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