The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The paper introduces SHPRR, an algorithm that adapts Sequential Halving for pure exploration problems where regret relative to a baseline is a key cost metric. It shows that regret cost decreases as the algorithm progresses, allowing a front‑loaded allocation of the regret budget to improve best‑arm identification. Experiments on synthetic and real data demonstrate that SHPRR outperforms uniform regret allocation and existing baselines.
arXiv:2607. 28408v1 Announce Type: new Abstract: This thesis studies policy learning in interactive systems where an agent observes a context, selects an action from a very large set, and receives partial feedback.
The paper introduces TS-SA, a variant of Langevin-based Thompson Sampling that integrates stochastic approximation into the bandit framework. TS-SA performs a single Langevin step per new reward and updates a persistent Markov chain for each arm, avoiding repeated sampling of past data. This approach yields a near‑optimal worst‑case regret bound of ≥O(√{KT}) under log‑concavity and smoothness, and empirical results show strong performance against standard baselines.
arXiv:2606. 01655v1 Announce Type: cross Abstract: The Bayesian paradigm offers principled tools for sequential decision-making under uncertainty, but its reliance on a probabilistic model for all parameters can hinder the incorporation of complex structural constraints.
arXiv:2606. 26657v1 Announce Type: new Abstract: Identifying high-utility candidates from massive discrete spaces under expensive evaluations is a recurring challenge across the sciences, with structure-based drug discovery as a prominent example.
arXiv:2606. 28616v1 Announce Type: new Abstract: In stochastic linear bandits, the canonical Upper Confidence Bound (UCB) algorithm admits a simple frequentist regret analysis but can be computationally demanding, while Thompson Sampling (TS) is computationally attractive yet typically harder to analyze due to its non-optimistic nature.