| 2025 | AISTATS | Optimal downsampling for Imbalanced Classification with Generalized Linear Models. | Yan Chen, Jose H. Blanchet, Krzysztof Dembczynski, Laura Fee Nern, Aaron E. Flores |
| 2025 | AISTATS | ScoreFusion: Fusing Score-based Generative Models via Kullback-Leibler Barycenters. | Hao Liu, Junze Ye, Jose H. Blanchet, Nian Si |
| 2025 | AISTATS | Statistical Learning of Distributionally Robust Stochastic Control in Continuous State Spaces. | Shengbo Wang, Nian Si, Jose H. Blanchet, Zhengyuan Zhou |
| 2025 | ICML | Tightening Causal Bounds via Covariate-Aware Optimal Transport. | Sirui Lin, Zijun Gao, Jose H. Blanchet, Peter W. Glynn |
| 2025 | WSC | Connecting Quantum Computing with Classical Stochastic Simulation. | Jose H. Blanchet, Mark S. Squillante, Mario Szegedy, Guanyang Wang |
| 2025 | WSC | Efficient Optimization Procedures for CVAR-Constrained Optimization with Regularly Varying Risk Factors. | Anish Senapati, Jose H. Blanchet, Fan Zhang, Bert Zwart |
| 2024 | AISTATS | Feasible Q-Learning for Average Reward Reinforcement Learning. | Ying Jin, Ramki Gummadi, Zhengyuan Zhou, Jose H. Blanchet |
| 2024 | WSC | Generative Learning for Simulation of Vehicle Faults. | Patrick K. Kuiper, Sirui Lin, Jose H. Blanchet, Vahid Tarokh |
| 2024 | UAI | Distributionally Robust Optimization as a Scalable Framework to Characterize Extreme Value Distributions. | Patrick K. Kuiper, Ali Hasan, Wenhao Yang, Yuting Ng, Hoda Bidkhori, Jose H. Blanchet, Vahid Tarokh |
| 2023 | AISTATS | Wasserstein Distributionally Robust Linear-Quadratic Estimation under Martingale Constraints. | Kyriakos Lotidis, Nicholas Bambos, Jose H. Blanchet, Jiajin Li |
| 2023 | ICLR | 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 H. Blanchet |
| 2023 | IJCAI | Dynamic Flows on Curved Space Generated by Labeled Data. | Xinru Hua, Truyen Nguyen, Tam Le, Jose H. Blanchet, Viet Anh Nguyen |
| 2023 | WSC | Statistical Limit Theorems in Distributionally Robust Optimization. | Jose H. Blanchet, Alexander Shapiro |
| 2022 | ICLR | Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality. | Yiping Lu, Haoxuan Chen, Jianfeng Lu, Lexing Ying, Jose H. Blanchet |
| 2022 | ICML | Distributionally Robust Q-Learning. | Zijian Liu, Qinxun Bai, Jose H. Blanchet, Perry Dong, Wei Xu, Zhengqing Zhou, Zhengyuan Zhou |
| 2022 | WSC | Human Imperceptible Attacks and Applications to Improve Fairness. | Xinru Hua, Huanzhong Xu, Jose H. Blanchet, Viet Anh Nguyen |
| 2022 | UAI | Modeling extremes with d-max-decreasing neural networks. | Ali Hasan, Khalil Elkhalil, Yuting Ng, Joo M. Pereira, Sina Farsiu, Jose H. Blanchet, Vahid Tarokh |
| 2021 | AISTATS | Finite-Sample Regret Bound for Distributionally Robust Offline Tabular Reinforcement Learning. | Zhengqing Zhou, Qinxun Bai, Zhengyuan Zhou, Linhai Qiu, Jose H. Blanchet, Peter W. Glynn |
| 2021 | ICML | Testing Group Fairness via Optimal Transport Projections. | Nian Si, Karthyek Murthy, Jose H. Blanchet, Viet Anh Nguyen |
| 2021 | ICML | Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts. | Bahar Taskesen, Man-Chung Yue, Jose H. Blanchet, Daniel Kuhn, Viet Anh Nguyen |
| 2021 | WSC | Measuring Reliability of Object Detection Algorithms for Automated Driving Perception Tasks. | Huanzhong Xu, Jose H. Blanchet, Marcos Paul Gerardo-Castro, Shreyasha Paudel |
| 2020 | ICML | Robust Bayesian Classification Using An Optimistic Score Ratio. | Viet Anh Nguyen, Nian Si, Jose H. Blanchet |
| 2020 | ICML | Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits. | Nian Si, Fan Zhang, Zhengyuan Zhou, Jose H. Blanchet |
