State-dependent importance sampling and large deviations. - Jose H. Blanchet; Jingchen Liu; Peter W. Glynn (VALUETOOLS, 2006)

Strongly efficient estimators for light-tailed sums. - Jose H. Blanchet; Peter W. Glynn (VALUETOOLS, 2006)

Efficient simulation for large deviation probabilities of sums of heavy-tailed increments. - Jose H. Blanchet; Jingchen Liu (WSC, 2006)

Path-sampling for state-dependent importance sampling. - Jose H. Blanchet; Jingchen Liu (WSC, 2007)

Rare-event simulation for a multidimensional random walk with t distributed increments. - Jose H. Blanchet; Jingchen Liu (WSC, 2007)

Importance sampling of compounding processes. - Jose H. Blanchet; Bert Zwart (WSC, 2007)

Efficient suboptimal rare-event simulation. - Xiaowei Zhang 004; Jose H. Blanchet; Peter W. Glynn (WSC, 2007)

Fluid heuristics, Lyapunov bounds and efficient importance sampling for a heavy-tailed G/G/1 queue. - Jose H. Blanchet; Peter W. Glynn; Jingchen Liu (Queueing Syst. Theory Appl., 2007)

Editorial: rare-event simulation for queues. - Jose H. Blanchet; Michel Mandjes (Queueing Syst. Theory Appl., 2007)

Efficient simulation for tail probabilities of Gaussian random fields. - Robert J. Adler; Jose H. Blanchet; Jingchen Liu (WSC, 2008)

Efficient tail estimation for sums of correlated lognormals. - Jose H. Blanchet; Sandeep Juneja 001; Leonardo Rojas-Nandayapa (WSC, 2008)

Large deviations perspective on ordinal optimization of heavy-tailed systems. - Jose H. Blanchet; Jingchen Liu; Bert Zwart (WSC, 2008)

Rare Event Simulation for Queues. - José H. Blanchet; Michel Mandjes (Rare Event Simulation using Monte Carlo Methods, 2009)

Rare Event Simulation and Counting Problems. - José H. Blanchet; Daniel Rudoy (Rare Event Simulation using Monte Carlo Methods, 2009)

Efficient Rare Event Simulation of Continuous Time Markovian Perpetuities. - Jose H. Blanchet; Peter W. Glynn (WSC, 2009)

Rare Event Simulation for a Generalized Hawkes Process. - Xiaowei Zhang 004; Peter W. Glynn; Kay Giesecke; Jose H. Blanchet (WSC, 2009)

Rare event simulation for a slotted time M/G/s model. - Jose H. Blanchet; Peter W. Glynn; Henry Lam (Queueing Syst. Theory Appl., 2009)

Monte Carlo for large credit portfolios with potentially high correlations. - Jose H. Blanchet; Jingchen Liu; Xuan Yang (WSC, 2010)

Analysis of a Splitting Estimator for Rare Event Probabilities in Jackson Networks - Jose H. Blanchet; Kevin Leder; Yixi Shi (CoRR, 2010)

Asymptotic expansions of defective renewal equations with applications to perturbed risk models and processor sharing queues. - Jose H. Blanchet; Bert Zwart (Math. Methods Oper. Res., 2010)

Asymptotic robustness of estimators in rare-event simulation. - Pierre L'Ecuyer; Jose H. Blanchet; Bruno Tuffin; Peter W. Glynn (ACM Trans. Model. Comput. Simul., 2010)

Importance sampling for stochastic recurrence equations with heavy tailed increments. - Jose H. Blanchet; Henrik Hult; Kevin Leder (WSC, 2011)

Rare event simulation techniques. - Jose H. Blanchet; Henry Lam (WSC, 2011)

Importance sampling for actuarial cost analysis under a heavy traffic model. - Jose H. Blanchet; Henry Lam (WSC, 2011)

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 (WSC, 2011)

Efficient simulation of tail probabilities of sums of correlated lognormals. - Søren Asmussen; Jose H. Blanchet; Sandeep Juneja 001; Leonardo Rojas-Nandayapa (Ann. Oper. Res., 2011)

Efficient rare event simulation for heavy-tailed compound sums. - Jose H. Blanchet; Chenxin Li (ACM Trans. Model. Comput. Simul., 2011)

