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ORCIDJose H. Blanchet
José H. Blanchet
Person information
- affiliation: Stanford University, Department of Management Science & Engineering, CA, USA
- affiliation: Columbia University, Department of Statistics, New York City, NY, USA
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2020 – today
- 2024
[j43]José H. Blanchet
, Renyuan Xu, Zhengyuan Zhou:
Delay-Adaptive Learning in Generalized Linear Contextual Bandits. Math. Oper. Res. 49(1): 326-345 (2024)- [j42]Jose H. Blanchet, Arun Jambulapati, Carson Kent, Aaron Sidford:
Towards optimal running timesfor optimal transport. Oper. Res. Lett. 52: 107054 (2024) - [c74]Ying Jin, Ramki Gummadi, Zhengyuan Zhou, Jose H. Blanchet:
Feasible Q-Learning for Average Reward Reinforcement Learning. AISTATS 2024: 1630-1638 - [c73]Shengbo Wang, José H. Blanchet, Peter W. Glynn:
Optimal Sample Complexity for Average Reward Markov Decision Processes. ICLR 2024 - [c72]José H. Blanchet, Peng Cui, Jiajin Li, Jiashuo Liu:
Stability Evaluation through Distributional Perturbation Analysis. ICML 2024 - [c71]Zhipeng Liang, Xiaoteng Ma, José H. Blanchet, Jun Yang, Jiheng Zhang, Zhengyuan Zhou:
Single-Trajectory Distributionally Robust Reinforcement Learning. ICML 2024 - [c70]Kaizhao Liu, José H. Blanchet, Lexing Ying, Yiping Lu:
Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty. ICML 2024 - [i63]Xinru Hua, Rasool Ahmad, Jose H. Blanchet, Wei Cai:
Accelerated Sampling of Rare Events using a Neural Network Bias Potential. CoRR abs/2401.06936 (2024) - [i62]Jose H. Blanchet, Jiajin Li, Markus Pelger, Greg Zanotti:
Automatic Outlier Rectification via Optimal Transport. CoRR abs/2403.14067 (2024) - [i61]Guanyang Wang, Jose H. Blanchet, Peter W. Glynn:
When are Unbiased Monte Carlo Estimators More Preferable than Biased Ones? CoRR abs/2404.01431 (2024) - [i60]Miao Lu, Han Zhong, Tong Zhang, Jose H. Blanchet:
Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm. CoRR abs/2404.03578 (2024) - [i59]Kaizhao Liu, Jose H. Blanchet, Lexing Ying, Yiping Lu:
Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty. CoRR abs/2404.19145 (2024) - [i58]Jose H. Blanchet, Peng Cui, Jiajin Li, Jiashuo Liu:
Stability Evaluation via Distributional Perturbation Analysis. CoRR abs/2405.03198 (2024) - [i57]Jiyuan Tan, Jose H. Blanchet, Vasilis Syrgkanis:
Consistency of Neural Causal Partial Identification. CoRR abs/2405.15673 (2024) - [i56]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 abs/2405.16436 (2024) - [i55]Yanlin Qu, Jose H. Blanchet, Peter W. Glynn:
Deep Learning for Computing Convergence Rates of Markov Chains. CoRR abs/2405.20435 (2024) - [i54]Shengbo Wang, Nian Si, Jose H. Blanchet, Zhengyuan Zhou:
Statistical Learning of Distributionally Robust Stochastic Control in Continuous State Spaces. CoRR abs/2406.11281 (2024) - [i53]Hao Liu, Junze Ye, Jose H. Blanchet, Nian Si:
ScoreFusion: fusing score-based generative models via Kullback-Leibler barycenters. CoRR abs/2406.19619 (2024) - [i52]Patrick K. Kuiper, Sirui Lin, Jose H. Blanchet, Vahid Tarokh:
Generative Learning for Simulation of Vehicle Faults. CoRR abs/2407.17654 (2024) - [i51]Patrick K. Kuiper, Ali Hasan, Wenhao Yang, Yuting Ng, Hoda Bidkhori, Jose H. Blanchet, Vahid Tarokh:
