COLT 2025
PulseAugur coverage of COLT 2025 — every cluster mentioning COLT 2025 across labs, papers, and developer communities, ranked by signal.
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New arXiv papers advance multi-armed bandit algorithms and regret minimization
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 …
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Two papers resolve open problems in weighted data selection for linear regression
Two new papers published on arXiv address open problems in weighted data selection for linear regression. The first paper resolves a threshold question posed at COLT 2025, proving that $(m+1)d$ weighted examples are exa…
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Minimax Alternating Regret for Experts Problem and OCO Resolved · 2 sources tracked
Researchers have resolved the minimax regret rate for the Experts Problem and Online Convex Optimization (OCO). For the d-expert problem, the minimax alternating regret is shown to be $\Theta(\log d)$, independent of th…
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New Bandit Algorithm Adapts to Unknown Heavy Tails
Researchers have developed a new algorithm for heavy-tailed bandits that does not require prior knowledge of the reward distribution's tail parameters. This addresses an open problem posed at COLT 2025 by Genalti and Me…