1 citations · 4 across the 11 of their papers we have counts for
11 papers
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…
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…