Jose H. Blanchet

Orcid: 0000-0001-5895-0912

Affiliations:
  • Stanford University, Department of Management Science & Engineering, CA, USA
  • Columbia University, Department of Statistics, New York City, NY, USA


According to our database1, Jose H. Blanchet authored at least 138 papers between 2006 and 2024.

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Bibliography

2024
Automatic Outlier Rectification via Optimal Transport.
CoRR, 2024

Accelerated Sampling of Rare Events using a Neural Network Bias Potential.
CoRR, 2024

2023
Distributionally Robust Batch Contextual Bandits.
Manag. Sci., October, 2023

Dropout Training is Distributionally Robust Optimal.
J. Mach. Learn. Res., 2023

On the Foundation of Distributionally Robust Reinforcement Learning.
CoRR, 2023

Optimal Sample Complexity for Average Reward Markov Decision Processes.
CoRR, 2023

Sample Complexity of Variance-reduced Distributionally Robust Q-learning.
CoRR, 2023

When can Regression-Adjusted Control Variates Help? Rare Events, Sobolev Embedding and Minimax Optimality.
CoRR, 2023

Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage.
CoRR, 2023

Neural Network Accelerated Process Design of Polycrystalline Microstructures.
CoRR, 2023

Optimal Sample Complexity of Reinforcement Learning for Uniformly Ergodic Discounted Markov Decision Processes.
CoRR, 2023

Single-Trajectory Distributionally Robust Reinforcement Learning.
CoRR, 2023

Statistical Limit Theorems in Distributionally Robust Optimization.
Proceedings of the Winter Simulation Conference, 2023

Universal Gradient Descent Ascent Method for Nonconvex-Nonconcave Minimax Optimization.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Payoff-based Learning with Matrix Multiplicative Weights in Quantum Games.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

When can Regression-Adjusted Control Variate Help? Rare Events, Sobolev Embedding and Minimax Optimality.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Dynamic Flows on Curved Space Generated by Labeled Data.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Minimax Optimal Kernel Operator Learning via Multilevel Training.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

A Finite Sample Complexity Bound for Distributionally Robust Q-learning.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Wasserstein Distributionally Robust Linear-Quadratic Estimation under Martingale Constraints.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Asymptotically Optimal Control of a Centralized Dynamic Matching Market with General Utilities.
Oper. Res., November, 2022

Some open problems in exact simulation of stochastic differential equations.
Queueing Syst. Theory Appl., 2022

Optimal Transport-Based Distributionally Robust Optimization: Structural Properties and Iterative Schemes.
Math. Oper. Res., 2022

Distributionally Robust Mean-Variance Portfolio Selection with Wasserstein Distances.
Manag. Sci., 2022

No Weighted-Regret Learning in Adversarial Bandits with Delays.
J. Mach. Learn. Res., 2022

Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls.
CoRR, 2022

Distributionally Robust Offline Reinforcement Learning with Linear Function Approximation.
CoRR, 2022

Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Learning.
CoRR, 2022

The Design and Implementation of a Broadly Applicable Algorithm for Optimizing Intra-Day Surgical Scheduling.
CoRR, 2022

Surgical Scheduling via Optimization and Machine Learning with Long-Tailed Data.
CoRR, 2022

Human Imperceptible Attacks and Applications to Improve Fairness.
Proceedings of the Winter Simulation Conference, 2022

Modeling extremes with d-max-decreasing neural networks.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Distributionally Robust Q-Learning.
Proceedings of the International Conference on Machine Learning, 2022

Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality.
Proceedings of the Tenth International Conference on Learning Representations, 2022

A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Sample Out-of-Sample Inference Based on Wasserstein Distance.
Oper. Res., 2021

Human Imperceptible Attacks and Applications to Improve Fairness.
CoRR, 2021

Statistical Analysis of Wasserstein Distributionally Robust Estimators.
CoRR, 2021

No Discounted-Regret Learning in Adversarial Bandits with Delays.
CoRR, 2021

Deep Extreme Value Copulas for Estimation and Sampling.
CoRR, 2021

Measuring Reliability of Object Detection Algorithms for Automated Driving Perception Tasks.
Proceedings of the Winter Simulation Conference, 2021

Adversarial Regression with Doubly Non-negative Weighting Matrices.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Modified Frank Wolfe in Probability Space.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts.
Proceedings of the 38th International Conference on Machine Learning, 2021

Testing Group Fairness via Optimal Transport Projections.
Proceedings of the 38th International Conference on Machine Learning, 2021

A Statistical Test for Probabilistic Fairness.
Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

Finite-Sample Regret Bound for Distributionally Robust Offline Tabular Reinforcement Learning.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Rates of Convergence to Stationarity for Reflected Brownian Motion.
Math. Oper. Res., 2020

Machine Learning's Dropout Training is Distributionally Robust Optimal.
CoRR, 2020

