Jose H. Blanchet

ORCID

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 104

Top Venues

Publications

Patrick K. Kuiper, Sirui Lin, Jose H. Blanchet, Vahid Tarokh
Generative Learning for Simulation of Vehicle Faults. CoRR (2024)
Jose H. Blanchet, Arun Jambulapati, Carson Kent, Aaron Sidford
Towards optimal running timesfor optimal transport. Oper. Res. Lett. 52 (2024)
Guanyang Wang, Jose H. Blanchet, Peter W. Glynn
When are Unbiased Monte Carlo Estimators More Preferable than Biased Ones? CoRR (2024)
Kaizhao Liu, Jose H. Blanchet, Lexing Ying, Yiping Lu
Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty. CoRR (2024)
Miao Lu, Han Zhong, Tong Zhang, Jose H. Blanchet
Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm. CoRR (2024)
Yanlin Qu, Jose H. Blanchet, Peter W. Glynn
Deep Learning for Computing Convergence Rates of Markov Chains. CoRR (2024)
Shengbo Wang, Nian Si, Jose H. Blanchet, Zhengyuan Zhou
Statistical Learning of Distributionally Robust Stochastic Control in Continuous State Spaces. CoRR (2024)
Jose H. Blanchet, Jiajin Li, Markus Pelger, Greg Zanotti
Automatic Outlier Rectification via Optimal Transport. CoRR (2024)
Hao Liu, Junze Ye, Jose H. Blanchet, Nian Si
ScoreFusion: fusing score-based generative models via Kullback-Leibler barycenters. CoRR (2024)
Ying Jin, Ramki Gummadi, Zhengyuan Zhou, Jose H. Blanchet
Feasible Q-Learning for Average Reward Reinforcement Learning. AISTATS (2024)
Jose H. Blanchet, Peng Cui, Jiajin Li, Jiashuo Liu
Stability Evaluation via Distributional Perturbation Analysis. CoRR (2024)
Zhihan Liu, Miao Lu, Shenao Zhang, Boyi Liu, Hongyi Guo, Yingxiang Yang, Jose H. Blanchet, Zhaoran Wang
Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer. CoRR (2024)
Jiyuan Tan, Jose H. Blanchet, Vasilis Syrgkanis
Consistency of Neural Causal Partial Identification. CoRR (2024)
Xinru Hua, Rasool Ahmad, Jose H. Blanchet, Wei Cai
Accelerated Sampling of Rare Events using a Neural Network Bias Potential. CoRR (2024)
Jiajin Li, Jianheng Tang, Lemin Kong, Huikang Liu, Jia Li, Anthony Man-Cho So, Jose H. Blanchet
A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data. CoRR (2023)
Jiajin Li, Jianheng Tang, Lemin Kong, Huikang Liu, Jia Li, Anthony Man-Cho So, Jose H. Blanchet
A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data. ICLR (2023)
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)
Jose H. Blanchet, Miao Lu, Tong Zhang, Han Zhong
Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage. NeurIPS (2023)
Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos, Jose H. Blanchet
Payoff-based Learning with Matrix Multiplicative Weights in Quantum Games. NeurIPS (2023)
Jose H. Blanchet, Haoxuan Chen, Yiping Lu, Lexing Ying
When can Regression-Adjusted Control Variates Help? Rare Events, Sobolev Embedding and Minimax Optimality. CoRR (2023)
Jose H. Blanchet, Alexander Shapiro
Statistical Limit Theorems in Distributionally Robust Optimization. WSC (2023)
Nian Si, Fan Zhang, Zhengyuan Zhou, Jose H. Blanchet
Distributionally Robust Batch Contextual Bandits. Manag. Sci. 69 (10) (2023)
Xinru Hua, Truyen Nguyen, Tam Le, Jose H. Blanchet, Viet Anh Nguyen
Dynamic Flows on Curved Space Generated by Labeled Data. CoRR (2023)
Zhipeng Liang, Xiaoteng Ma, Jose H. Blanchet, Jiheng Zhang, Zhengyuan Zhou
Single-Trajectory Distributionally Robust Reinforcement Learning. CoRR (2023)
Shengbo Wang, Nian Si, Jose H. Blanchet, Zhengyuan Zhou
On the Foundation of Distributionally Robust Reinforcement Learning. CoRR (2023)
Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos, Jose H. Blanchet