Distributionally Robust Optimization as a Scalable Framework to Characterize Extreme Value Distributions. CoRR abs/2408.00131 (2024) - [i50]Yan Chen, Jose H. Blanchet, Krzysztof Dembczynski, Laura Fee Nern, Aaron Flores:
Optimal Downsampling for Imbalanced Classification with Generalized Linear Models. CoRR abs/2410.08994 (2024) - [i49]Yanlin Qu, Ravi Kant, Yan Chen, Brendan Kitts, San Gultekin, Aaron Flores, Jose H. Blanchet:
Double Distributionally Robust Bid Shading for First Price Auctions. CoRR abs/2410.14864 (2024) - [i48]Jose H. Blanchet, Aleksandar Mijatovic, Wenhao Yang:
Limit Theorems for Stochastic Gradient Descent with Infinite Variance. CoRR abs/2410.16340 (2024) - 2023
- [j41]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: 180:1-180:60 (2023) - [j40]Nian Si, Fan Zhang, Zhengyuan Zhou, Jose H. Blanchet:
Distributionally Robust Batch Contextual Bandits. Manag. Sci. 69(10): 5772-5793 (2023) - [c69]Shengbo Wang, Nian Si, José H. Blanchet, Zhengyuan Zhou:
A Finite Sample Complexity Bound for Distributionally Robust Q-learning. AISTATS 2023: 3370-3398 - [c68]Kyriakos Lotidis, Nicholas Bambos, Jose H. Blanchet, Jiajin Li:
Wasserstein Distributionally Robust Linear-Quadratic Estimation under Martingale Constraints. AISTATS 2023: 8629-8644 - [c67]Jikai Jin, Yiping Lu, José H. Blanchet, Lexing Ying:
Minimax Optimal Kernel Operator Learning via Multilevel Training. ICLR 2023 - [c66]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 - [c65]Xinru Hua, Truyen Nguyen, Tam Le, Jose H. Blanchet, Viet Anh Nguyen:
Dynamic Flows on Curved Space Generated by Labeled Data. IJCAI 2023: 3803-3811 - [c64]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 - [c63]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 - [c62]Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos, Jose H. Blanchet:
Payoff-based Learning with Matrix Multiplicative Weights in Quantum Games. NeurIPS 2023 - [c61]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 - [c60]Jose H. Blanchet, Alexander Shapiro:
Statistical Limit Theorems in Distributionally Robust Optimization. WSC 2023: 31-45 - [i47]Zhipeng Liang, Xiaoteng Ma, Jose H. Blanchet, Jiheng Zhang, Zhengyuan Zhou:
Single-Trajectory Distributionally Robust Reinforcement Learning. CoRR abs/2301.11721 (2023) - [i46]Xinru Hua, Truyen Nguyen, Tam Le, Jose H. Blanchet, Viet Anh Nguyen:
Dynamic Flows on Curved Space Generated by Labeled Data. CoRR abs/2302.00061 (2023) - [i45]Shengbo Wang, Jose H. Blanchet, Peter W. Glynn:
Optimal Sample Complexity of Reinforcement Learning for Uniformly Ergodic Discounted Markov Decision Processes. CoRR abs/2302.07477 (2023) - [i44]Shengbo Wang, Nian Si, Jose H. Blanchet, Zhengyuan Zhou:
A Finite Sample Complexity Bound for Distributionally Robust Q-learning. CoRR abs/2302.13203 (2023) - [i43]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 abs/2303.06595 (2023) - [i42]Junrong Lin, Mahmudul Hasan, Pinar Acar, José H. Blanchet, Vahid Tarokh:
Neural Network Accelerated Process Design of Polycrystalline Microstructures. CoRR abs/2305.00003 (2023) - [i41]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. CoRR abs/2305.09659 (2023) - [i40]Jose H. Blanchet, Haoxuan Chen, Yiping Lu, Lexing Ying:
When can Regression-Adjusted Control Variates Help? Rare Events, Sobolev Embedding and Minimax Optimality. CoRR abs/2305.16527 (2023) - [i39]Shengbo Wang, Nian Si, Jose H. Blanchet, Zhengyuan Zhou:
