9 papers
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…
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…
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…