New arXiv papers advance multi-armed bandit algorithms and regret minimization
ByPulseAugur Editorial·[14 sources]·
Multiple research papers published on arXiv explore advancements in multi-armed bandit algorithms. One paper addresses optimal switching regret for bandits with an oblivious adversary, proposing a single algorithm that achieves optimal regret regardless of the number of switches. Another study focuses on selecting k paths with minimum longest path length in a stochastic semi-bandit setting, relevant for parallel data transmission. Further research investigates linear contextual bandits with arbitrary adaptive action sets, achieving nearly minimax-optimal regret bounds. Additionally, papers delve into bandit settings with probing and winner feedback, satisficing regret minimization, and Thompson sampling for non-monotone convex ridge bandits, all contributing to a deeper understanding of regret minimization and optimal strategies in various bandit problem formulations.
AI
IMPACT
These research papers contribute to the theoretical understanding and practical application of bandit algorithms, potentially leading to more efficient decision-making in AI systems.
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Cluster consists of multiple academic papers published on arXiv related to machine learning and bandit algorithms.
arXiv:2609.17595v1 Announce Type: new Abstract: In the improving multi-armed bandits problem, each of $k$ arms has an unknown nondecreasing, discretely concave reward curve $f_i$, and pulling arm $i$ for the $t$-th time yields $f_i(t)$. For sufficiently long horizons, Blum and Ra…
arXiv:2609.16824v1 Announce Type: new Abstract: Decentralized bandit systems often contain heterogeneous agents: rewards can change at individual agents even when the best action for the network stays the same. These local changes may cancel when rewards are averaged across agent…
Decentralized bandit systems often contain heterogeneous agents: rewards can change at individual agents even when the best action for the network stays the same. These local changes may cancel when rewards are averaged across agents, so the number of local changes $\Stloc$ can b…
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 $S$ is known, an optimal expected regret of $\widetilde{\mathcal{O}}(\sqrt{(S+1)KT})$…
arXiv:2609.14557v1 Announce Type: new Abstract: When performing parallel data transmission through a network using multiple paths, it is practically important to minimize the maximum transmission time among the selected paths. This study addresses an online problem in which $k$ p…
arXiv:2609.15170v1 Announce Type: new Abstract: We study stochastic linear contextual bandits with arbitrary action menus that may depend on the fixed parameter and the interaction history. We establish matching upper and lower bounds, up to logarithmic factors. Let $d$ be the di…
arXiv:2609.15248v1 Announce Type: new Abstract: A learner probes at most $k$ of $n$ arms each round, receives the maximum of their rewards in $[0,1]$, and competes with the best fixed arm. When does the probing advantage pay for learning? We determine two minimax laws. Under inde…
arXiv:2406.06802v4 Announce Type: replace-cross Abstract: Motivated by the concept of satisficing in decision-making, we consider the problem of satisficing regret minimization in bandit optimization. In this setting, the learner aims at selecting satisficing arms (arms with mean…
arXiv:2602.04761v2 Announce Type: replace Abstract: Gradient-variation online learning has drawn increasing attention due to its deep connections to game theory and optimization. It has been studied extensively in the full-information setting, but is underexplored with bandit fee…
arXiv:2311.01327v3 Announce Type: replace Abstract: We investigate the contextual bandits with knapsack (CBwK) problem in a high-dimensional linear setting, where the feature dimension can be very large. Our goal is to harness sparsity to obtain sharper regret guarantees. To this…
arXiv cs.LG
TIER_1English(EN)·Nathan Boyer, Dorian Baudry, Patrick Rebeschini·
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. We propose two Follow-the-Regu…
arXiv cs.LG
TIER_1English(EN)·Kaan Buyukkalayci, Osama Hanna, Christina Fragouli·
arXiv:2603.12530v3 Announce Type: replace Abstract: Recent work shows that when contexts are drawn i.i.d., linear contextual bandits can be reduced to single-context linear bandits. This ``contexts are cheap'' perspective is highly advantageous, as it allows for sharper finite-ti…
Bakhtiari, Lattimore and Szepesvári (COLT 2025) proved that Thompson sampling (TS) has Bayesian regret $\tilde O(d^{5/2}\sqrt n)$ for bandit convex optimisation with convex \emph{monotone} ridge losses $f(x)=\ell(\ip{x}θ)$, and asked whether monotonicity of the link is necessary.…
