arXiv research

New algorithm for shareable arms with load-dependent rewards in stochastic bandits.

The paper advocates for interpretable, accountable, reproducible machine learning in medicine.

Study uses few-shot learning to analyze claims and arguments in German debate on arms deliveries.

Understanding search queries is critical for shopping search engines to deliver a satisfying customer experience. Popular shopping search engines receive billions of unique queries yearly, each of which can depict any of hundreds of user preferences or intents. In order to get the right results to customers it must be …

We study a bad arm existing checking problem in which a player's task is to judge whether a positive arm exists or not among given K arms by drawing as small number of arms as possible. Here, an arm is positive if its expected loss suffered by drawing the arm is at least a given threshold. This problem is a formalizati…

New algorithms for streaming bandits with limited memory.

Study best arm identification in restless Markov multi-armed bandits with state-dependent transitions.

Paper tackles identifying an odd arm in a multi-armed bandit with restless Markov processes and trembling hand.

Purveyors of malicious network attacks continue to increase the complexity and the sophistication of their techniques, and their ability to evade detection continues to improve as well. Hence, intrusion detection systems must also evolve to meet these increasingly challenging threats. Machine learning is often used to …

Improved theoretical guarantees for Top Two algorithms.

We address the M-best-arm identification problem in multi-armed bandits. A player has a limited budget to explore K arms (M<K), and once pulled, each arm yields a reward drawn (independently) from a fixed, unknown distribution. The goal is to find the top M arms in the sense of expected reward. We develop an algorithm …

Sequential screening and dynamic regret in multi-armed bandits with arriving arms

Paper tackles good arm identification in stochastic bandits.

We consider the best-arm identification problem in multi-armed bandits, which focuses purely on exploration. A player is given a fixed budget to explore a finite set of arms, and the rewards of each arm are drawn independently from a fixed, unknown distribution. The player aims to identify the arm with the largest expe…

A novel algorithm reduces communication costs in federated best arm identification.

New algorithms identify best arm with less pulls, adapting to arm covariances.

Study best arm identification in restless bandits with unknown TPMs.

New method for identifying best arm in batched multi-armed bandit problems.

Optimal best-arm identification with known number of optimal arms.

We consider a novel multi-armed bandit framework where the rewards obtained by pulling the arms are functions of a common latent random variable. The correlation between arms due to the common random source can be used to design a generalized upper-confidence-bound (UCB) algorithm that identifies certain arms as $non-c…

Paper tackles outlier detection in multi-armed bandits, achieving high accuracy with reduced exploration costs.

We study a strategic version of the multi-armed bandit problem, where each arm is an individual strategic agent and we, the principal, pull one arm each round. When pulled, the arm receives some private reward $v_a$ and can choose an amount $x_a$ to pass on to the principal (keeping $v_a-x_a$ for itself). All non-pulle…

Improved best-arm identification in correlated multi-armed bandits.

Optimal best-arm identification in linear bandits reduces sampling budget.

Study optimizes identifying the best arm with fixed rounds and Gaussian outcomes.

This work optimizes identifying good arms in nonparametric multi-armed bandits.

We analyze the regret of combinatorial Thompson sampling (CTS) for the combinatorial multi-armed bandit with probabilistically triggered arms under the semi-bandit feedback setting. We assume that the learner has access to an exact optimization oracle but does not know the expected base arm outcomes beforehand. When th…

Develops a framework for clustering and distribution matching with bandit feedback.

New algorithm reduces super-arm selection complexity exponentially.

New algorithm identifies best target arm with known additive relationship between source and target MAB instances.

This paper studies Thompson sampling's arm-pull dynamics and inference, revealing key differences from UCB algorithms.

Study best arm identification with limited precision sampling in bandits.

The paper tackles a bandit problem with infinitely many arms per group, aiming to identify the group with the highest quantile reward.

New algorithm reduces worst-case sample complexity for learning best arm.

Greedy algorithms outperform UCB in many-armed bandit problems.

New strategies for identifying the best arm in bandits with decreasing variances.

We consider the Max $K$-Armed Bandit problem, where a learning agent is faced with several sources (arms) of items (rewards), and interested in finding the best item overall. At each time step the agent chooses an arm, and obtains a random real valued reward. The rewards of each arm are assumed to be i.i.d., with an un…

Motivated by drug design, we consider the best-arm identification problem in generalized linear bandits. More specifically, we assume each arm has a vector of covariates, there is an unknown vector of parameters that is common across the arms, and a generalized linear model captures the dependence of rewards on the cov…

New methods optimize personalized treatment assignment in trials with many arms.

Sampling from distributions to find the one with the largest mean arises in a broad range of applications, and it can be mathematically modeled as a multi-armed bandit problem in which each distribution is associated with an arm. This paper studies the sample complexity of identifying the best arm (largest mean) in a m…

New algorithm finds best feasible arm in grouped bandits.

New algorithm identifies good arms with fewer samples when thresholds are close.

New algorithm for identifying optimal arms in stochastic bandit problems.

In the Best-$K$ identification problem (Best-$K$-Arm), we are given $N$ stochastic bandit arms with unknown reward distributions. Our goal is to identify the $K$ arms with the largest means with high confidence, by drawing samples from the arms adaptively. This problem is motivated by various practical applications and…

New algorithms find all ε-good arms in stochastic bandits.

We consider a class of restless multi-armed bandit (RMAB) problems with unknown arm dynamics. At each time, a player chooses an arm out of N arms to play, referred to as an active arm, and receives a random reward from a finite set of reward states. The reward state of the active arm transits according to an unknown Ma…

Thompson Sampling tackles USS, a sequential selection problem without feedback.

Proposes a fair RMAB framework ensuring equal exposure to arms.