Payoff-based learning with matrix multiplicative weights in quantum games. CoRR (2023)
Taoli Zheng, Linglingzhi Zhu, Anthony Man-Cho So, Jose H. Blanchet, Jiajin Li
Universal Gradient Descent Ascent Method for Nonconvex-Nonconcave Minimax Optimization. NeurIPS (2023)
Jose H. Blanchet, Yang Kang, José Luis Montiel Olea, Viet Anh Nguyen, Xuhui Zhang
Dropout Training is Distributionally Robust Optimal. J. Mach. Learn. Res. 24 (2023)
Shengbo Wang, Nian Si, Jose H. Blanchet, Zhengyuan Zhou
Sample Complexity of Variance-reduced Distributionally Robust Q-learning. CoRR (2023)
Kyriakos Lotidis, Nicholas Bambos, Jose H. Blanchet, Jiajin Li
Wasserstein Distributionally Robust Linear-Quadratic Estimation under Martingale Constraints. AISTATS (2023)
Shengbo Wang, Jose H. Blanchet, Peter W. Glynn
Optimal Sample Complexity for Average Reward Markov Decision Processes. CoRR (2023)
Xinru Hua, Truyen Nguyen, Tam Le, Jose H. Blanchet, Viet Anh Nguyen
Dynamic Flows on Curved Space Generated by Labeled Data. IJCAI (2023)
Jin Xie, Teng Zhang, Jose H. Blanchet, Peter W. Glynn, Matthew Randolph, David Scheinker
The Design and Implementation of a Broadly Applicable Algorithm for Optimizing Intra-Day Surgical Scheduling. CoRR (2022)
Jiajin Li, Sirui Lin, Jose H. Blanchet, Viet Anh Nguyen
Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints. NeurIPS (2022)
Taoli Zheng, Linglingzhi Zhu, Anthony Man-Cho So, Jose H. Blanchet, Jiajin Li
Doubly Smoothed GDA: Global Convergent Algorithm for Constrained Nonconvex-Nonconcave Minimax Optimization. CoRR (2022)
Ali Hasan, Khalil Elkhalil, Yuting Ng, João M. Pereira, Sina Farsiu, Jose H. Blanchet, Vahid Tarokh
Modeling extremes with d-max-decreasing neural networks. UAI (2022)
Jose H. Blanchet, Karthyek R. A. Murthy, Fan Zhang
Optimal Transport-Based Distributionally Robust Optimization: Structural Properties and Iterative Schemes. Math. Oper. Res. 47 (2) (2022)
Xuhui Zhang, Jose H. Blanchet, Soumyadip Ghosh, Mark S. Squillante
A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality. CoRR (2022)
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)
Yiping Lu, Jose H. Blanchet, Lexing Ying
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent. CoRR (2022)
Jose H. Blanchet, Lin Chen, Xun Yu Zhou
Distributionally Robust Mean-Variance Portfolio Selection with Wasserstein Distances. Manag. Sci. 68 (9) (2022)
Jiajin Li, Sirui Lin, Jose H. Blanchet, Viet Anh Nguyen
Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints. CoRR (2022)
Xinru Hua, Huanzhong Xu, Jose H. Blanchet, Viet Anh Nguyen
Human Imperceptible Attacks and Applications to Improve Fairness. WSC (2022)
Xiaoteng Ma, Zhipeng Liang, Jose H. Blanchet, Mingwen Liu, Li Xia, Jiheng Zhang, Qianchuan Zhao, Zhengyuan Zhou
Distributionally Robust Offline Reinforcement Learning with Linear Function Approximation. CoRR (2022)
Yuan Shi, Saied Mahdian, Jose H. Blanchet, Peter W. Glynn, Andrew Young Shin, David Scheinker
Surgical Scheduling via Optimization and Machine Learning with Long-Tailed Data. CoRR (2022)
Yiping Lu, Jiajin Li, Lexing Ying, Jose H. Blanchet
Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls. CoRR (2022)
Jose H. Blanchet
Some open problems in exact simulation of stochastic differential equations. Queueing Syst. Theory Appl. 100 (3-4) (2022)
Jiajin Li, Jianheng Tang, Lemin Kong, Huikang Liu, Jia Li, Anthony Man-Cho So, Jose H. Blanchet
Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Learning. CoRR (2022)
Zijian Liu, Qinxun Bai, Jose H. Blanchet, Perry Dong, Wei Xu, Zhengqing Zhou, Zhengyuan Zhou
Distributionally Robust Q-Learning. ICML (2022)
Jose H. Blanchet, Martin I. Reiman, Virag Shah, Lawrence M. Wein, Linjia Wu