Sample Complexity of Variance-reduced Distributionally Robust Q-learning. CoRR abs/2305.18420 (2023) - [i38]Shengbo Wang, Jose H. Blanchet, Peter W. Glynn:
Optimal Sample Complexity for Average Reward Markov Decision Processes. CoRR abs/2310.08833 (2023) - [i37]Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos, Jose H. Blanchet:
Payoff-based learning with matrix multiplicative weights in quantum games. CoRR abs/2311.02423 (2023) - [i36]Shengbo Wang, Nian Si, Jose H. Blanchet, Zhengyuan Zhou:
On the Foundation of Distributionally Robust Reinforcement Learning. CoRR abs/2311.09018 (2023) - 2022
- [j39]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): 3355-3370 (2022) - [j38]Ilai Bistritz, Zhengyuan Zhou, Xi Chen, Nicholas Bambos, José H. Blanchet:
No Weighted-Regret Learning in Adversarial Bandits with Delays. J. Mach. Learn. Res. 23: 139:1-139:43 (2022) - [j37]Jose H. Blanchet, Lin Chen, Xun Yu Zhou:
Distributionally Robust Mean-Variance Portfolio Selection with Wasserstein Distances. Manag. Sci. 68(9): 6382-6410 (2022) - [j36]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): 1500-1529 (2022) - [j35]Jose H. Blanchet:
Some open problems in exact simulation of stochastic differential equations. Queueing Syst. Theory Appl. 100(3-4): 509-511 (2022) - [c59]Xuhui Zhang, José H. Blanchet, Soumyadip Ghosh, Mark S. Squillante:
A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality. AISTATS 2022: 3794-3820 - [c58]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 - [c57]Zijian Liu, Qinxun Bai, Jose H. Blanchet, Perry Dong, Wei Xu, Zhengqing Zhou, Zhengyuan Zhou:
Distributionally Robust Q-Learning. ICML 2022: 13623-13643 - [c56]Yiping Lu, José H. Blanchet, Lexing Ying:
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent. NeurIPS 2022 - [c55]Jiajin Li, Sirui Lin, Jose H. Blanchet, Viet Anh Nguyen:
Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints. NeurIPS 2022 - [c54]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: 759-768 - [c53]Xinru Hua, Huanzhong Xu, Jose H. Blanchet, Viet Anh Nguyen:
Human Imperceptible Attacks and Applications to Improve Fairness. WSC 2022: 2641-2652 - [i35]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 abs/2202.06383 (2022) - [i34]Xuhui Zhang, Jose H. Blanchet, Soumyadip Ghosh, Mark S. Squillante:
A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality. CoRR abs/2202.11685 (2022) - [i33]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 abs/2203.08146 (2022) - [i32]Yiping Lu, Jose H. Blanchet, Lexing Ying:
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent. CoRR abs/2205.07331 (2022) - [i31]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 abs/2205.08115 (2022) - [i30]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 abs/2209.06620 (2022) - [i29]Jikai Jin, Yiping Lu, Jose H. Blanchet, Lexing Ying:
Minimax Optimal Kernel Operator Learning via Multilevel Training. CoRR abs/2209.14430 (2022) - [i28]Jiajin Li, Sirui Lin, Jose H. Blanchet, Viet Anh Nguyen:
Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints. CoRR abs/2210.01413 (2022) - [i27]Yiping Lu, Jiajin Li, Lexing Ying, Jose H. Blanchet:
Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls. CoRR abs/2211.15241 (2022) - 2021
- [j34]Jose H. Blanchet, Yang Kang:
Sample Out-of-Sample Inference Based on Wasserstein Distance. Oper. Res. 69(3): 985-1013 (2021) - [c52]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: 3331-3339 - [c51]Bahar Taskesen, Jose H. Blanchet, Daniel Kuhn, Viet Anh Nguyen:
A Statistical Test for Probabilistic Fairness. FAccT 2021: 648-665 - [c50]Nian Si, Karthyek Murthy, Jose H. Blanchet, Viet Anh Nguyen:
Testing Group Fairness via Optimal Transport Projections. ICML 2021: 9649-9659 - [c49]Bahar Taskesen, Man-Chung Yue, Jose H. Blanchet, Daniel Kuhn, Viet Anh Nguyen:
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts. ICML 2021: 10162-10172 - [c48]Carson Kent, Jiajin Li, José H. Blanchet, Peter W. Glynn:
Modified Frank Wolfe in Probability Space. NeurIPS 2021: 14448-14462 - [c47]Tam Le, Truyen Nguyen, Makoto Yamada, Jose H. Blanchet, Viet Anh Nguyen:
Adversarial Regression with Doubly Non-negative Weighting Matrices. NeurIPS 2021: 16964-16976 - [c46]Huanzhong Xu, Jose H. Blanchet, Marcos Paul Gerardo-Castro, Shreyasha Paudel:
Measuring Reliability of Object Detection Algorithms for Automated Driving Perception Tasks. WSC 2021: 1-12 - [i26]Ali Hasan, Khalil Elkhalil, João M. Pereira, Sina Farsiu, Jose H. Blanchet, Vahid Tarokh:
Deep Extreme Value Copulas for Estimation and Sampling. CoRR abs/2102.09042 (2021) - [i25]Ilai Bistritz, Zhengyuan Zhou, Xi Chen, Nicholas Bambos, Jose H. Blanchet:
No Discounted-Regret Learning in Adversarial Bandits with Delays. CoRR abs/2103.04550 (2021) - [i24]Bahar Taskesen, Man-Chung Yue, Jose H. Blanchet, Daniel Kuhn, Viet Anh Nguyen:
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts. CoRR abs/2106.00322 (2021) - [i23]Nian Si, Karthyek Murthy, Jose H. Blanchet, Viet Anh Nguyen:
Testing Group Fairness via Optimal Transport Projections. CoRR abs/2106.01070 (2021) - [i22]Jose H. Blanchet, Karthyek Murthy, Viet Anh Nguyen:
Statistical Analysis of Wasserstein Distributionally Robust Estimators. CoRR abs/2108.02120 (2021) - [i21]Tam Le, Truyen Nguyen, Makoto Yamada, Jose H. Blanchet, Viet Anh Nguyen:
Adversarial Regression with Doubly Non-negative Weighting Matrices. CoRR abs/2109.14875 (2021) - [i20]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. CoRR abs/2110.06897 (2021) - [i19]Xinru Hua, Huanzhong Xu, Jose H. Blanchet, Viet Nguyen:
Human Imperceptible Attacks and Applications to Improve Fairness. CoRR abs/2111.15603 (2021) - 2020
- [j33]Jose H. Blanchet, Xinyun Chen:
Rates of Convergence to Stationarity for Reflected Brownian Motion. Math. Oper. Res. 45(2): 660-681 (2020) - [c45]Viet Anh Nguyen, Nian Si, Jose H. Blanchet:
Robust Bayesian Classification Using An Optimistic Score Ratio. ICML 2020: 7327-7337 - [c44]Nian Si, Fan Zhang, Zhengyuan Zhou, Jose H. Blanchet:
Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits. ICML 2020: 8884-8894 - [c43]Viet Anh Nguyen, Fan Zhang, José H. Blanchet, Erick Delage, Yinyu Ye:
Distributionally Robust Local Non-parametric Conditional Estimation. NeurIPS 2020 - [c42]Viet Anh Nguyen, Xuhui Zhang, José H. Blanchet, Angelos Georghiou:
Distributionally Robust Parametric Maximum Likelihood Estimation. NeurIPS 2020 - [c41]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 - [c40]Saied Mahdian, Jose H. Blanchet, Peter W. Glynn:
A Class of Optimal Transport Regularized Formulations with Applications to Wasserstein GANs. WSC 2020: 433-444 - [i18]Jose H. Blanchet, Martin I. Reiman, Virag Shah, Lawrence M. Wein:
