10 citations · 32 across the 25 of their papers we have counts for
6 papers · 1 filter
Singular Control of (Reflected) Brownian Motion: A Computational Method Suitable for Queueing Applications
Baris Ata, J. Michael Harrison, Nian Si
Motivated by applications in queueing theory, we consider a class of singular stochastic control problems whose state space is the d-dimensional positive orthant. The original prob…
On the Foundation of Distributionally Robust Reinforcement Learning
Shengbo Wang, Nian Si, Jose Blanchet +1
Motivated by the need for a robust policy in the face of environment shifts between training and deployment, we contribute to the theoretical foundation of distributionally robust…
Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach
Nian Si
In modern recommendation systems, the standard pipeline involves training machine learning models on historical data to predict user behaviors and improve recommendations continuou…
Drift Control of High-Dimensional RBM: A Computational Method Based on Neural Networks
Baris Ata, J. Michael Harrison, Nian Si
Motivated by applications in queueing theory, we consider a stochastic control problem whose state space is the -dimensional positive orthant. The controlled process evolves…
Sample Complexity of Variance-reduced Distributionally Robust Q-learning
Shengbo Wang, Nian Si, Jose Blanchet +1
Dynamic decision-making under distributional shifts is of fundamental interest in theory and applications of reinforcement learning: The distribution of the environment in which th…
A Finite Sample Complexity Bound for Distributionally Robust Q-learning
Shengbo Wang, Nian Si, Jose Blanchet +1
We consider a reinforcement learning setting in which the deployment environment is different from the training environment. Applying a robust Markov decision processes formulation…