Asymptotically Optimal Control of a Centralized Dynamic Matching Market with General Utilities. Oper. Res. 70 (6) (2022)
Jikai Jin, Yiping Lu, Jose H. Blanchet, Lexing Ying
Minimax Optimal Kernel Operator Learning via Multilevel Training. CoRR (2022)
Nian Si, Karthyek Murthy, Jose H. Blanchet, Viet Anh Nguyen
Testing Group Fairness via Optimal Transport Projections. CoRR (2021)
Tam Le, Truyen Nguyen, Makoto Yamada, Jose H. Blanchet, Viet Anh Nguyen
Adversarial Regression with Doubly Non-negative Weighting Matrices. CoRR (2021)
Bahar Taskesen, Man-Chung Yue, Jose H. Blanchet, Daniel Kuhn, Viet Anh Nguyen
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts. CoRR (2021)
Jose H. Blanchet, Fernando Hernandez, Viet Anh Nguyen, Markus Pelger, Xuhui Zhang
Time-Series Imputation with Wasserstein Interpolation for Optimal Look-Ahead-Bias and Variance Tradeoff. CoRR (2021)
Ilai Bistritz, Zhengyuan Zhou, Xi Chen, Nicholas Bambos, Jose H. Blanchet
No Discounted-Regret Learning in Adversarial Bandits with Delays. CoRR (2021)
Jose H. Blanchet, Yang Kang
Sample Out-of-Sample Inference Based on Wasserstein Distance. Oper. Res. 69 (3) (2021)
Tam Le, Truyen Nguyen, Makoto Yamada, Jose H. Blanchet, Viet Anh Nguyen
Adversarial Regression with Doubly Non-negative Weighting Matrices. NeurIPS (2021)
Bahar Taskesen, Man-Chung Yue, Jose H. Blanchet, Daniel Kuhn, Viet Anh Nguyen
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts. ICML (2021)
Ali Hasan, Khalil Elkhalil, João M. Pereira, Sina Farsiu, Jose H. Blanchet, Vahid Tarokh
Deep Extreme Value Copulas for Estimation and Sampling. CoRR (2021)
Nian Si, Karthyek Murthy, Jose H. Blanchet, Viet Anh Nguyen
Testing Group Fairness via Optimal Transport Projections. ICML (2021)
Jose H. Blanchet, Karthyek Murthy, Viet Anh Nguyen
Statistical Analysis of Wasserstein Distributionally Robust Estimators. CoRR (2021)
Bahar Taskesen, Jose H. Blanchet, Daniel Kuhn, Viet Anh Nguyen
A Statistical Test for Probabilistic Fairness. FAccT (2021)
Zhengqing Zhou, Qinxun Bai, Zhengyuan Zhou, Linhai Qiu, Jose H. Blanchet, Peter W. Glynn
Finite-Sample Regret Bound for Distributionally Robust Offline Tabular Reinforcement Learning. AISTATS (2021)
Jose H. Blanchet, Xinyun Chen
Rates of Convergence to Stationarity for Reflected Brownian Motion. Math. Oper. Res. 45 (2) (2020)
Saied Mahdian, Jose H. Blanchet, Peter W. Glynn
A Class of Optimal Transport Regularized Formulations with Applications to Wasserstein GANs. WSC (2020)
Bahar Taskesen, Jose H. Blanchet, Daniel Kuhn, Viet Anh Nguyen
A Statistical Test for Probabilistic Fairness. CoRR (2020)
Viet Anh Nguyen, Nian Si, Jose H. Blanchet
Robust Bayesian Classification Using An Optimistic Score Ratio. ICML (2020)
Jose H. Blanchet, Yang Kang, Jose Luis Montiel Olea, Viet Anh Nguyen, Xuhui Zhang
Machine Learning's Dropout Training is Distributionally Robust Optimal. CoRR (2020)
Bahar Taskesen, Viet Anh Nguyen, Daniel Kuhn, Jose H. Blanchet
A Distributionally Robust Approach to Fair Classification. CoRR (2020)
Viet Anh Nguyen, Nian Si, Jose H. Blanchet
Robust Bayesian Classification Using an Optimistic Score Ratio. CoRR (2020)
Jose H. Blanchet, Martin I. Reiman, Virag Shah, Lawrence M. Wein
Asymptotically Optimal Control of a Centralized Dynamic Matching Market with General Utilities. CoRR (2020)
Nian Si, Jose H. Blanchet, Soumyadip Ghosh, Mark S. Squillante
Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality. NeurIPS (2020)
Jose H. Blanchet, Renyuan Xu, Zhengyuan Zhou
Delay-Adaptive Learning in Generalized Linear Contextual Bandits. CoRR (2020)