Empirical Analysis of a Stochastic Approximation Approach for Computing Quasi-stationary Distributions. - Jose H. Blanchet; Peter W. Glynn; Shuheng Zheng (EVOLVE, 2012)

Sampling point processes on stable unbounded regions and exact simulation of queues. - Jose H. Blanchet; Jing Dong (WSC, 2012)

Rare-event simulation for multi-server queues in the Halfin-Whitt regime. - Jose H. Blanchet; Jing Dong (SIGMETRICS Perform. Evaluation Rev., 2012)

On Lyapunov Inequalities and Subsolutions for Efficient Importance Sampling. - Jose H. Blanchet; Peter W. Glynn; Kevin Leder (ACM Trans. Model. Comput. Simul., 2012)

Stochastic models and control for electrical power line temperature. - Daniel Bienstock; Jose H. Blanchet; Juan Li (Allerton, 2013)

Power line control under uncertainty of ambient temperature. - Jose H. Blanchet; Daniel Bienstock; Juan Li (CDC, 2013)

Efficient splitting-based rare event simulation algorithms for heavy-tailed sums. - Jose H. Blanchet; Yixi Shi (WSC, 2013)

Optimal rare event Monte Carlo for Markov modulated regularly varying random walks. - Karthyek R. A. Murthy; Sandeep Juneja 001; Jose H. Blanchet (WSC, 2013)

Optimal Sampling of Overflow Paths in Jackson Networks. - Jose H. Blanchet (Math. Oper. Res., 2013)

Asymptotics of the area under the graph of a Lévy-driven workload process. - Jose H. Blanchet; Michel Mandjes (Oper. Res. Lett., 2013)

Efficient rare event simulation for heavy-tailed systems via cross entropy. - Jose H. Blanchet; Yixi Shi (Oper. Res. Lett., 2013)

Large deviations for the empirical mean of an M/M/1 queue. - Jose H. Blanchet; Peter W. Glynn; Sean P. Meyn (Queueing Syst. Theory Appl., 2013)

Editorial foreword to special issue on Simulation of Stochastic Networks and related topics. - Jose H. Blanchet; Gareth O. Roberts (Queueing Syst. Theory Appl., 2013)

Characterizing optimal sampling of binary contingency tables via the configuration model. - Jose H. Blanchet; Alexandre Stauffer (Random Struct. Algorithms, 2013)

Rare-event simulation for stochastic recurrence equations with heavy-tailed innovations. - Jose H. Blanchet; Henrik Hult; Kevin Leder (ACM Trans. Model. Comput. Simul., 2013)

Robust rare-event performance analysis with natural non-convex constraints. - Jose H. Blanchet; Christopher Dolan; Henry Lam (WSC, 2014)

Rare-Event Simulation for Many-Server Queues. - Jose H. Blanchet; Henry Lam (Math. Oper. Res., 2014)

Unbiased monte carlo computation of smooth functions of expectations via Taylor expansions. - Jose H. Blanchet; Nan Chen; Peter W. Glynn (WSC, 2015)

Unbiased Monte Carlo for optimization and functions of expectations via multi-level randomization. - Jose H. Blanchet; Peter W. Glynn (WSC, 2015)

Budget-constrained stochastic approximation. - Uday V. Shanbhag; Jose H. Blanchet (WSC, 2015)

Affine Point Processes: Approximation and Efficient Simulation. - Xiaowei Zhang 004; Jose H. Blanchet; Kay Giesecke; Peter W. Glynn (Math. Oper. Res., 2015)

Tail asymptotics for delay in a half-loaded GI/GI/2 queue with heavy-tailed job sizes. - Jose H. Blanchet; Karthyek R. A. Murthy (Queueing Syst. Theory Appl., 2015)

Exact Sampling of Stationary and Time-Reversed Queues. - Jose H. Blanchet; Aya Wallwater (ACM Trans. Model. Comput. Simul., 2015)

A Markov Chain Approximation to Choice Modeling. - Jose H. Blanchet; Guillermo Gallego 001; Vineet Goyal (Oper. Res., 2016)

Distributionally Robust Groupwise Regularization Estimator. - Jose H. Blanchet; Yang Kang (ACML, 2017)

