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Found 10 papers, 2 papers with code
Online Estimation via Offline Estimation: An Information-Theoretic Framework
Our main results settle the statistical and computational complexity of online estimation in this framework.
Byzantine-Robust Federated Linear Bandits
In this paper, we study a linear bandit optimization problem in a federated setting where a large collection of distributed agents collaboratively learn a common linear bandit model.
The Statistical Complexity of Interactive Decision Making
The main result of this work provides a complexity measure, the Decision-Estimation Coefficient, that is proven to be both necessary and sufficient for sample-efficient interactive learning.
Robust learning under clean-label attack
We study the problem of robust learning under clean-label data-poisoning attacks, where the attacker injects (an arbitrary set of) correctly-labeled examples to the training set to fool the algorithm into making mistakes on specific test instances at test time.
Stochastic Bandits with Vector Losses: Minimizing $\ell^\infty$-Norm of Relative Losses
We study two goals: (a) finding the arm with the minimum $\ell^\infty$-norm of relative losses with a given confidence level (which refers to fixed-confidence best-arm identification); (b) minimizing the $\ell^\infty$-norm of cumulative relative losses (which refers to regret minimization).
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes
We study minimax optimal reinforcement learning in episodic factored Markov decision processes (FMDPs), which are MDPs with conditionally independent transition components.
Concentration Inequalities for Multinoulli Random Variables
We investigate concentration inequalities for Dirichlet and Multinomial random variables.
Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs
The exploration bonus is an effective approach to manage the exploration-exploitation trade-off in Markov Decision Processes (MDPs).
Importance Resampling for Off-policy Prediction
Importance sampling (IS) is a common reweighting strategy for off-policy prediction in reinforcement learning.
Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes
We introduce and analyse two algorithms for exploration-exploitation in discrete and continuous Markov Decision Processes (MDPs) based on exploration bonuses.