Yanjun Han, Zhengqing Zhou, Zhengyuan Zhou, Jose H. Blanchet, Peter W. Glynn, Yinyu Ye
Sequential Batch Learning in Finite-Action Linear Contextual Bandits. CoRR (2020)
Nian Si, Fan Zhang, Zhengyuan Zhou, Jose H. Blanchet
Distributional Robust Batch Contextual Bandits. CoRR (2020)
Nian Si, Fan Zhang, Zhengyuan Zhou, Jose H. Blanchet
Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits. ICML (2020)
Jose H. Blanchet, Yang Kang, Karthyek Rajhaa A. M.
Robust Wasserstein profile inference and applications to machine learning. J. Appl. Probab. 56 (3) (2019)
Virag Shah, Jose H. Blanchet, Ramesh Johari
Semi-parametric dynamic contextual pricing. CoRR (2019)
Casey Chu, Jose H. Blanchet, Peter W. Glynn
Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning. ICML (2019)
Virag Shah, Ramesh Johari, Jose H. Blanchet
Semi-Parametric Dynamic Contextual Pricing. NeurIPS (2019)
Zhengyuan Zhou, Renyuan Xu, Jose H. Blanchet
Learning in Generalized Linear Contextual Bandits with Stochastic Delays. NeurIPS (2019)
Bohan Chen, Jose H. Blanchet, Chang-han Rhee, Bert Zwart
Efficient Rare-Event Simulation for Multiple Jump Events in Regularly Varying Random Walks and Compound Poisson Processes. Math. Oper. Res. 44 (3) (2019)
Casey Chu, Jose H. Blanchet, Peter W. Glynn
Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning. CoRR (2019)
Jose H. Blanchet, Karthyek R. A. Murthy
Quantifying Distributional Model Risk via Optimal Transport. Math. Oper. Res. 44 (2) (2019)
Jose H. Blanchet, Yang Kang, Fan Zhang, Zhangyi Hu
A Distributionally Robust Boosting Algorithm. CoRR (2019)
Jose H. Blanchet, Fan Zhang, Yang Kang, Zhangyi Hu
A Distributionally Robust Boosting Algorithm. WSC (2019)
Mihail Bazhba, Jose H. Blanchet, Chang-han Rhee, Bert Zwart
Queue length asymptotics for the multiple-server queue with heavy-tailed Weibull service times. Queueing Syst. Theory Appl. 93 (3-4) (2019)
Jose H. Blanchet, Xinyun Chen
Perfect Sampling of Generalized Jackson Networks. Math. Oper. Res. 44 (2) (2019)
Jose H. Blanchet, Nian Si
Optimal uncertainty size in distributionally robust inverse covariance estimation. Oper. Res. Lett. 47 (6) (2019)
Ilai Bistritz, Zhengyuan Zhou, Xi Chen, Nicholas Bambos, Jose H. Blanchet
Online EXP3 Learning in Adversarial Bandits with Delayed Feedback. NeurIPS (2019)
Saied Mahdian, Jose H. Blanchet, Peter W. Glynn
Optimal Transport Relaxations with Application to Wasserstein GANs. CoRR (2019)
Jose H. Blanchet, Jing Dong, Zhipeng Liu
Exact sampling of the infinite horizon maximum of a random walk over a nonlinear boundary. J. Appl. Probab. 56 (1) (2019)
Jose H. Blanchet, Juan Li, Marvin K. Nakayama
Rare-Event Simulation for Distribution Networks. Oper. Res. 67 (5) (2019)
Jose H. Blanchet, Peter W. Glynn, Jun Yan, Zhengqing Zhou
Multivariate Distributionally Robust Convex Regression under Absolute Error Loss. NeurIPS (2019)
Jose H. Blanchet, Coralia Cartis, Matt Menickelly, Katya Scheinberg
Convergence Rate Analysis of a Stochastic Trust-Region Method via Supermartingales. INFORMS J. Optim. 1 (2) (2019)
Virag Shah, Jose H. Blanchet, Ramesh Johari
Bandit Learning with Positive Externalities. CoRR (2018)
Jose H. Blanchet, Karthyek Rajhaa A. M.
Exact simulation of multidimensional reflected Brownian motion. J. Appl. Probab. 55 (1) (2018)
Virag Shah, Jose H. Blanchet, Ramesh Johari
Bandit Learning with Positive Externalities. NeurIPS (2018)
Jose H. Blanchet, Jing Dong, Yanan Pei
Perfect sampling of GI/GI/c queues. Queueing Syst. Theory Appl. 90 (1-2) (2018)

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Publication Activity (10 Years)

Top Topics

Top Venues

Jose H. Blanchet