Computing worst-case expectations given marginals via simulation. - Jose H. Blanchet; Fei He; Henry Lam (WSC, 2017)

Bandit Learning with Positive Externalities. - Virag Shah; Jose H. Blanchet; Ramesh Johari (NeurIPS, 2018)

Towards Optimal Running Times for Optimal Transport. - Jose H. Blanchet; Arun Jambulapati; Carson Kent; Aaron Sidford (CoRR, 2018)

Exact simulation of multidimensional reflected Brownian motion. - Jose H. Blanchet; Karthyek Rajhaa A. M. (J. Appl. Probab., 2018)

Perfect sampling of GI/GI/c queues. - Jose H. Blanchet; Jing Dong; Yanan Pei (Queueing Syst. Theory Appl., 2018)

Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning. - Casey Chu; Jose H. Blanchet; Peter W. Glynn (ICML, 2019)

Online EXP3 Learning in Adversarial Bandits with Delayed Feedback. - Ilai Bistritz; Zhengyuan Zhou; Xi Chen; Nicholas Bambos; Jose H. Blanchet (NeurIPS, 2019)

Multivariate Distributionally Robust Convex Regression under Absolute Error Loss. - Jose H. Blanchet; Peter W. Glynn; Jun Yan; Zhengqing Zhou (NeurIPS, 2019)

Semi-Parametric Dynamic Contextual Pricing. - Virag Shah; Ramesh Johari; Jose H. Blanchet (NeurIPS, 2019)

Learning in Generalized Linear Contextual Bandits with Stochastic Delays. - Zhengyuan Zhou; Renyuan Xu; Jose H. Blanchet (NeurIPS, 2019)

Data-Driven Optimal Transport Cost Selection For Distributionally Robust Optimization. - José H. Blanchet; Yang Kang; Karthyek R. A. Murthy; Fan Zhang 059 (WSC, 2019)

A Distributionally Robust Boosting Algorithm. - Jose H. Blanchet; Fan Zhang 059; Yang Kang; Zhangyi Hu (WSC, 2019)

Optimal Transport Relaxations with Application to Wasserstein GANs. - Saied Mahdian; Jose H. Blanchet; Peter W. Glynn (CoRR, 2019)

Rare-Event Simulation for Distribution Networks. - Jose H. Blanchet; Juan Li; Marvin K. Nakayama (Oper. Res., 2019)

Exact sampling of the infinite horizon maximum of a random walk over a nonlinear boundary. - Jose H. Blanchet; Jing Dong; Zhipeng Liu (J. Appl. Probab., 2019)

Robust Wasserstein profile inference and applications to machine learning. - Jose H. Blanchet; Yang Kang; Karthyek Rajhaa A. M. (J. Appl. Probab., 2019)

Perfect Sampling of Generalized Jackson Networks. - Jose H. Blanchet; Xinyun Chen (Math. Oper. Res., 2019)

Quantifying Distributional Model Risk via Optimal Transport. - Jose H. Blanchet; Karthyek R. A. Murthy (Math. Oper. Res., 2019)

Efficient Rare-Event Simulation for Multiple Jump Events in Regularly Varying Random Walks and Compound Poisson Processes. - Bohan Chen; Jose H. Blanchet; Chang-han Rhee; Bert Zwart (Math. Oper. Res., 2019)

Optimal uncertainty size in distributionally robust inverse covariance estimation. - Jose H. Blanchet; Nian Si (Oper. Res. Lett., 2019)

Queue length asymptotics for the multiple-server queue with heavy-tailed Weibull service times. - Mihail Bazhba; Jose H. Blanchet; Chang-han Rhee; Bert Zwart (Queueing Syst. Theory Appl., 2019)

Convergence Rate Analysis of a Stochastic Trust-Region Method via Supermartingales. - Jose H. Blanchet; Coralia Cartis; Matt Menickelly; Katya Scheinberg (INFORMS J. Optim., 2019)

Robust Bayesian Classification Using An Optimistic Score Ratio. - Viet Anh Nguyen; Nian Si; Jose H. Blanchet (ICML, 2020)

Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits. - Nian Si; Fan Zhang 059; Zhengyuan Zhou; Jose H. Blanchet (ICML, 2020)

