPSW 2022 Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls Yiping Lu, Jiajin Li, Lexing Ying, Jose Blanchet

NeurIPS 2022 Tikhonov Regularization Is Optimal Transport Robust Under Martingale Constraints Jiajin Li, Sirui Lin, Jose Blanchet, Viet Anh Nguyen

AISTATS 2021 Finite-Sample Regret Bound for Distributionally Robust Offline Tabular Reinforcement Learning Zhengqing Zhou, Zhengyuan Zhou, Qinxun Bai, Linhai Qiu, Jose Blanchet, Peter Glynn

NeurIPS 2021 Adversarial Regression with Doubly Non-Negative Weighting Matrices Tam Le, Truyen Nguyen, Makoto Yamada, Jose Blanchet, Viet Anh Nguyen

NeurIPS 2021 Modified Frank Wolfe in Probability Space Carson Kent, Jiajin Li, Jose Blanchet, Peter W Glynn

ICML 2021 Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts Bahar Taskesen, Man-Chung Yue, Jose Blanchet, Daniel Kuhn, Viet Anh Nguyen

NeurIPSW 2021 Statistical Numerical PDE : Fast Rate, Neural Scaling Law and When It’s Optimal Yiping Lu, Haoxuan Chen, Jianfeng Lu, Lexing Ying, Jose Blanchet

ICML 2021 Testing Group Fairness via Optimal Transport Projections Nian Si, Karthyek Murthy, Jose Blanchet, Viet Anh Nguyen

NeurIPS 2020 Distributionally Robust Local Non-Parametric Conditional Estimation Viet Anh Nguyen, Fan Zhang, Jose Blanchet, Erick Delage, Yinyu Ye

NeurIPS 2020 Distributionally Robust Parametric Maximum Likelihood Estimation Viet Anh Nguyen, Xuhui Zhang, Jose Blanchet, Angelos Georghiou

ICML 2020 Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits Nian Si, Fan Zhang, Zhengyuan Zhou, Jose Blanchet

NeurIPS 2020 Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality Nian Si, Jose Blanchet, Soumyadip Ghosh, Mark Squillante

ICML 2020 Robust Bayesian Classification Using an Optimistic Score Ratio Viet Anh Nguyen, Nian Si, Jose Blanchet

NeurIPS 2019 Learning in Generalized Linear Contextual Bandits with Stochastic Delays Zhengyuan Zhou, Renyuan Xu, Jose Blanchet

NeurIPS 2019 Multivariate Distributionally Robust Convex Regression Under Absolute Error Loss Jose Blanchet, Peter W. Glynn, Jun Yan, Zhengqing Zhou

NeurIPS 2019 Online EXP3 Learning in Adversarial Bandits with Delayed Feedback Ilai Bistritz, Zhengyuan Zhou, Xi Chen, Nicholas Bambos, Jose Blanchet

ICML 2019 Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning Casey Chu, Jose Blanchet, Peter Glynn

NeurIPS 2019 Semi-Parametric Dynamic Contextual Pricing Virag Shah, Ramesh Johari, Jose Blanchet

NeurIPS 2018 Bandit Learning with Positive Externalities Virag Shah, Jose Blanchet, Ramesh Johari

ACML 2017 Distributionally Robust Groupwise Regularization Estimator Jose Blanchet, Yang Kang