18 citations · 22 across the 18 of their papers we have counts for
18 papers
Extending Subsampling to Sequential Stopping
Jose Blanchet, Peter Glynn, Wenhao Yang
Fixed-width sequential stopping rules terminate a stochastic simulation once an estimated confidence interval reaches a prescribed width. Classical fixed-width theory typically rel…
A Harris recurrent continuous-time Markov process without wide-sense regenerative structure
Yanlin Qu, Peter Glynn
While Harris recurrent Markov chains (in discrete time) automatically exhibit wide-sense regenerative structure, we construct a Harris recurrent Markov process (in continuous time)…
Fast Convergence of Policy Regret in Learning Stochastic Optimal Control
Shengbo Wang, Jose Blanchet, Peter Glynn
Policy learning in modern operations environments faces a fundamental tension between limited operational data and the large, often continuous, state and action spaces over which g…
Statistical Inference for Stochastic Gradient Descent: Beyond Finite Variance
Jose Blanchet, Peter Glynn, Wenhao Yang
Stochastic gradient descent (SGD) is foundational to large-scale statistical learning and stochastic optimization. However, in some modern statistical learning problems, stochastic…
Statistical Inference in Causal Partial Identification with Smooth Densities
Sirui Lin, Zijun Gao, Jose Blanchet +1
Many causal quantities are only partially identifiable due to the inherent missingness of potential outcomes, and the associated partial identification (PI) sets can be obtained by…
Deep Learning for Markov Chains: Lyapunov Functions, Poisson's Equation, and Stationary Distributions
Yanlin Qu, Jose Blanchet, Peter Glynn
Lyapunov functions are fundamental to establishing the stability of Markovian models, yet their construction typically demands substantial creativity and analytical effort. In this…
Causal Partial Identification via Conditional Optimal Transport
Sirui Lin, Zijun Gao, Jose Blanchet +1
We study the estimation of causal estimand involving the joint distribution of treatment and control outcomes for a single unit. In typical causal inference settings, it is impossi…
Tightening Causal Bounds via Covariate-Aware Optimal Transport
Sirui Lin, Zijun Gao, Jose Blanchet +1
Causal estimands can vary significantly depending on the relationship between outcomes in treatment and control groups, potentially leading to wide partial identification (PI) inte…
Wasserstein Projection Tests: Higher-Order Asymptotics and Connections to Empirical Likelihood
Sirui Lin, Jose Blanchet, Peter Glynn +1
Tests of moment restrictions can be constructed by projecting the empirical distribution onto the set of laws satisfying the null. Wasserstein projection (WP) performs this project…
An Efficient High-Dimensional Gradient Estimator for Stochastic Differential Equations
Shengbo Wang, Jose Blanchet, Peter Glynn
Overparameterized stochastic differential equation (SDE) models have achieved remarkable success in various complex environments, such as PDE-constrained optimization, stochastic c…
Deep Learning for Computing Convergence Rates of Markov Chains
Yanlin Qu, Jose Blanchet, Peter Glynn
Convergence rate analysis for general state-space Markov chains is fundamentally important in areas such as Markov chain Monte Carlo and algorithmic analysis (for computing explici…
When are Unbiased Monte Carlo Estimators More Preferable than Biased Ones?
Guanyang Wang, Jose Blanchet, Peter W. Glynn
Due to the potential benefits of parallelization, designing unbiased Monte Carlo estimators, primarily in the setting of randomized multilevel Monte Carlo, has recently become very…
Optimal Sample Complexity for Average Reward Markov Decision Processes
Shengbo Wang, Jose Blanchet, Peter Glynn
We resolve the open question regarding the sample complexity of policy learning for maximizing the long-run average reward associated with a uniformly ergodic Markov decision proce…
Computable Bounds on Convergence of Markov Chains in Wasserstein Distance via Contractive Drift
Yanlin Qu, Jose Blanchet, Peter Glynn
We introduce a unified framework to estimate the convergence of Markov chains to equilibrium in Wasserstein distance. The framework can provide convergence bounds with rates rangin…
Optimal Sample Complexity of Stable Discounted Markov Decision Processes
Shengbo Wang, Jose Blanchet, Peter Glynn
We study the optimal sample complexity of tabular reinforcement learning for infinite-horizon discounted Markov decision processes. The unrestricted minimax sample complexity is kn…
The Design and Implementation of a Broadly Applicable Algorithm for Optimizing Intra-Day Surgical Scheduling
Jin Xie, Teng Zhang, Jose Blanchet +3
Surgical scheduling optimization is an active area of research. However, few algorithms to optimize surgical scheduling are implemented and see sustained use. An algorithm is more…
Surgical Scheduling via Optimization and Machine Learning with Long-Tailed Data
Yuan Shi, Saied Mahdian, Jose Blanchet +3
Using data from cardiovascular surgery patients with long and highly variable post-surgical lengths of stay (LOS), we develop a modeling framework to reduce recovery unit congestio…
On the transition from heavy traffic to heavy tails for the M/G/1 queue: The regularly varying case
Mariana Olvera-Cravioto, Jose Blanchet, Peter Glynn
Two of the most popular approximations for the distribution of the steady-state waiting time, , of the M/G/1 queue are the so-called heavy-traffic approximation and hea…