Distributionally Robust Local Non-parametric Conditional Estimation. - Viet Anh Nguyen; Fan Zhang 059; José H. Blanchet; Erick Delage; Yinyu Ye 001 (NeurIPS, 2020)

Distributionally Robust Parametric Maximum Likelihood Estimation. - Viet Anh Nguyen; Xuhui Zhang; José H. Blanchet; Angelos Georghiou (NeurIPS, 2020)

Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality. - Nian Si; Jose H. Blanchet; Soumyadip Ghosh; Mark S. Squillante (NeurIPS, 2020)

A Class of Optimal Transport Regularized Formulations with Applications to Wasserstein GANs. - Saied Mahdian; Jose H. Blanchet; Peter W. Glynn (WSC, 2020)

Asymptotically Optimal Control of a Centralized Dynamic Matching Market with General Utilities. - Jose H. Blanchet; Martin I. Reiman; Virag Shah; Lawrence M. Wein (CoRR, 2020)

Sequential Batch Learning in Finite-Action Linear Contextual Bandits. - Yanjun Han; Zhengqing Zhou; Zhengyuan Zhou; Jose H. Blanchet; Peter W. Glynn; Yinyu Ye 001 (CoRR, 2020)

Distributional Robust Batch Contextual Bandits. - Nian Si; Fan Zhang 059; Zhengyuan Zhou; Jose H. Blanchet (CoRR, 2020)

A Distributionally Robust Approach to Fair Classification. - Bahar Taskesen; Viet Anh Nguyen; Daniel Kuhn 001; Jose H. Blanchet (CoRR, 2020)

Machine Learning's Dropout Training is Distributionally Robust Optimal. - Jose H. Blanchet; Yang Kang; Jose Luis Montiel Olea; Viet Anh Nguyen; Xuhui Zhang (CoRR, 2020)

Rates of Convergence to Stationarity for Reflected Brownian Motion. - Jose H. Blanchet; Xinyun Chen (Math. Oper. Res., 2020)

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 (AISTATS, 2021)

A Statistical Test for Probabilistic Fairness. - Bahar Taskesen; Jose H. Blanchet; Daniel Kuhn 001; Viet Anh Nguyen (FAccT, 2021)

Testing Group Fairness via Optimal Transport Projections. - Nian Si; Karthyek Murthy; Jose H. Blanchet; Viet Anh Nguyen (ICML, 2021)

Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts. - Bahar Taskesen; Man-Chung Yue; Jose H. Blanchet; Daniel Kuhn 001; Viet Anh Nguyen (ICML, 2021)

Modified Frank Wolfe in Probability Space. - Carson Kent; Jiajin Li; José H. Blanchet; Peter W. Glynn (NeurIPS, 2021)

Adversarial Regression with Doubly Non-negative Weighting Matrices. - Tam Le; Truyen Nguyen; Makoto Yamada; Jose H. Blanchet; Viet Anh Nguyen (NeurIPS, 2021)

Measuring Reliability of Object Detection Algorithms for Automated Driving Perception Tasks. - Huanzhong Xu; Jose H. Blanchet; Marcos Paul Gerardo-Castro; Shreyasha Paudel (WSC, 2021)

Deep Extreme Value Copulas for Estimation and Sampling. - Ali Hasan; Khalil Elkhalil; João M. Pereira 002; Sina Farsiu; Jose H. Blanchet; Vahid Tarokh (CoRR, 2021)

No Discounted-Regret Learning in Adversarial Bandits with Delays. - Ilai Bistritz; Zhengyuan Zhou; Xi Chen; Nicholas Bambos; Jose H. Blanchet (CoRR, 2021)

Statistical Analysis of Wasserstein Distributionally Robust Estimators. - Jose H. Blanchet; Karthyek Murthy; Viet Anh Nguyen (CoRR, 2021)

Human Imperceptible Attacks and Applications to Improve Fairness. - Xinru Hua; Huanzhong Xu; Jose H. Blanchet; Viet Nguyen (CoRR, 2021)

Sample Out-of-Sample Inference Based on Wasserstein Distance. - Jose H. Blanchet; Yang Kang (Oper. Res., 2021)

