arXiv Machine Learning
1d ago
Minimax Optimal Regret for Causal Logistic Bandits with Counterfactual Fairness
arXiv:2610. 01377v1 Announce Type: new Abstract: We study causal logistic bandits with counterfactual fairness constraints.
Learning When to Trust in Contextual Social Bandits
arXiv:2603. 13356v2 Announce Type: replace Abstract: Robust reinforcement learning typically assumes that feedback sources are either globally trustworthy or corrupted within a fixed global budget.
Related stories
arXiv Machine Learning
Jul 1
A Complete Characterization of Learnability for Adversarial Noisy Bandits
arXiv:2605. 09200v2 Announce Type: replace Abstract: We study adversarial noisy bandits given a known function class $\mathcal{F}$.
arXiv Machine Learning
Jun 2
Online Learning in MDPs with Partially Adversarial Transitions and Losses
arXiv:2602. 09474v2 Announce Type: replace Abstract: We study reinforcement learning in MDPs whose transition function is stochastic at most steps but may behave adversarially at a fixed subset of $\Lambda$ steps per episode.
arXiv Machine Learning
Jun 2
Robust Linear Dueling Bandits with Post-serving Context under Unknown Delays and Adversarial Corruptions
arXiv:2605. 01752v4 Announce Type: replace Abstract: We study linear dueling bandits in volatile environments characterized by the simultaneous presence of post-serving contexts, delayed feedback, and adversarial corruption.
arXiv Machine Learning
Jun 19
Matching Markets meet Cumulative Prospect Theory: Towards Optimal and Adversarially Robust Learning
arXiv:2606. 19883v1 Announce Type: new Abstract: We study a multi-agent multi-armed bandit problem in the competitive setup with two-sided matching markets under a human centric decision making model.
arXiv Machine Learning
Jul 28
Nearly Tight Bounds for Cross-Learning Contextual Bandits with Graphical Feedback
arXiv:2502. 04678v2 Announce Type: replace Abstract: Repeated first-price auctions are contextual decision problems with censored but reusable feedback: after submitting a bid, a learner can infer the outcomes of related bids and evaluate them under different private values.
arXiv Machine Learning
Aug 26
An Efficient Minimax-Optimal Algorithm for Adversarial $m$-Set Bandits
arXiv:2608. 12231v2 Announce Type: replace Abstract: We study adversarial combinatorial bandits with $m$-set actions, where at each round the learner selects $m$ out of $d$ items and observes only the aggregate loss of the selected items.
arXiv Machine Learning
Sep 15
Toward Optimal Switching Regret for Multi-Armed Bandits with Oblivious Adversary
arXiv:2609.13547v1 Announce Type: new Abstract: We study switching regret in adversarial multi-armed bandits, where the learner competes with an arm sequence that changes at most $S$ times. When $S$...
arXiv Machine Learning
Sep 10
Best-of-Both Worlds for linear contextual bandits with paid observations
arXiv:2510. 07424v3 Announce Type: replace Abstract: We study linear contextual bandits with paid observations, where at each round the learner observes a context, selects an action, and may pay a fixed cost to observe feedback from a subset of arms.
arXiv Machine Learning
Jun 16
Near-Optimal Regret for Distributed Adversarial Bandits: A Black-Box Approach
arXiv:2602. 06404v2 Announce Type: replace Abstract: We study distributed adversarial bandits, where $N$ agents cooperate to minimize the global average loss while observing only their own local losses.
arXiv Machine Learning
Jun 16
Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare
arXiv:2605. 01961v2 Announce Type: replace Abstract: Learning from human preference data is becoming a useful tool, from fine-tuning large language models to training reinforcement learning agents.
arXiv Machine Learning
Jun 29
Learning in Markovian bandits with non-observable states and constrained decision epochs
arXiv:2606. 27448v1 Announce Type: new Abstract: This paper studies the problem of regret minimization in Markovian bandits with \emph{non-observable states} and possibly \emph{constrained} decision epochs.