Asymptotically Optimal Control of a Centralized Dynamic Matching Market with General Utilities. CoRR abs/2002.03205 (2020) - [i17]Jose H. Blanchet, Renyuan Xu, Zhengyuan Zhou:
Delay-Adaptive Learning in Generalized Linear Contextual Bandits. CoRR abs/2003.05174 (2020) - [i16]Yanjun Han, Zhengqing Zhou, Zhengyuan Zhou, Jose H. Blanchet, Peter W. Glynn, Yinyu Ye:
Sequential Batch Learning in Finite-Action Linear Contextual Bandits. CoRR abs/2004.06321 (2020) - [i15]Nian Si, Fan Zhang, Zhengyuan Zhou, Jose H. Blanchet:
Distributional Robust Batch Contextual Bandits. CoRR abs/2006.05630 (2020) - [i14]Viet Anh Nguyen, Nian Si, Jose H. Blanchet:
Robust Bayesian Classification Using an Optimistic Score Ratio. CoRR abs/2007.04458 (2020) - [i13]Bahar Taskesen, Viet Anh Nguyen, Daniel Kuhn, Jose H. Blanchet:
A Distributionally Robust Approach to Fair Classification. CoRR abs/2007.09530 (2020) - [i12]Jose H. Blanchet, Yang Kang, Jose Luis Montiel Olea, Viet Anh Nguyen, Xuhui Zhang:
Machine Learning's Dropout Training is Distributionally Robust Optimal. CoRR abs/2009.06111 (2020) - [i11]Viet Anh Nguyen, Xuhui Zhang, José H. Blanchet, Angelos Georghiou:
Distributionally Robust Parametric Maximum Likelihood Estimation. CoRR abs/2010.05321 (2020) - [i10]Viet Anh Nguyen, Fan Zhang, José H. Blanchet, Erick Delage, Yinyu Ye:
Distributionally Robust Local Non-parametric Conditional Estimation. CoRR abs/2010.05373 (2020) - [i9]Bahar Taskesen, Jose H. Blanchet, Daniel Kuhn, Viet Anh Nguyen:
A Statistical Test for Probabilistic Fairness. CoRR abs/2012.04800 (2020)
2010 – 2019
- 2019
- [j32]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): 92-119 (2019) - [j31]Jose H. Blanchet, Juan Li, Marvin K. Nakayama:
Rare-Event Simulation for Distribution Networks. Oper. Res. 67(5): 1383-1396 (2019) - [j30]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): 116-138 (2019) - [j29]Jose H. Blanchet, Yang Kang, Karthyek Rajhaa A. M.:
Robust Wasserstein profile inference and applications to machine learning. J. Appl. Probab. 56(3): 830-857 (2019) - [j28]Jose H. Blanchet, Karthyek R. A. Murthy:
Quantifying Distributional Model Risk via Optimal Transport. Math. Oper. Res. 44(2): 565-600 (2019) - [j27]Jose H. Blanchet, Xinyun Chen:
Perfect Sampling of Generalized Jackson Networks. Math. Oper. Res. 44(2): 693-714 (2019) - [j26]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): 919-942 (2019) - [j25]Jose H. Blanchet, Nian Si:
Optimal uncertainty size in distributionally robust inverse covariance estimation. Oper. Res. Lett. 47(6): 618-621 (2019) - [j24]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): 195-226 (2019) - [c39]Casey Chu, Jose H. Blanchet, Peter W. Glynn:
Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning. ICML 2019: 1213-1222 - [c38]Virag Shah, Ramesh Johari, Jose H. Blanchet:
Semi-Parametric Dynamic Contextual Pricing. NeurIPS 2019: 2360-2370 - [c37]Zhengyuan Zhou, Renyuan Xu, Jose H. Blanchet:
Learning in Generalized Linear Contextual Bandits with Stochastic Delays. NeurIPS 2019: 5198-5209 - [c36]Ilai Bistritz, Zhengyuan Zhou, Xi Chen, Nicholas Bambos, Jose H. Blanchet:
Online EXP3 Learning in Adversarial Bandits with Delayed Feedback. NeurIPS 2019: 11345-11354 - [c35]Jose H. Blanchet, Peter W. Glynn, Jun Yan, Zhengqing Zhou:
Multivariate Distributionally Robust Convex Regression under Absolute Error Loss. NeurIPS 2019: 11794-11803 - [c34]Jose H. Blanchet, Fan Zhang, Yang Kang, Zhangyi Hu:
