Bandits with Abstention under Expert Advice

• URL: http://arxiv.org/abs/2402.14585v2
• Date: Tue, 12 Nov 2024 14:58:48 GMT
• Title: Bandits with Abstention under Expert Advice
• Authors: Stephen Pasteris, Alberto Rumi, Maximilian Thiessen, Shota Saito, Atsushi Miyauchi, Fabio Vitale, Mark Herbster,
• Abstract summary: We study the classic problem of prediction with expert advice under bandit feedback. We propose the CBA algorithm, which exploits this assumption to obtain reward bounds. We are the first to achieve bounds on the expected cumulative reward for general confidence-rated predictors.
• Score: 17.7637383442147
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the classic problem of prediction with expert advice under bandit feedback. Our model assumes that one action, corresponding to the learner's abstention from play, has no reward or loss on every trial. We propose the CBA algorithm, which exploits this assumption to obtain reward bounds that can significantly improve those of the classical Exp4 algorithm. We can view our problem as the aggregation of confidence-rated predictors when the learner has the option of abstention from play. Importantly, we are the first to achieve bounds on the expected cumulative reward for general confidence-rated predictors. In the special case of specialists we achieve a novel reward bound, significantly improving previous bounds of SpecialistExp (treating abstention as another action). As an example application, we discuss learning unions of balls in a finite metric space. In this contextual setting, we devise an efficient implementation of CBA, reducing the runtime from quadratic to almost linear in the number of contexts. Preliminary experiments show that CBA improves over existing bandit algorithms.

Related papers

• Bayesian Algorithms for Adversarial Online Learning: from Finite to Infinite Action Spaces [51.513172647831745]
  We develop a form Thompson sampling for online learning under full feedback.<n>We show regret decomposes into regret the learner expected a priori, plus a prior-robustness-type term we call excess regret.
  arXiv  Detail & Related papers  (2025-02-20T18:10:12Z)
• Reward Learning as Doubly Nonparametric Bandits: Optimal Design and Scaling Laws [22.099915149343957]
  We propose a theoretical framework for studying reward learning and the associated optimal experiment design problem. We first derive non-asymptotic excess risk bounds for a simple plug-in estimator based on ridge regression. We then solve the query design problem by optimizing these risk bounds with respect to the choice of query set and obtain a finite sample statistical rate.
  arXiv  Detail & Related papers  (2023-02-23T22:07:33Z)
• Risk-Aware Algorithms for Combinatorial Semi-Bandits [7.716156977428555]
  We study the multi-armed bandit problem under semi-bandit feedback. We consider the problem of maximizing the Conditional Value-at-Risk (CVaR), a risk measure that takes into account only the worst-case rewards. We propose new algorithms that maximize the CVaR of the rewards obtained from the super arms of the bandit.
  arXiv  Detail & Related papers  (2021-12-02T11:29:43Z)
• Break your Bandit Routine with LSD Rewards: a Last Switch Dependent Analysis of Satiation and Seasonality [6.146046338698175]
  We introduce a novel non-stationary bandit problem, where the expected reward of an arm is fully determined by the time elapsed since the arm last took part in a switch of actions. Our model generalizes previous notions of delay-dependent rewards, and also relaxes most assumptions on the reward function. We prove an algorithm and prove a bound on its regret with respect to the optimal non-stationary policy.
  arXiv  Detail & Related papers  (2021-10-22T14:53:13Z)
• Anti-Concentrated Confidence Bonuses for Scalable Exploration [57.91943847134011]
  Intrinsic rewards play a central role in handling the exploration-exploitation trade-off. We introduce emphanti-concentrated confidence bounds for efficiently approximating the elliptical bonus. We develop a practical variant for deep reinforcement learning that is competitive with contemporary intrinsic rewards on Atari benchmarks.
  arXiv  Detail & Related papers  (2021-10-21T15:25:15Z)
• Deep Upper Confidence Bound Algorithm for Contextual Bandit Ranking of Information Selection [0.0]
  Contextual multi-armed bandits (CMAB) have been widely used for learning to filter and prioritize information according to a user's interest. In this work, we analyze top-K ranking under the CMAB framework where the top-K arms are chosen iteratively to maximize a reward. We introduce a novel algorithm called the Deep Upper Confidence Bound (UCB) algorithm.
  arXiv  Detail & Related papers  (2021-10-08T13:32:14Z)
• Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination [82.52105963476703]
  A recurring theme in statistical learning, online learning, and beyond is that faster convergence rates are possible for problems with low noise. First-order guarantees are relatively well understood in statistical and online learning. We show that the logarithmic loss and an information-theoretic quantity called the triangular discrimination play a fundamental role in obtaining first-order guarantees.
  arXiv  Detail & Related papers  (2021-07-05T19:20:34Z)
• Bayesian decision-making under misspecified priors with applications to meta-learning [64.38020203019013]
  Thompson sampling and other sequential decision-making algorithms are popular approaches to tackle explore/exploit trade-offs in contextual bandits. We show that performance degrades gracefully with misspecified priors.
  arXiv  Detail & Related papers  (2021-07-03T23:17:26Z)
• Confidence-Budget Matching for Sequential Budgeted Learning [69.77435313099366]
  We formalize decision-making problems with querying budget. We consider multi-armed bandits, linear bandits, and reinforcement learning problems. We show that CBM based algorithms perform well in the presence of adversity.
  arXiv  Detail & Related papers  (2021-02-05T19:56:31Z)
• Stochastic Bandits with Linear Constraints [69.757694218456]
  We study a constrained contextual linear bandit setting, where the goal of the agent is to produce a sequence of policies. We propose an upper-confidence bound algorithm for this problem, called optimistic pessimistic linear bandit (OPLB)
  arXiv  Detail & Related papers  (2020-06-17T22:32:19Z)
• Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures [62.41541049302712]
  We propose a novel decision maker grounded in control theory that controls the amount of risk we allow in the search as a function of a given budget of failures. Our algorithm uses the failures budget more efficiently in a variety of optimization experiments, and generally achieves lower regret, than state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-05-15T09:54:09Z)
• A General Theory of the Stochastic Linear Bandit and Its Applications [8.071506311915398]
  We introduce a general analysis framework and a family of algorithms for the linear bandit problem. Our new notion of optimism in expectation gives rise to a new algorithm, called sieved greedy (SG) that reduces the overexploration problem in OFUL. In addition to proving that SG is theoretically rate optimal, our empirical simulations show that SG outperforms existing benchmarks such as greedy, OFUL, and TS.
  arXiv  Detail & Related papers  (2020-02-12T18:54:41Z)
• Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles [112.89548995091182]
  We provide the first universal and optimal reduction from contextual bandits to online regression. Our algorithm requires no distributional assumptions beyond realizability, and works even when contexts are chosen adversarially.
  arXiv  Detail & Related papers  (2020-02-12T11:33:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.