1 code implementation • 6 Nov 2018 • Jonathan N. Lee, Michael Laskey, Ajay Kumar Tanwani, Anil Aswani, Ken Goldberg
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.
no code implementations • 2 Jul 2019 • Jonathan N. Lee, Aldo Pacchiano, Michael. I. Jordan
Maximum a posteriori (MAP) inference is a fundamental computational paradigm for statistical inference.
no code implementations • ICML 2020 • Jonathan N. Lee, Aldo Pacchiano, Peter Bartlett, Michael. I. Jordan
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.
no code implementations • 19 Nov 2020 • Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill
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.
no code implementations • 23 Dec 2021 • Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai
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.
no code implementations • 3 Nov 2022 • Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill
We propose the first model selection algorithm for offline RL that achieves minimax rate-optimal oracle inequalities up to logarithmic factors.
no code implementations • 31 Jan 2023 • Jonathan N. Lee, Alekh Agarwal, Christoph Dann, Tong Zhang
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.
no code implementations • 19 Feb 2023 • Jonathan N. Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill
Whether this is possible for more realistic context distributions has remained an open and important question for tasks such as model selection.
no code implementations • NeurIPS 2023 • Aldo Pacchiano, Jonathan N. Lee, Emma Brunskill
We study the problem of experiment planning with function approximation in contextual bandit problems.