A Distributionally Robust Approach to Fair Classification.
CoRR, 2020

Distributional Robust Batch Contextual Bandits.
CoRR, 2020

Sequential Batch Learning in Finite-Action Linear Contextual Bandits.
CoRR, 2020

Delay-Adaptive Learning in Generalized Linear Contextual Bandits.
CoRR, 2020

Asymptotically Optimal Control of a Centralized Dynamic Matching Market with General Utilities.
CoRR, 2020

A Class of Optimal Transport Regularized Formulations with Applications to Wasserstein GANs.
Proceedings of the Winter Simulation Conference, 2020

Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Distributionally Robust Parametric Maximum Likelihood Estimation.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Distributionally Robust Local Non-parametric Conditional Estimation.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits.
Proceedings of the 37th International Conference on Machine Learning, 2020

Robust Bayesian Classification Using An Optimistic Score Ratio.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Convergence Rate Analysis of a Stochastic Trust-Region Method via Supermartingales.
INFORMS J. Optim., April, 2019

Queue length asymptotics for the multiple-server queue with heavy-tailed Weibull service times.
Queueing Syst. Theory Appl., 2019

Optimal uncertainty size in distributionally robust inverse covariance estimation.
Oper. Res. Lett., 2019

Efficient Rare-Event Simulation for Multiple Jump Events in Regularly Varying Random Walks and Compound Poisson Processes.
Math. Oper. Res., 2019

Quantifying Distributional Model Risk via Optimal Transport.
Math. Oper. Res., 2019

Perfect Sampling of Generalized Jackson Networks.
Math. Oper. Res., 2019

Robust Wasserstein profile inference and applications to machine learning.
J. Appl. Probab., 2019

Exact sampling of the infinite horizon maximum of a random walk over a nonlinear boundary.
J. Appl. Probab., 2019

Rare-Event Simulation for Distribution Networks.
Oper. Res., 2019

Optimal Transport Relaxations with Application to Wasserstein GANs.
CoRR, 2019

A Distributionally Robust Boosting Algorithm.
Proceedings of the 2019 Winter Simulation Conference, 2019

Data-Driven Optimal Transport Cost Selection For Distributionally Robust Optimization.
Proceedings of the 2019 Winter Simulation Conference, 2019

Learning in Generalized Linear Contextual Bandits with Stochastic Delays.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Semi-Parametric Dynamic Contextual Pricing.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Multivariate Distributionally Robust Convex Regression under Absolute Error Loss.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Online EXP3 Learning in Adversarial Bandits with Delayed Feedback.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
Perfect sampling of GI/GI/c queues.
Queueing Syst. Theory Appl., 2018

Exact simulation of multidimensional reflected Brownian motion.
J. Appl. Probab., 2018

Towards Optimal Running Times for Optimal Transport.
CoRR, 2018

Bandit Learning with Positive Externalities.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017
Computing worst-case expectations given marginals via simulation.
Proceedings of the 2017 Winter Simulation Conference, 2017

Distributionally Robust Groupwise Regularization Estimator.
Proceedings of The 9th Asian Conference on Machine Learning, 2017

2016
A Markov Chain Approximation to Choice Modeling.
Oper. Res., 2016

2015
Exact Sampling of Stationary and Time-Reversed Queues.
ACM Trans. Model. Comput. Simul., 2015

Tail asymptotics for delay in a half-loaded GI/GI/2 queue with heavy-tailed job sizes.
Queueing Syst. Theory Appl., 2015

Affine Point Processes: Approximation and Efficient Simulation.
Math. Oper. Res., 2015

Budget-constrained stochastic approximation.
Proceedings of the 2015 Winter Simulation Conference, 2015

Unbiased Monte Carlo for optimization and functions of expectations via multi-level randomization.
Proceedings of the 2015 Winter Simulation Conference, 2015

Unbiased monte carlo computation of smooth functions of expectations via Taylor expansions.
Proceedings of the 2015 Winter Simulation Conference, 2015

2014
Rare-Event Simulation for Many-Server Queues.
Math. Oper. Res., 2014

Robust rare-event performance analysis with natural non-convex constraints.
Proceedings of the 2014 Winter Simulation Conference, 2014

2013
Rare-event simulation for stochastic recurrence equations with heavy-tailed innovations.
ACM Trans. Model. Comput. Simul., 2013

Characterizing optimal sampling of binary contingency tables via the configuration model.
Random Struct. Algorithms, 2013

Editorial foreword to special issue on Simulation of Stochastic Networks and related topics.
Queueing Syst. Theory Appl., 2013

Large deviations for the empirical mean of an M/M/1 queue.
Queueing Syst. Theory Appl., 2013

Efficient rare event simulation for heavy-tailed systems via cross entropy.
Oper. Res. Lett., 2013

