Jose Blanchet

Classical and Quantum Speedups for Non-Convex Optimization via Energy Conserving Descent UAI 2026 Learning Max-Stable Representations that Extrapolate UAI 2026 Optimal downsampling for Imbalanced Classification with Generalized Linear Models AISTATS 2025 ScoreFusion: Fusing Score-based Generative Models via Kullback–Leibler Barycenters AISTATS 2025 Statistical Learning of Distributionally Robust Stochastic Control in Continuous State Spaces AISTATS 2025 Tightening Causal Bounds via Covariate-Aware Optimal Transport ICML 2025 An Efficient High-dimensional Gradient Estimator for Stochastic Differential Equations NeurIPS 2024 Automatic Outlier Rectification via Optimal Transport NeurIPS 2024 Consistency of Neural Causal Partial Identification NeurIPS 2024 Deep Learning for Computing Convergence Rates of Markov Chains NeurIPS 2024 Distributionally Robust Optimization as a Scalable Framework to Characterize Extreme Value Distributions UAI 2024 Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms NeurIPS 2024 Feasible Q-Learning for Average Reward Reinforcement Learning AISTATS 2024 Optimal Sample Complexity for Average Reward Markov Decision Processes ICLR 2024 Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty ICML 2024 Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer NeurIPS 2024 Sample Complexity of Variance-Reduced Distributionally Robust Q-Learning JMLR 2024 Single-Trajectory Distributionally Robust Reinforcement Learning ICML 2024 Stability Evaluation through Distributional Perturbation Analysis ICML 2024 A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data ICLR 2023 A Finite Sample Complexity Bound for Distributionally Robust Q-learning AISTATS 2023 Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage NeurIPS 2023 Dropout Training is Distributionally Robust Optimal JMLR 2023 Dynamic Flows on Curved Space Generated by Labeled Data IJCAI 2023 Minimax Optimal Kernel Operator Learning via Multilevel Training ICLR 2023 Payoff-based Learning with Matrix Multiplicative Weights in Quantum Games NeurIPS 2023 Universal Gradient Descent Ascent Method for Nonconvex-Nonconcave Minimax Optimization NeurIPS 2023 Wasserstein Distributionally Robust Linear-Quadratic Estimation under Martingale Constraints AISTATS 2023 When can Regression-Adjusted Control Variate Help? Rare Events, Sobolev Embedding and Minimax Optimality NeurIPS 2023 A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality AISTATS 2022 Distributionally Robust Q-Learning ICML 2022 Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality ICLR 2022 Modeling extremes with d-max-decreasing neural networks UAI 2022 No Weighted-Regret Learning in Adversarial Bandits with Delays JMLR 2022 Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent NeurIPS 2022 Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints NeurIPS 2022 Adversarial Regression with Doubly Non-negative Weighting Matrices NeurIPS 2021 Finite-Sample Regret Bound for Distributionally Robust Offline Tabular Reinforcement Learning AISTATS 2021 Modified Frank Wolfe in Probability Space NeurIPS 2021 Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts ICML 2021 Testing Group Fairness via Optimal Transport Projections ICML 2021 Distributionally Robust Local Non-parametric Conditional Estimation NeurIPS 2020 Distributionally Robust Parametric Maximum Likelihood Estimation NeurIPS 2020 Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits ICML 2020 Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality NeurIPS 2020 Robust Bayesian Classification Using An Optimistic Score Ratio ICML 2020 Learning in Generalized Linear Contextual Bandits with Stochastic Delays NeurIPS 2019 Multivariate Distributionally Robust Convex Regression under Absolute Error Loss NeurIPS 2019 Online EXP3 Learning in Adversarial Bandits with Delayed Feedback NeurIPS 2019 Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning ICML 2019 Semi-Parametric Dynamic Contextual Pricing NeurIPS 2019 Bandit Learning with Positive Externalities NeurIPS 2018 Distributionally Robust Groupwise Regularization Estimator ACML 2017