arXiv research

New tighter confidence bounds for sequential kernel regression.

A new differentiable UCB algorithm for linear bandits learns adaptive confidence bounds.

Note improves confidence bounds for random variables.

Improved confidence bounds for linear logistic model with applications to bandits.

The paper honors Lai's contributions to multi-armed bandits and establishes new regret bounds.

A new framework tightens risk measure confidence bounds.

Improved online confidence bounds for multinomial logistic models in bandits.

The paper presents a method to compute trusted confidence bounds for LECs in CPS.

This note gives a short, self-contained, proof of a sharp connection between Gittins indices and Bayesian upper confidence bound algorithms. I consider a Gaussian multi-armed bandit problem with discount factor $γ$. The Gittins index of an arm is shown to equal the $γ$-quantile of the posterior distribution of the arm'…

Improved Bayesian optimisation method using randomised Gaussian process UCB.

New principle optimizes bandit decisions with context.

Proposes a method to compute valid lower confidence bounds for multiple models selected based on their performance.

Paper improves regret bounds for Gaussian process upper confidence bound in Bayesian optimization.

The estimation of probabilities of default (PDs) for low default portfolios by means of upper confidence bounds is a well established procedure in many financial institutions. However, there are often discussions within the institutions or between institutions and supervisors about which confidence level to use for the…

One-bit feedback suffices for a bandit problem's optimal strategy.

Bayesian methods improve drug discovery experiment design.

Upper Confidence Bound (UCB) method is arguably the most celebrated one used in online decision making with partial information feedback. Existing techniques for constructing confidence bounds are typically built upon various concentration inequalities, which thus lead to over-exploration. In this paper, we propose a n…

Study optimizes dynamic product selection and pricing using censored preference feedback.

A new method optimizes robustness measures under input uncertainty using randomized Gaussian process upper confidence bound.

Improved UCB method for stochastic bandits using distance tuning.

Berry et al. (1997) initiated the development of the infinite arms bandit problem. They derived a regret lower bound of all allocation strategies for Bernoulli rewards with uniform priors, and proposed strategies based on success runs. Bonald and Proutière (2013) proposed a two-target algorithm that achieves the regret…

New algorithm optimizes online decision-making with dynamically generated actions.

WS-KDE provides robust confidence bounds for stochastic functions.

Contextual bandits are widely used in Internet services from news recommendation to advertising, and to Web search. Generalized linear models (logistical regression in particular) have demonstrated stronger performance than linear models in many applications where rewards are binary. However, most theoretical analyses …

A new UCB algorithm for heavy-tailed bandits with near-optimal regret.

This paper proposes a DGP approach with UCBs for point target tracking over WSNs.

We provide a pointwise confidence bound for non-linear least-squares with fixed design.

UCB algorithm provides stable sample means for sequential data.

In this paper, the method UCB-RS, which resorts to recommendation system (RS) for enhancing the upper-confidence bound algorithm UCB, is presented. The proposed method is used for dealing with non-stationary and large-state spaces multi-armed bandit problems. The proposed method has been targeted to the problem of the …

Develops confidence bounds for off-policy evaluation in contextual bandits.

Paper proposes a new UCB approach for estimating maximum mean.

A new algorithm for better decision-making in recommendation systems.

We integrate information-theoretic concepts into the design and analysis of optimistic algorithms and Thompson sampling. By making a connection between information-theoretic quantities and confidence bounds, we obtain results that relate the per-period performance of the agent with its information gain about the enviro…

In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework that allows a general nonlinear reward function, whose expected value may not depend only on the means of the input random variables but possibly on the entire distributions of these variables. Our framework enables a much larger c…

We present two algorithms for Bayesian optimization in the batch feedback setting, based on Gaussian process upper confidence bound and Thompson sampling approaches, along with frequentist regret guarantees and numerical results.

This paper improves GP-UCB by using a shifted exponential distribution for confidence parameters.

CRB tackles rising rewards in combinatorial online learning.

EBUCB framework achieves optimal regret with bounded approximate inference error.

Bayesian bandit algorithms with approximate inference improve regret bounds in stochastic linear bandits.

ARTEO algorithm optimizes safety-critical systems with uncertainty.

This paper is devoted to regret lower bounds in the classical model of stochastic multi-armed bandit. A well-known result of Lai and Robbins, which has then been extended by Burnetas and Katehakis, has established the presence of a logarithmic bound for all consistent policies. We relax the notion of consistence, and e…

Bayes-UCBVI tackles reinforcement learning with a new upper confidence bound method.

We study an extension of the classic stochastic multi-armed bandit problem which involves multiple plays and Markovian rewards in the rested bandits setting. In order to tackle this problem we consider an adaptive allocation rule which at each stage combines the information from the sample means of all the arms, with t…

In this paper we propose a multi-armed bandit inspired, pool based active learning algorithm for the problem of binary classification. By carefully constructing an analogy between active learning and multi-armed bandits, we utilize ideas such as lower confidence bounds, and self-concordant regularization from the multi…

Efficiently identifies good policies by choosing contexts for human feedback.

Excessive reuse of test data has become commonplace in today's machine learning workflows. Popular benchmarks, competitions, industrial scale tuning, among other applications, all involve test data reuse beyond guidance by statistical confidence bounds. Nonetheless, recent replication studies give evidence that popular…

Develops hypothesis tests for conditional distributions using learning-theoretic bounds.

This paper analyzes the multi-armed bandit problem using frequency-domain methods.