arXiv Machine Learning
6d ago
Reoptimization Algorithms for Contextual Bandits with Knapsack Constraints
arXiv:2608. 11383v1 Announce Type: new Abstract: We study new algorithms for Contextual Bandits with Knapsack.
Diversified Multinomial Logit Contextual Bandits
arXiv:2607. 11684v1 Announce Type: cross Abstract: Existing contextual multinomial logit (MNL) bandits model relevance-driven choice but ignore the potential benefits of within-assortment diversity, while submodular/combinatorial bandits encode diversity in rewards but lack structured choice probabilities.
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arXiv AI
Jun 9
Bandits for Efficient Experimentation: Adapting to Control Group, Preferences, and Context Drifts
arXiv:2606. 09802v1 Announce Type: cross Abstract: We consider a variant of the linear contextual stochastic multi-armed bandits, where the learner must provide recommendations to a group of users, each having its personalized preference vector, and in the presence of context distributions that are drifting over time.
arXiv Machine Learning
Jun 2
Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates
arXiv:2606. 00984v1 Announce Type: cross Abstract: We study linear contextual bandits under rare parameter updates: the learner may incorporate reward feedback into its parameter estimate only at a small number of update times, while still observing contexts online and selecting actions sequentially.
arXiv Machine Learning
Jul 1
Contextual Slate GLM Bandits with Limited Adaptivity
arXiv:2606. 31449v1 Announce Type: new Abstract: We investigate the contextual slate bandit problem with generalized linear rewards under limited adaptivity.
arXiv Machine Learning
Jun 30
Optimal Regret for Single Index Bandits
arXiv:2605. 09454v2 Announce Type: replace-cross Abstract: We study the $\textit{single-index bandit}$ problem, where rewards depend on an unknown one-dimensional projection of high-dimensional contexts through an unknown reward function.
arXiv AI
5d ago
Fast A/B/n Testing: Exact Multi-Policy Comparison via Tree-Coupled Feedback Sharing
arXiv:2608. 12831v1 Announce Type: cross Abstract: Online platforms increasingly compare many adaptive decision policies---ranking systems, recommendation algorithms, pricing rules, and language-model agents---while each reward-bearing interaction can be costly or risky.
arXiv Machine Learning
Aug 4
Meritocratic Fairness via $K$-Shapley Values in Budgeted Combinatorial Bandits with Full-Bandit Feedback
arXiv:2605. 00762v2 Announce Type: replace Abstract: We study meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback, where a learner selects at most $K$ arms per time step and observes only the noisy aggregate reward of the selected set.
arXiv Machine Learning
Jul 30
Top-$k$ Pareto Bandits: Hypervolume Regret for Multi-Objective Slate Selection
arXiv:2607. 26273v1 Announce Type: new Abstract: We consider a stochastic multi-objective bandit problem where, at each round, the agent selects a slate of $k$ arms and observes their $d$-dimensional reward vectors under semi-bandit feedback.
Hugging Face Trending Papers
Jun 8
Bandits for Efficient Experimentation: Adapting to Control Group, Preferences, and Context Drifts
We consider a variant of the linear contextual stochastic multi-armed bandits, where the learner must provide recommendations to a group of users, each having its personalized preference vector, and in the presence of context distributions that are drifting over time. Under practitioner-friendly assumptions, we reduce this setting to linear bandit with stationary mean but heteroskedastic and non-stationary noise.
arXiv Machine Learning
Jul 16
On the Sublinear Regret of Continuous K-Max Bandits
arXiv:2502. 13467v2 Announce Type: replace Abstract: The $K$-Max combinatorial multi-armed bandit problem arises in applications such as recommendation and distributed decision making, where the reward is determined by the maximum outcome among $K$ selected arms.
arXiv Machine Learning
Jul 13
Optimal Top-$k$ Identification from Pairwise Comparisons
arXiv:2607. 08979v1 Announce Type: new Abstract: We study the active learning problem of fixed-confidence top-$k$ identification from noisy pairwise comparisons.
arXiv Machine Learning
Jul 14
Rank-Conditioned Sample Reuse for the Plackett--Luce Best-of-$K$ Objective
arXiv:2607. 11146v1 Announce Type: new Abstract: We study the coupled objective J_K^WOR = E_{S ~ PL-WOR_K}[max_{i in S} R_i]: the expected maximum reward of a size-K Plackett-Luce draw without replacement, the law of Gumbel-Top-K / Stochastic Beam Search decoding.