ArXiv TLDR

Tomáš Kocák

4 papers · Latest:

Statistical Machine Learning

Spectral bandits

This paper introduces "spectral bandits," an online learning framework for graph-based problems like recommendations, using smooth payoffs and effective dimension.

2604.25272
Statistical Machine Learning

Online learning with Erdős-Rényi side-observation graphs

This paper introduces two novel algorithms for multi-armed bandits with probabilistic side observations, achieving near-optimal regret bounds for unknown observation rates.

2604.25271
Machine Learning

Online learning with noisy side observations

This paper introduces a new online learning model with noisy side observations and an efficient, parameter-free algorithm achieving $\widetilde{O}(\sqrt{\alpha^* T})$ regret.

2604.13740
Machine Learning

Spectral Thompson sampling

SpectralTS efficiently solves graph bandit problems by leveraging an effective dimension, achieving comparable regret with improved computational performance.

2604.13739

📬 Weekly AI Paper Digest

Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.