A Distributionally Robust Boosting Algorithm. WSC 2019: 3728-3739 - [c33]José H. Blanchet, Yang Kang, Karthyek R. A. Murthy, Fan Zhang:
Data-Driven Optimal Transport Cost Selection For Distributionally Robust Optimization. WSC 2019: 3740-3751 - [i8]Virag Shah, Jose H. Blanchet, Ramesh Johari:
Semi-parametric dynamic contextual pricing. CoRR abs/1901.02045 (2019) - [i7]Casey Chu, Jose H. Blanchet, Peter W. Glynn:
Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning. CoRR abs/1901.10691 (2019) - [i6]Jose H. Blanchet, Yang Kang, Fan Zhang, Zhangyi Hu:
A Distributionally Robust Boosting Algorithm. CoRR abs/1905.07845 (2019) - [i5]Saied Mahdian, Jose H. Blanchet, Peter W. Glynn:
Optimal Transport Relaxations with Application to Wasserstein GANs. CoRR abs/1906.03317 (2019) - 2018
- [j23]Jose H. Blanchet, Karthyek Rajhaa A. M.:
Exact simulation of multidimensional reflected Brownian motion. J. Appl. Probab. 55(1): 137-156 (2018) - [j22]Jose H. Blanchet, Jing Dong, Yanan Pei:
Perfect sampling of GI/GI/c queues. Queueing Syst. Theory Appl. 90(1-2): 1-33 (2018) - [c32]Virag Shah, Jose H. Blanchet, Ramesh Johari:
Bandit Learning with Positive Externalities. NeurIPS 2018: 4923-4933 - [i4]Virag Shah, Jose H. Blanchet, Ramesh Johari:
Bandit Learning with Positive Externalities. CoRR abs/1802.05693 (2018) - [i3]Jose H. Blanchet, Arun Jambulapati, Carson Kent, Aaron Sidford:
Towards Optimal Running Times for Optimal Transport. CoRR abs/1810.07717 (2018) - 2017
- [c31]Jose H. Blanchet, Yang Kang:
Distributionally Robust Groupwise Regularization Estimator. ACML 2017: 97-112 - [c30]Jose H. Blanchet, Fei He, Henry Lam:
Computing worst-case expectations given marginals via simulation. WSC 2017: 2315-2323 - 2016
- [j21]Jose H. Blanchet, Guillermo Gallego, Vineet Goyal:
A Markov Chain Approximation to Choice Modeling. Oper. Res. 64(4): 886-905 (2016) - 2015
- [j20]Xiaowei Zhang, Jose H. Blanchet, Kay Giesecke, Peter W. Glynn:
Affine Point Processes: Approximation and Efficient Simulation. Math. Oper. Res. 40(4): 797-819 (2015) - [j19]Jose H. Blanchet, Karthyek R. A. Murthy:
Tail asymptotics for delay in a half-loaded GI/GI/2 queue with heavy-tailed job sizes. Queueing Syst. Theory Appl. 81(4): 301-340 (2015) - [j18]Jose H. Blanchet, Aya Wallwater:
Exact Sampling of Stationary and Time-Reversed Queues. ACM Trans. Model. Comput. Simul. 25(4): 26:1-26:27 (2015) - [c29]Jose H. Blanchet, Nan Chen, Peter W. Glynn:
Unbiased monte carlo computation of smooth functions of expectations via Taylor expansions. WSC 2015: 360-367 - [c28]Uday V. Shanbhag, Jose H. Blanchet:
Budget-constrained stochastic approximation. WSC 2015: 368-379 - [c27]Jose H. Blanchet, Peter W. Glynn:
Unbiased Monte Carlo for optimization and functions of expectations via multi-level randomization. WSC 2015: 3656-3667 - 2014
- [j17]Jose H. Blanchet, Henry Lam:
Rare-Event Simulation for Many-Server Queues. Math. Oper. Res. 39(4): 1142-1178 (2014) - [c26]Jose H. Blanchet, Christopher Dolan, Henry Lam:
Robust rare-event performance analysis with natural non-convex constraints. WSC 2014: 595-603 - 2013
- [j16]Jose H. Blanchet:
Optimal Sampling of Overflow Paths in Jackson Networks. Math. Oper. Res. 38(4): 698-719 (2013) - [j15]Jose H. Blanchet, Yixi Shi:
Efficient rare event simulation for heavy-tailed systems via cross entropy. Oper. Res. Lett. 41(3): 271-276 (2013) - [j14]Jose H. Blanchet, Michel Mandjes:
Asymptotics of the area under the graph of a Lévy-driven workload process. Oper. Res. Lett. 41(6): 730-736 (2013) - [j13]Jose H. Blanchet, Gareth O. Roberts:
Editorial foreword to special issue on Simulation of Stochastic Networks and related topics. Queueing Syst. Theory Appl. 73(4): 341-343 (2013) - [j12]Jose H. Blanchet, Peter W. Glynn, Sean P. Meyn:
Large deviations for the empirical mean of an M/M/1 queue. Queueing Syst. Theory Appl. 73(4): 425-446 (2013) - [j11]Jose H. Blanchet, Alexandre Stauffer:
Characterizing optimal sampling of binary contingency tables via the configuration model. Random Struct. Algorithms 42(2): 159-184 (2013) - [j10]Jose H. Blanchet, Henrik Hult, Kevin Leder:
Rare-event simulation for stochastic recurrence equations with heavy-tailed innovations. ACM Trans. Model. Comput. Simul. 23(4): 22:1-22:25 (2013) - [c25]Daniel Bienstock, Jose H. Blanchet, Juan Li:
Stochastic models and control for electrical power line temperature. Allerton 2013: 1344-1348 - [c24]Jose H. Blanchet, Daniel Bienstock, Juan Li:
Power line control under uncertainty of ambient temperature. CDC 2013: 4977-4982 - [c23]Jose H. Blanchet, Guillermo Gallego, Vineet Goyal:
A markov chain approximation to choice modeling. EC 2013: 103-104 - [c22]Karthyek R. A. Murthy, Sandeep Juneja, Jose H. Blanchet:
Optimal rare event Monte Carlo for Markov modulated regularly varying random walks. WSC 2013: 564-576 - [c21]Jose H. Blanchet, Yixi Shi:
Efficient splitting-based rare event simulation algorithms for heavy-tailed sums. WSC 2013: 724-735 - 2012
- [j9]Jose H. Blanchet, Jing Dong:
Rare-event simulation for multi-server queues in the Halfin-Whitt regime. SIGMETRICS Perform. Evaluation Rev. 39(4): 35 (2012) - [j8]Jose H. Blanchet, Peter W. Glynn, Kevin Leder:
On Lyapunov Inequalities and Subsolutions for Efficient Importance Sampling. ACM Trans. Model. Comput. Simul. 22(3): 13:1-13:27 (2012) - [c20]Jose H. Blanchet, Peter W. Glynn, Shuheng Zheng:
Empirical Analysis of a Stochastic Approximation Approach for Computing Quasi-stationary Distributions. EVOLVE 2012: 19-37 - [c19]Jose H. Blanchet, Jing Dong:
Sampling point processes on stable unbounded regions and exact simulation of queues. WSC 2012: 11:1-11:12 - 2011
- [j7]Søren Asmussen, Jose H. Blanchet, Sandeep Juneja, Leonardo Rojas-Nandayapa:
Efficient simulation of tail probabilities of sums of correlated lognormals. Ann. Oper. Res. 189(1): 5-23 (2011) - [j6]Jose H. Blanchet, Chenxin Li:
Efficient rare event simulation for heavy-tailed compound sums. ACM Trans. Model. Comput. Simul. 21(2): 9:1-9:23 (2011) - [c18]Jose H. Blanchet, Henry Lam:
Rare event simulation techniques. WSC 2011: 146-160 - [c17]Jose H. Blanchet, Yixi Shi:
Efficient rare event simulation for heavy-tailed systems via cross entropy. WSC 2011: 516-527 - [c16]Jose H. Blanchet, Henry Lam:
Importance sampling for actuarial cost analysis under a heavy traffic model. WSC 2011: 3817-3828 - [c15]Jose H. Blanchet, Henrik Hult, Kevin Leder:
Importance sampling for stochastic recurrence equations with heavy tailed increments. WSC 2011: 3829-3836 - [c14]Jose H. Blanchet, Juan Li, Marvin K. Nakayama:
A conditional Monte Carlo method for estimating the failure probability of a distribution network with random demands. WSC 2011: 3837-3848 - 2010