| 2020 | WSC | A Class of Optimal Transport Regularized Formulations with Applications to Wasserstein GANs. | Saied Mahdian, Jose H. Blanchet, Peter W. Glynn |
| 2019 | ICML | Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning. | Casey Chu, Jose H. Blanchet, Peter W. Glynn |
| 2019 | WSC | A Distributionally Robust Boosting Algorithm. | Jose H. Blanchet, Fan Zhang, Yang Kang, Zhangyi Hu |
| 2017 | ACML | Distributionally Robust Groupwise Regularization Estimator. | Jose H. Blanchet, Yang Kang |
| 2017 | WSC | Computing worst-case expectations given marginals via simulation. | Jose H. Blanchet, Fei He, Henry Lam |
| 2015 | WSC | Unbiased monte carlo computation of smooth functions of expectations via Taylor expansions. | Jose H. Blanchet, Nan Chen, Peter W. Glynn |
| 2015 | WSC | Unbiased Monte Carlo for optimization and functions of expectations via multi-level randomization. | Jose H. Blanchet, Peter W. Glynn |
| 2015 | WSC | Budget-constrained stochastic approximation. | Uday V. Shanbhag, Jose H. Blanchet |
| 2014 | WSC | Robust rare-event performance analysis with natural non-convex constraints. | Jose H. Blanchet, Christopher Dolan, Henry Lam |
| 2013 | WSC | Efficient splitting-based rare event simulation algorithms for heavy-tailed sums. | Jose H. Blanchet, Yixi Shi |
| 2013 | WSC | Optimal rare event Monte Carlo for Markov modulated regularly varying random walks. | Karthyek R. A. Murthy, Sandeep Juneja, Jose H. Blanchet |
| 2012 | WSC | Sampling point processes on stable unbounded regions and exact simulation of queues. | Jose H. Blanchet, Jing Dong |
| 2011 | WSC | Importance sampling for stochastic recurrence equations with heavy tailed increments. | Jose H. Blanchet, Henrik Hult, Kevin Leder |
| 2011 | WSC | Rare event simulation techniques. | Jose H. Blanchet, Henry Lam |
| 2011 | WSC | Importance sampling for actuarial cost analysis under a heavy traffic model. | Jose H. Blanchet, Henry Lam |
| 2011 | WSC | A conditional Monte Carlo method for estimating the failure probability of a distribution network with random demands. | Jose H. Blanchet, Juan Li, Marvin K. Nakayama |
| 2011 | WSC | Efficient rare event simulation for heavy-tailed systems via cross entropy. | Jose H. Blanchet, Yixi Shi |
| 2010 | WSC | Monte Carlo for large credit portfolios with potentially high correlations. | Jose H. Blanchet, Jingchen Liu, Xuan Yang |
| 2009 | WSC | Efficient Rare Event Simulation of Continuous Time Markovian Perpetuities. | Jose H. Blanchet, Peter W. Glynn |
| 2009 | WSC | Rare Event Simulation for a Generalized Hawkes Process. | Xiaowei Zhang, Peter W. Glynn, Kay Giesecke, Jose H. Blanchet |
| 2008 | WSC | Efficient simulation for tail probabilities of Gaussian random fields. | Robert J. Adler, Jose H. Blanchet, Jingchen Liu |
| 2008 | WSC | Efficient tail estimation for sums of correlated lognormals. | Jose H. Blanchet, Sandeep Juneja, Leonardo Rojas-Nandayapa |
| 2008 | WSC | Large deviations perspective on ordinal optimization of heavy-tailed systems. | Jose H. Blanchet, Jingchen Liu, Bert Zwart |
| 2007 | WSC | Path-sampling for state-dependent importance sampling. | Jose H. Blanchet, Jingchen Liu |
| 2007 | WSC | Rare-event simulation for a multidimensional random walk with | Jose H. Blanchet, Jingchen Liu |
| 2007 | WSC | Importance sampling of compounding processes. | Jose H. Blanchet, Bert Zwart |
| 2007 | WSC | Efficient suboptimal rare-event simulation. | Xiaowei Zhang, Jose H. Blanchet, Peter W. Glynn |
| 2006 | WSC | Efficient simulation for large deviation probabilities of sums of heavy-tailed increments. | Jose H. Blanchet, Jingchen Liu |