arXiv Machine Learning
Oct 7

On the Computational Tractability of Robust Bandits

The paper studies robust bandits, a framework for learning when the true environment lies outside the learner’s hypothesis class. It presents a polynomial‑time algorithm achieving ≠O(√T) regret for a specific tractable case, while showing that slight extensions of this case are NP‑hard. The work highlights the computational limits of robust bandits and suggests their relevance to AI alignment.

By Vanessa Kosoy, Vinayak Pathak
arXiv Machine Learning
Oct 16

Transfer Learning in Bandits from Unknown Adaptive Policies: Problem-Dependent Lower and Upper Bounds

The paper investigates transfer learning in stochastic multi‑armed bandits when historical data come from an unknown adaptive policy. It establishes asymptotic problem‑dependent lower bounds and introduces the KL‑UCB‑Transfer algorithm, which combines online and historical data to achieve logarithmic regret with an improved constant under certain mean‑gap conditions. The study also relaxes the source‑target discrepancy assumption to a one‑sided condition, demonstrating that logarithmic regret can still be achieved under misspecification and identifying regimes of improvement, degradation, or loss of guarantee.

By Adrien Prevost, Timothee Mathieu, Waris Radji, Odalric-Ambrym Maillard