arXiv:2610.07615v1 Announce Type: new Abstract: Active Feature Acquisition (AFA) is a classification problem in which an agent decides which costly features to acquire before predicting each sample's...
arXiv:2605. 01961v2 Announce Type: replace Abstract: Learning from human preference data is becoming a useful tool, from fine-tuning large language models to training reinforcement learning agents.
The paper introduces Online Hyperparameter Optimization (OHPO), framing it as an infinitely many‑armed bandit problem over mixed and conditional search spaces. It proposes the IMABO framework, which couples any bandit policy with any oracle for proposing new configurations, and presents IMOSS—a restart‑free anytime policy with provable regret bounds. Experiments show that IMABO, combined with practical oracles such as TPE, an incumbent‑mutation oracle, and a pretrained tabular foundation model, outperforms random search across a range of settings from classical ML models to LLM‑based agents.
The paper introduces a hierarchical Bayesian framework for multi-task contextual bandits, capturing both heterogeneity and correlations among bandit instances. It proposes an empirical Bayesian method to estimate the covariance matrix of the prior, leading to two new algorithms—ebmTS and ebmUCB—that incorporate this prior into decision making. The authors provide frequentist regret bounds and show through synthetic and real-world experiments that these algorithms outperform existing methods, especially in complex settings.
The paper introduces a new variant of the multi‑armed bandit problem that incorporates dueling feedback—pairwise comparisons of model responses—and heterogeneous sampling costs to identify the best large language model (LLM) from a set with varying query costs. Assuming a Condorcet winner, the authors propose a Track‑and‑Stop style algorithm that guarantees asymptotically optimal cost as the error probability approaches zero. Extensive experiments on synthetic and real‑world data show that this cost‑aware approach consistently outperforms both classical cost‑unaware algorithms and other cost‑aware extensions.
arXiv:2606. 00913v1 Announce Type: cross Abstract: Multi-arm bandit algorithms are increasingly used in online platforms, clinical trials, and social science experiments, but valid statistical inference on their performance remains an open challenge.
arXiv:2606. 00846v1 Announce Type: new Abstract: Users increasingly face the challenge of selecting an appropriate LLM for a given task from a rapidly growing pool of LLMs, each with distinct but often opaque latent properties.
arXiv:2608. 16707v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as decision-making agents in settings that require sophisticated environmental exploration.
Exhaustively evaluating every candidate LLM configuration on every benchmark item to identify a high-performing one is costly. We formulate configuration selection as a cost-aware Bayesian bandit prob...
arXiv:2605. 00762v2 Announce Type: replace Abstract: We study meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback, where a learner selects at most $K$ arms per time step and observes only the noisy aggregate reward of the selected set.
arXiv:2608. 03437v1 Announce Type: cross Abstract: While human evaluation is the gold standard in many NLP tasks, it suffers from prohibitive costs and poor scalability.
arXiv:2004. 06321v2 Announce Type: replace Abstract: We study the sequential batch learning problem in linear contextual bandits with finite action sets, where the decision maker is constrained to split incoming individuals into (at most) a fixed number of batches and can only observe outcomes for the individuals within a batch at the batch's end.