← Back to all news
Hugging Face Trending Papers

Leveraging Similarities in Multi-Armed Bandits

In many online learning and bandit problems, the actions we consider possess inherent similarities--for instance because they share latent traits, tags, or hierarchical structure. We study online learning with a similarity-structured action set, encoded by a rooted tree whose leaves are the actions and whose levels quantify how closely two actions are related.

Related stories

arXiv Machine Learning
Sep 10

Improved Dimension Dependence for Bandit Convex Optimization with Gradient Variations

The paper presents an improved analysis of non‑consecutive gradient variation in Bandit Convex Optimization (BCO) with two‑point feedback, leading to better dimension dependence for both convex and strongly convex functions compared to prior work. It also derives new problem‑dependent guarantees such as gradient‑variance and small‑loss regret bounds, extends the technique to one‑point bandit linear optimization over hyper‑rectangular domains, and establishes the first gradient‑variation dynamic and universal regret bounds for two‑point BCO.

By Hang Yu, Yu-Hu Yan, Peng Zhao
reinforcement-learning
More like this →
Hugging Face Trending Papers
Jul 15

Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation

We study the contextual combinatorial semi-bandit (CCSB) problem with general reward function approximation. At each round, the learner observes a context, selects a combinatorial action consisting of a subset of basic arms, and receives the reward of each selected arm; the goal is to maximize the cumulative reward over time.

reinforcement-learningbenchmarks
More like this →
arXiv Machine Learning
Aug 19

Latent Order Bandits

The paper introduces Latent Order Bandits (LOB), a new bandit framework that relaxes the strict assumptions of traditional latent bandits by only requiring a partial order of action preferences within each latent state. LOB allows instances sharing the same state to have different reward distributions as long as the action ranking remains consistent, making it suitable for scenarios like user groups on streaming services who agree on genre preferences but rate differently. The authors present an upper‑confidence bound algorithm for both total and partial latent orders, provide regret bounds, and propose a posterior‑sampling variant that empirically outperforms full‑prior latent bandits when reward scales vary across instances sharing the same latent state.

By Emil Carlsson, Newton Mwai, Fredrik D. Johansson
reinforcement-learning
More like this →
arXiv AI
3d ago

On the Complexity of Preference-Based Bandits

The paper investigates preference-based bandits where a learner selects pairs of arms and receives binary preference feedback modeled by Bradley–Terry. It introduces the locally sensitive eluder dimension, a new complexity measure for logistic preference feedback, and proposes the GINOP algorithm that uses log-loss confidence sets to balance optimism and exploration. The authors prove a first-order regret bound showing that learning with preference feedback can be as statistically efficient as learning from direct rewards, and they validate their theory with empirical experiments.

By Ahmed Ben Yahmed (CREST, ENSAE Paris, FAIRPLAY), Marc Abeille (FAIRPLAY), Cl\'ement Calauz\`enes (FAIRPLAY)
reinforcement-learning
More like this →
arXiv Machine Learning
Jun 5

Multi-Agent Lipschitz Bandits

arXiv:2602. 16965v2 Announce Type: replace Abstract: We study the decentralized multi-player stochastic bandit problem over a continuous, Lipschitz-structured action space where hard collisions yield zero reward.

By Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni, Lijun Chen
agentsreinforcement-learning
More like this →