A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality. - Xuhui Zhang; José H. Blanchet; Soumyadip Ghosh; Mark S. Squillante (AISTATS, 2022)

Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality. - Yiping Lu 001; Haoxuan Chen; Jianfeng Lu 001; Lexing Ying; Jose H. Blanchet (ICLR, 2022)

Distributionally Robust Q-Learning. - Zijian Liu 003; Qinxun Bai; Jose H. Blanchet; Perry Dong; Wei Xu 017; Zhengqing Zhou; Zhengyuan Zhou (ICML, 2022)

Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent. - Yiping Lu 001; José H. Blanchet; Lexing Ying (NeurIPS, 2022)

Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints. - Jiajin Li; Sirui Lin; Jose H. Blanchet; Viet Anh Nguyen (NeurIPS, 2022)

Modeling extremes with d-max-decreasing neural networks. - Ali Hasan; Khalil Elkhalil; Yuting Ng; João M. Pereira 002; Sina Farsiu; Jose H. Blanchet; Vahid Tarokh (UAI, 2022)

Human Imperceptible Attacks and Applications to Improve Fairness. - Xinru Hua; Huanzhong Xu; Jose H. Blanchet; Viet Anh Nguyen (WSC, 2022)

Surgical Scheduling via Optimization and Machine Learning with Long-Tailed Data. - Yuan Shi; Saied Mahdian; Jose H. Blanchet; Peter W. Glynn; Andrew Young Shin; David Scheinker (CoRR, 2022)

The Design and Implementation of a Broadly Applicable Algorithm for Optimizing Intra-Day Surgical Scheduling. - Jin Xie 006; Teng Zhang; Jose H. Blanchet; Peter W. Glynn; Matthew Randolph; David Scheinker (CoRR, 2022)

Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Learning. - Jiajin Li; Jianheng Tang; Lemin Kong; Huikang Liu; Jia Li 009; Anthony Man-Cho So; Jose H. Blanchet (CoRR, 2022)

Distributionally Robust Offline Reinforcement Learning with Linear Function Approximation. - Xiaoteng Ma; Zhipeng Liang; Jose H. Blanchet; Mingwen Liu; Li Xia; Jiheng Zhang; Qianchuan Zhao; Zhengyuan Zhou (CoRR, 2022)

Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls. - Yiping Lu 001; Jiajin Li; Lexing Ying; Jose H. Blanchet (CoRR, 2022)

No Weighted-Regret Learning in Adversarial Bandits with Delays. - Ilai Bistritz; Zhengyuan Zhou; Xi Chen; Nicholas Bambos; José H. Blanchet (J. Mach. Learn. Res., 2022)

Distributionally Robust Mean-Variance Portfolio Selection with Wasserstein Distances. - Jose H. Blanchet; Lin Chen; Xun Yu Zhou (Manag. Sci., 2022)

Optimal Transport-Based Distributionally Robust Optimization: Structural Properties and Iterative Schemes. - Jose H. Blanchet; Karthyek R. A. Murthy; Fan Zhang 059 (Math. Oper. Res., 2022)

Some open problems in exact simulation of stochastic differential equations. - Jose H. Blanchet (Queueing Syst. Theory Appl., 2022)

Asymptotically Optimal Control of a Centralized Dynamic Matching Market with General Utilities. - Jose H. Blanchet; Martin I. Reiman; Virag Shah; Lawrence M. Wein; Linjia Wu (Oper. Res., 2022)

Wasserstein Distributionally Robust Linear-Quadratic Estimation under Martingale Constraints. - Kyriakos Lotidis; Nicholas Bambos; Jose H. Blanchet; Jiajin Li (AISTATS, 2023)

A Finite Sample Complexity Bound for Distributionally Robust Q-learning. - Shengbo Wang; Nian Si; José H. Blanchet; Zhengyuan Zhou (AISTATS, 2023)

Minimax Optimal Kernel Operator Learning via Multilevel Training. - Jikai Jin; Yiping Lu 001; José H. Blanchet; Lexing Ying (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 009; Anthony Man-Cho So; Jose H. Blanchet (ICLR, 2023)