- [j5]Jose H. Blanchet, Bert Zwart:
Asymptotic expansions of defective renewal equations with applications to perturbed risk models and processor sharing queues. Math. Methods Oper. Res. 72(2): 311-326 (2010) - [j4]Pierre L'Ecuyer, Jose H. Blanchet, Bruno Tuffin, Peter W. Glynn:
Asymptotic robustness of estimators in rare-event simulation. ACM Trans. Model. Comput. Simul. 20(1): 6:1-6:41 (2010) - [c13]Jose H. Blanchet, Jingchen Liu, Xuan Yang:
Monte Carlo for large credit portfolios with potentially high correlations. WSC 2010: 2810-2820 - [i2]Jose H. Blanchet, Alexandre Stauffer:
Characterizing Optimal Sampling of Binary Contingency Tables via the Configuration Model. CoRR abs/1007.1214 (2010) - [i1]Jose H. Blanchet, Kevin Leder, Yixi Shi:
Analysis of a Splitting Estimator for Rare Event Probabilities in Jackson Networks. CoRR abs/1007.5030 (2010)
2000 – 2009
- 2009
- [j3]Jose H. Blanchet, Peter W. Glynn, Henry Lam:
Rare event simulation for a slotted time M/G/s model. Queueing Syst. Theory Appl. 63(1-4): 33-57 (2009) - [c12]Jose H. Blanchet, Peter W. Glynn:
Efficient Rare Event Simulation of Continuous Time Markovian Perpetuities. WSC 2009: 444-451 - [c11]Xiaowei Zhang, Peter W. Glynn, Kay Giesecke, Jose H. Blanchet:
Rare Event Simulation for a Generalized Hawkes Process. WSC 2009: 1291-1298 - [p2]José H. Blanchet, Michel Mandjes:
Rare Event Simulation for Queues. Rare Event Simulation using Monte Carlo Methods 2009: 87-124 - [p1]José H. Blanchet, Daniel Rudoy:
Rare Event Simulation and Counting Problems. Rare Event Simulation using Monte Carlo Methods 2009: 171-192 - 2008
- [c10]Robert J. Adler, Jose H. Blanchet, Jingchen Liu:
Efficient simulation for tail probabilities of Gaussian random fields. WSC 2008: 328-336 - [c9]Jose H. Blanchet, Jingchen Liu, Bert Zwart:
Large deviations perspective on ordinal optimization of heavy-tailed systems. WSC 2008: 489-494 - [c8]Jose H. Blanchet, Sandeep Juneja, Leonardo Rojas-Nandayapa:
Efficient tail estimation for sums of correlated lognormals. WSC 2008: 607-614 - 2007
- [j2]Jose H. Blanchet, Michel Mandjes:
Editorial: rare-event simulation for queues. Queueing Syst. Theory Appl. 57(2-3): 57-59 (2007) - [j1]Jose H. Blanchet, Peter W. Glynn, Jingchen Liu:
Fluid heuristics, Lyapunov bounds and efficient importance sampling for a heavy-tailed G/G/1 queue. Queueing Syst. Theory Appl. 57(2-3): 99-113 (2007) - [c7]Jose H. Blanchet, Bert Zwart:
Importance sampling of compounding processes. WSC 2007: 372-379 - [c6]Jose H. Blanchet, Jingchen Liu:
Path-sampling for state-dependent importance sampling. WSC 2007: 380-388 - [c5]Xiaowei Zhang, Jose H. Blanchet, Peter W. Glynn:
Efficient suboptimal rare-event simulation. WSC 2007: 389-394 - [c4]Jose H. Blanchet, Jingchen Liu:
Rare-event simulation for a multidimensional random walk with t distributed increments. WSC 2007: 395-402 - 2006
- [c3]Jose H. Blanchet, Peter W. Glynn:
Strongly efficient estimators for light-tailed sums. VALUETOOLS 2006: 18 - [c2]Jose H. Blanchet, Jingchen Liu, Peter W. Glynn:
State-dependent importance sampling and large deviations. VALUETOOLS 2006: 20 - [c1]Jose H. Blanchet, Jingchen Liu:
Efficient simulation for large deviation probabilities of sums of heavy-tailed increments. WSC 2006: 757-764
Coauthor Index
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