← Authors

Jose Blanchet

51 papers · 2017–2025 · 8 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (8) 🌍 Conference Polyglot (8) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (8) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (17) 🌈 Renaissance Researcher (7) 🧭 Keyword Pioneer 🏠 Conference Loyalist (22) 🀝 Dynamic Duo (11) πŸ‘‘ Triple Crown πŸ† Keyword Champion (2) πŸ”¬ Deep Specialist (17) πŸ—ƒοΈ Keyword Collector (53) πŸ”₯ Unstoppable (9) ⚑ Prolific Year (5) ❓ The Questioner πŸ“ˆ Trend Setter πŸ’Ž Century Club (51)

Conferences

NIPS (22) ICML (10) AISTATS (8) ICLR (4) JMLR (3) UAI (2) ACML (1) IJCAI (1)

Top co-authors

Zhengyuan Zhou (11) Viet Anh Nguyen (9) Nian Si (8) JIAJIN LI (7) Peter Glynn (6) Shengbo Wang (5) Lexing Ying (5) Yiping Lu (5) Nicholas Bambos (4) Zhengqing Zhou (3)

Research topics

Optimization & Theory (1)

Keywords

distributionally robust optimization (12) optimal transport (6) sample complexity (5) regret bound (4) wasserstein distance (4) robust policy (4) reinforcement learning (3) offline reinforcement learning (3) zero-sum game (3) multi-armed bandit (3) probability measure (2) distributionally robust (2) adversarial learning (2) distributional robustness (2) variance reduction (2) gradient descent (2) kernel regression (2) convex optimization (2) markov decision process (2) domain adaptation (2)

Papers

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