Dynamic Flows on Curved Space Generated by Labeled Data. - Xinru Hua; Truyen Nguyen; Tam Le; Jose H. Blanchet; Viet Anh Nguyen (IJCAI, 2023)

When can Regression-Adjusted Control Variate Help? Rare Events, Sobolev Embedding and Minimax Optimality. - Jose H. Blanchet; Haoxuan Chen; Yiping Lu 001; Lexing Ying (NeurIPS, 2023)

Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage. - Jose H. Blanchet; Miao Lu; Tong Zhang 001; Han Zhong 001 (NeurIPS, 2023)

Payoff-based Learning with Matrix Multiplicative Weights in Quantum Games. - Kyriakos Lotidis; Panayotis Mertikopoulos; Nicholas Bambos; Jose H. Blanchet (NeurIPS, 2023)

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

Statistical Limit Theorems in Distributionally Robust Optimization. - Jose H. Blanchet; Alexander Shapiro 001 (WSC, 2023)

Single-Trajectory Distributionally Robust Reinforcement Learning. - Zhipeng Liang; Xiaoteng Ma; Jose H. Blanchet; Jiheng Zhang; Zhengyuan Zhou (CoRR, 2023)

Optimal Sample Complexity of Reinforcement Learning for Uniformly Ergodic Discounted Markov Decision Processes. - Shengbo Wang; Jose H. Blanchet; Peter W. Glynn (CoRR, 2023)

Neural Network Accelerated Process Design of Polycrystalline Microstructures. - Junrong Lin; Mahmudul Hasan 016; Pinar Acar; José H. Blanchet; Vahid Tarokh (CoRR, 2023)

When can Regression-Adjusted Control Variates Help? Rare Events, Sobolev Embedding and Minimax Optimality. - Jose H. Blanchet; Haoxuan Chen; Yiping Lu 001; Lexing Ying (CoRR, 2023)

Sample Complexity of Variance-reduced Distributionally Robust Q-learning. - Shengbo Wang; Nian Si; Jose H. Blanchet; Zhengyuan Zhou (CoRR, 2023)

On the Foundation of Distributionally Robust Reinforcement Learning. - Shengbo Wang; Nian Si; Jose H. Blanchet; Zhengyuan Zhou (CoRR, 2023)

Dropout Training is Distributionally Robust Optimal. - Jose H. Blanchet; Yang Kang; José Luis Montiel Olea; Viet Anh Nguyen; Xuhui Zhang (J. Mach. Learn. Res., 2023)

Distributionally Robust Batch Contextual Bandits. - Nian Si; Fan Zhang 059; Zhengyuan Zhou; Jose H. Blanchet (Manag. Sci., 2023)

Feasible Q-Learning for Average Reward Reinforcement Learning. - Ying Jin; Ramki Gummadi; Zhengyuan Zhou; Jose H. Blanchet (AISTATS, 2024)

Optimal Sample Complexity for Average Reward Markov Decision Processes. - Shengbo Wang; José H. Blanchet; Peter W. Glynn (ICLR, 2024)

Stability Evaluation through Distributional Perturbation Analysis. - José H. Blanchet; Peng Cui 001; Jiajin Li; Jiashuo Liu (ICML, 2024)

Single-Trajectory Distributionally Robust Reinforcement Learning. - Zhipeng Liang; Xiaoteng Ma; José H. Blanchet; Jun Yang 028; Jiheng Zhang; Zhengyuan Zhou (ICML, 2024)

Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty. - Kaizhao Liu; José H. Blanchet; Lexing Ying; Yiping Lu 001 (ICML, 2024)

Automatic Outlier Rectification via Optimal Transport. - Jose H. Blanchet; Jiajin Li; Markus Pelger; Greg Zanotti (NeurIPS, 2024)

Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer. - Zhihan Liu; Miao Lu; Shenao Zhang; Boyi Liu 001; Hongyi Guo; Yingxiang Yang; Jose H. Blanchet; Zhaoran Wang 001 (NeurIPS, 2024)

Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms. - Miao Lu; Han Zhong 001; Tong Zhang 001; Jose H. Blanchet (NeurIPS, 2024)

Deep Learning for Computing Convergence Rates of Markov Chains. - Yanlin Qu; Jose H. Blanchet; Peter W. Glynn (NeurIPS, 2024)