Asymptotics of the area under the graph of a Lévy-driven workload process.
Oper. Res. Lett., 2013

Optimal Sampling of Overflow Paths in Jackson Networks.
Math. Oper. Res., 2013

Optimal rare event Monte Carlo for Markov modulated regularly varying random walks.
Proceedings of the Winter Simulations Conference: Simulation Making Decisions in a Complex World, 2013

Efficient splitting-based rare event simulation algorithms for heavy-tailed sums.
Proceedings of the Winter Simulations Conference: Simulation Making Decisions in a Complex World, 2013

Power line control under uncertainty of ambient temperature.
Proceedings of the 52nd IEEE Conference on Decision and Control, 2013

Stochastic models and control for electrical power line temperature.
Proceedings of the 51st Annual Allerton Conference on Communication, 2013

2012
On Lyapunov Inequalities and Subsolutions for Efficient Importance Sampling.
ACM Trans. Model. Comput. Simul., 2012

Rare-event simulation for multi-server queues in the Halfin-Whitt regime.
SIGMETRICS Perform. Evaluation Rev., 2012

Sampling point processes on stable unbounded regions and exact simulation of queues.
Proceedings of the Winter Simulation Conference, 2012

Empirical Analysis of a Stochastic Approximation Approach for Computing Quasi-stationary Distributions.
Proceedings of the EVOLVE, 2012

2011
Efficient rare event simulation for heavy-tailed compound sums.
ACM Trans. Model. Comput. Simul., 2011

Efficient simulation of tail probabilities of sums of correlated lognormals.
Ann. Oper. Res., 2011

A conditional Monte Carlo method for estimating the failure probability of a distribution network with random demands.
Proceedings of the Winter Simulation Conference 2011, 2011

Importance sampling for actuarial cost analysis under a heavy traffic model.
Proceedings of the Winter Simulation Conference 2011, 2011

Rare event simulation techniques.
Proceedings of the Winter Simulation Conference 2011, 2011

Importance sampling for stochastic recurrence equations with heavy tailed increments.
Proceedings of the Winter Simulation Conference 2011, 2011

2010
Asymptotic robustness of estimators in rare-event simulation.
ACM Trans. Model. Comput. Simul., 2010

Asymptotic expansions of defective renewal equations with applications to perturbed risk models and processor sharing queues.
Math. Methods Oper. Res., 2010

Analysis of a Splitting Estimator for Rare Event Probabilities in Jackson Networks
CoRR, 2010

Monte Carlo for large credit portfolios with potentially high correlations.
Proceedings of the 2010 Winter Simulation Conference, 2010

2009
Rare event simulation for a slotted time <i>M</i>/<i>G</i>/<i>s</i> model.
Queueing Syst. Theory Appl., 2009

Rare Event Simulation for a Generalized Hawkes Process.
Proceedings of the 2009 Winter Simulation Conference, 2009

Efficient Rare Event Simulation of Continuous Time Markovian Perpetuities.
Proceedings of the 2009 Winter Simulation Conference, 2009

Rare Event Simulation and Counting Problems.
Proceedings of the Rare Event Simulation using Monte Carlo Methods, 2009

Rare Event Simulation for Queues.
Proceedings of the Rare Event Simulation using Monte Carlo Methods, 2009

2008
Large deviations perspective on ordinal optimization of heavy-tailed systems.
Proceedings of the 2008 Winter Simulation Conference, Global Gateway to Discovery, 2008

Efficient tail estimation for sums of correlated lognormals.
Proceedings of the 2008 Winter Simulation Conference, Global Gateway to Discovery, 2008

Efficient simulation for tail probabilities of Gaussian random fields.
Proceedings of the 2008 Winter Simulation Conference, Global Gateway to Discovery, 2008

2007
Editorial: rare-event simulation for queues.
Queueing Syst. Theory Appl., 2007

Fluid heuristics, Lyapunov bounds and efficient importance sampling for a heavy-tailed G/G/1 queue.
Queueing Syst. Theory Appl., 2007

Efficient suboptimal rare-event simulation.
Proceedings of the Winter Simulation Conference, 2007

Importance sampling of compounding processes.
Proceedings of the Winter Simulation Conference, 2007

Rare-event simulation for a multidimensional random walk with <i>t</i> distributed increments.
Proceedings of the Winter Simulation Conference, 2007

Path-sampling for state-dependent importance sampling.
Proceedings of the Winter Simulation Conference, 2007

2006
Efficient simulation for large deviation probabilities of sums of heavy-tailed increments.
Proceedings of the Winter Simulation Conference WSC 2006, 2006

Strongly efficient estimators for light-tailed sums.
Proceedings of the 1st International Conference on Performance Evaluation Methodolgies and Tools, 2006

State-dependent importance sampling and large deviations.
Proceedings of the 1st International Conference on Performance Evaluation Methodolgies and Tools, 2006


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