arXiv research

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

Unified meta algorithms estimate various distribution functionals in infinite-armed bandits.

We consider the problem of near-optimal arm identification in the fixed confidence setting of the infinitely armed bandit problem when nothing is known about the arm reservoir distribution. We (1) introduce a PAC-like framework within which to derive and cast results; (2) derive a sample complexity lower bound for near…

We consider a stochastic bandit problem with infinitely many arms. In this setting, the learner has no chance of trying all the arms even once and has to dedicate its limited number of samples only to a certain number of arms. All previous algorithms for this setting were designed for minimizing the cumulative regret o…

Optimal algorithm for selecting high-quality arms from infinite bandit arms.

New algorithm reduces regret in infinitely many-armed bandits with decreasing rewards.

New method tracks shifts in infinite-armed bandits without prior knowledge.

This paper studies the problem of identifying any $k$ distinct arms among the top $ρ$ fraction (e.g., top 5\%) of arms from a finite or infinite set with a probably approximately correct (PAC) tolerance $ε$. We consider two cases: (i) when the threshold of the top arms' expected rewards is known and (ii) when it is unk…

This paper considers a multi-armed bandit game where the number of arms is much larger than the maximum budget and is effectively infinite. We characterize necessary and sufficient conditions on the total budget for an algorithm to return an ε-good arm with probability at least 1 - δ. In such situations, the sample com…

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…

Study tackles infinitely many-armed bandits with rotting rewards, achieving tight regret bounds.

We consider the problem of identifying any $k$ out of the best $m$ arms in an $n$-armed stochastic multi-armed bandit. Framed in the PAC setting, this particular problem generalises both the problem of `best subset selection' and that of selecting `one out of the best m' arms [arcsk 2017]. In applications such as crowd…

We propose $\tt RandUCB$, a bandit strategy that builds on theoretically derived confidence intervals similar to upper confidence bound (UCB) algorithms, but akin to Thompson sampling (TS), it uses randomization to trade off exploration and exploitation. In the $K$-armed bandit setting, we show that there are infinitel…

We analyze the $K$-armed bandit problem where the reward for each arm is a noisy realization based on an observed context under mild nonparametric assumptions. We attain tight results for top-arm identification and a sublinear regret of $\widetilde{O}\Big(T^{\frac{1+D}{2+D}}\Big)$, where $D$ is the context dimension, f…

The paper provides robustness guarantees for mode estimation in bandits.

Study quantile multi-armed bandits for identifying the best arm with a specified quantile level.

New algorithm for identifying optimal arms in stochastic bandit problems.

An optimal algorithm for multi-armed bandits with constraints.

A new method optimizes in nonstationary environments with many arms efficiently.

In a linear stochastic bandit model, each arm is a vector in an Euclidean space and the observed return at each time step is an unknown linear function of the chosen arm at that time step. In this paper, we investigate the problem of learning the best arm in a linear stochastic bandit model, where each arm's expected r…

The paper proposes a novel upper confidence bound (UCB) procedure for identifying the arm with the largest mean in a multi-armed bandit game in the fixed confidence setting using a small number of total samples. The procedure cannot be improved in the sense that the number of samples required to identify the best arm i…

The paper tackles best arm identification in contaminated bandits with optimal error guarantees and sample complexity.

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

The paper identifies the best treatment to maximize NDPO, a key outcome in causal mediation analysis.

Study on indexability of restless multi-armed bandits and rollout policy performance.

New framework improves restless bandit policies for large numbers of arms.

In this paper, we study multi-armed bandit problems in explore-then-commit setting. In our proposed explore-then-commit setting, the goal is to identify the best arm after a pure experimentation (exploration) phase and exploit it once or for a given finite number of times. We identify that although the arm with the hig…

This paper studies active learning in the context of robust statistics. Specifically, we propose a variant of the Best Arm Identification problem for \emph{contaminated bandits}, where each arm pull has probability $\varepsilon$ of generating a sample from an arbitrary contamination distribution instead of the true und…

New algorithm optimizes decision-making for complex systems with varying parameters.

Given a finite set of unknown distributions or arms that can be sampled, we consider the problem of identifying the one with the maximum mean using a $δ$-correct algorithm (an adaptive, sequential algorithm that restricts the probability of error to a specified $δ$) that has minimum sample complexity. Lower bounds for …

Linear contextual bandit is an important class of sequential decision making problems with a wide range of applications to recommender systems, online advertising, healthcare, and many other machine learning related tasks. While there is a lot of prior research, tight regret bounds of linear contextual bandit with infi…

New algorithm tackles multi-agent bandits with heavy-tailed data.

Unified framework for response-adaptive targeting in multi-treatment experiments

Model-free reinforcement learning is known to be memory and computation efficient and more amendable to large scale problems. In this paper, two model-free algorithms are introduced for learning infinite-horizon average-reward Markov Decision Processes (MDPs). The first algorithm reduces the problem to the discounted-r…

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.

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.