Consistency of Neural Causal Partial Identification. - Jiyuan Tan; Jose H. Blanchet; Vasilis Syrgkanis (NeurIPS, 2024)

An Efficient High-dimensional Gradient Estimator for Stochastic Differential Equations. - Shengbo Wang; Jose H. Blanchet; Peter W. Glynn (NeurIPS, 2024)

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 (UAI, 2024)

Generative Learning for Simulation of Vehicle Faults. - Patrick K. Kuiper; Sirui Lin; Jose H. Blanchet; Vahid Tarokh (WSC, 2024)

Accelerated Sampling of Rare Events using a Neural Network Bias Potential. - Xinru Hua; Rasool Ahmad; Jose H. Blanchet; Wei Cai (CoRR, 2024)

When are Unbiased Monte Carlo Estimators More Preferable than Biased Ones? - Guanyang Wang; Jose H. Blanchet; Peter W. Glynn (CoRR, 2024)

Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm. - Miao Lu; Han Zhong 001; Tong Zhang 001; Jose H. Blanchet (CoRR, 2024)

Stability Evaluation via Distributional Perturbation Analysis. - Jose H. Blanchet; Peng Cui 001; Jiajin Li; Jiashuo Liu (CoRR, 2024)

Double Distributionally Robust Bid Shading for First Price Auctions. - Yanlin Qu; Ravi Kant; Yan Chen; Brendan Kitts; San Gultekin; Aaron Flores 001; Jose H. Blanchet (CoRR, 2024)

Limit Theorems for Stochastic Gradient Descent with Infinite Variance. - Jose H. Blanchet; Aleksandar Mijatovic; Wenhao Yang (CoRR, 2024)

Delay-Adaptive Learning in Generalized Linear Contextual Bandits. - José H. Blanchet; Renyuan Xu; Zhengyuan Zhou (Math. Oper. Res., 2024)

Towards optimal running timesfor optimal transport. - Jose H. Blanchet; Arun Jambulapati; Carson Kent; Aaron Sidford (Oper. Res. Lett., 2024)

Optimal downsampling for Imbalanced Classification with Generalized Linear Models. - Yan Chen; Jose H. Blanchet; Krzysztof Dembczynski; Laura Fee Nern; Aaron E. Flores 001 (AISTATS, 2025)

ScoreFusion: Fusing Score-based Generative Models via Kullback-Leibler Barycenters. - Hao Liu; Junze Ye; Jose H. Blanchet; Nian Si (AISTATS, 2025)

Statistical Learning of Distributionally Robust Stochastic Control in Continuous State Spaces. - Shengbo Wang; Nian Si; Jose H. Blanchet; Zhengyuan Zhou (AISTATS, 2025)

Epsilon-Minimax Solutions of Statistical Decision Problems via the Hedge Algorithm. - Andres Aradillas Fernandez; Jose H. Blanchet; José Luis Montiel Olea; Chen Qiu; Jörg Stoye; Lezhi Tan (EC, 2025)

Sample-Path Large Deviations for Unbounded Additive Functionals of the Reflected Random Walk. - Mihail Bazhba; Jose H. Blanchet; Chang-Han Rhee; Bert Zwart (Math. Oper. Res., 2025)

Non-linear Quantum Monte Carlo. - Jose H. Blanchet; Yassine Hamoudi; Mario Szegedy; Guanyang Wang (CoRR, 2025)

Learning an Optimal Assortment Policy under Observational Data. - Yuxuan Han; Han Zhong 001; Miao Lu; Jose H. Blanchet; Zhengyuan Zhou (CoRR, 2025)

Fairness-Utility Trade-off via Wasserstein Projection. - Yan Chen; Zheng Tan; Jose H. Blanchet; Hanzhang Qin (CoRR, 2025)

DRO: A Python Library for Distributionally Robust Optimization in Machine Learning. - Jiashuo Liu; Tianyu Wang; Henry Lam; Hongseok Namkoong; Jose H. Blanchet (CoRR, 2025)

Estimation of Treatment Effects in Extreme and Unobserved Data. - Jiyuan Tan; Jose H. Blanchet; Vasilis Syrgkanis (CoRR, 2025)