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Found 9 papers, 1 papers with code
Experiment Planning with Function Approximation
We study the problem of experiment planning with function approximation in contextual bandit problems.
Estimating Optimal Policy Value in General Linear Contextual Bandits
Whether this is possible for more realistic context distributions has remained an open and important question for tasks such as model selection.
Learning in POMDPs is Sample-Efficient with Hindsight Observability
POMDPs capture a broad class of decision making problems, but hardness results suggest that learning is intractable even in simple settings due to the inherent partial observability.
Oracle Inequalities for Model Selection in Offline Reinforcement Learning
We propose the first model selection algorithm for offline RL that achieves minimax rate-optimal oracle inequalities up to logarithmic factors.
Model Selection in Batch Policy Optimization
We formalize the problem in the contextual bandit setting with linear model classes by identifying three sources of error that any model selection algorithm should optimally trade-off in order to be competitive: (1) approximation error, (2) statistical complexity, and (3) coverage.
Online Model Selection for Reinforcement Learning with Function Approximation
Towards this end, we consider the problem of model selection in RL with function approximation, given a set of candidate RL algorithms with known regret guarantees.
Accelerated Message Passing for Entropy-Regularized MAP Inference
Maximum a posteriori (MAP) inference in discrete-valued Markov random fields is a fundamental problem in machine learning that involves identifying the most likely configuration of random variables given a distribution.
Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference
Maximum a posteriori (MAP) inference is a fundamental computational paradigm for statistical inference.
Dynamic Regret Convergence Analysis and an Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning
In this article, we reframe this result using dynamic regret theory from the field of online optimization and show that dynamic regret can be applied to any on-policy algorithm to analyze its convergence and optimality.
