137 citations · 656 across the 54 of their papers we have counts for
10 papers · 1 filter
A Convex Framework for Confounding Robust Inference
Kei Ishikawa, Niao He, Takafumi Kanamori
We study policy evaluation of offline contextual bandits subject to unobserved confounders. Sensitivity analysis methods are commonly used to estimate the policy value under the wo…
On Imitation in Mean-field Games
Giorgia Ramponi, Pavel Kolev, Olivier Pietquin +3
We explore the problem of imitation learning (IL) in the context of mean-field games (MFGs), where the goal is to imitate the behavior of a population of agents following a Nash eq…
Provably Convergent Policy Optimization via Metric-aware Trust Region Methods
Jun Song, Niao He, Lijun Ding +1
Trust-region methods based on Kullback-Leibler divergence are pervasively used to stabilize policy optimization in reinforcement learning. In this paper, we exploit more flexible m…
Provably Learning Nash Policies in Constrained Markov Potential Games
Pragnya Alatur, Giorgia Ramponi, Niao He +1
Multi-agent reinforcement learning (MARL) addresses sequential decision-making problems with multiple agents, where each agent optimizes its own objective. In many real-world insta…
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space
Anas Barakat, Ilyas Fatkhullin, Niao He
We consider the reinforcement learning (RL) problem with general utilities which consists in maximizing a function of the state-action occupancy measure. Beyond the standard cumula…
Cancellation-Free Regret Bounds for Lagrangian Approaches in Constrained Markov Decision Processes
Adrian Müller, Pragnya Alatur, Giorgia Ramponi +1
Constrained Markov Decision Processes (CMDPs) are one of the common ways to model safe reinforcement learning problems, where constraint functions model the safety objectives. Lagr…
Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods
Junchi Yang, Xiang Li, Ilyas Fatkhullin +1
The classical analysis of Stochastic Gradient Descent (SGD) with polynomially decaying stepsize relies on well-tuned depending on problem parameters such as…
Kernel Conditional Moment Constraints for Confounding Robust Inference
Kei Ishikawa, Niao He
We study policy evaluation of offline contextual bandits subject to unobserved confounders. Sensitivity analysis methods are commonly used to estimate the policy value under the wo…
Robust Knowledge Transfer in Tiered Reinforcement Learning
Jiawei Huang, Niao He
In this paper, we study the Tiered Reinforcement Learning setting, a parallel transfer learning framework, where the goal is to transfer knowledge from the low-tier (source) task t…
Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies
Ilyas Fatkhullin, Anas Barakat, Anastasia Kireeva +1
Recently, the impressive empirical success of policy gradient (PG) methods has catalyzed the development of their theoretical foundations. Despite the huge efforts directed at the…