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Found 30 papers, 1 papers with code
Active Ranking of Experts Based on their Performances in Many Tasks
We consider the problem of ranking n experts based on their performances on d tasks.
Online Learning with Feedback Graphs: The True Shape of Regret
Sequential learning with feedback graphs is a natural extension of the multi-armed bandit problem where the problem is equipped with an underlying graph structure that provides additional information - playing an action reveals the losses of all the neighbors of the action.
The price of unfairness in linear bandits with biased feedback
We also derive gap-dependent upper bounds on the regret, and matching lower bounds for some problem instance. Interestingly, these results reveal a transition between a regime where the problem is as difficult as its unbiased counterpart, and a regime where it can be much harder.
Problem Dependent View on Structured Thresholding Bandit Problems
Taking $K$ as the number of arms, we consider the case where (i) the sequence of arm's means $(\mu_k)_{k=1}^K$ is monotonically increasing (MTBP) and (ii) the case where $(\mu_k)_{k=1}^K$ is concave (CTBP).
Bandits with many optimal arms
We characterize the optimal learning rates both in the cumulative regret setting, and in the best-arm identification setting in terms of the problem parameters $T$ (the budget), $p^*$ and $\Delta$.
Generalized non-stationary bandits
On top of the switching bandit problem (\textbf{Case a}), we are interested in three concrete examples: (\textbf{b}) the means of the arms are local polynomials, (\textbf{c}) the means of the arms are locally smooth, and (\textbf{d}) the gaps of the arms have a bounded number of inflexion points and where the highest arm mean cannot vary too much in a short range.
The Elliptical Potential Lemma Revisited
This note proposes a new proof and new perspectives on the so-called Elliptical Potential Lemma.
Stochastic bandits with arm-dependent delays
Significant work has been recently dedicated to the stochastic delayed bandit setting because of its relevance in applications.
The Influence of Shape Constraints on the Thresholding Bandit Problem
We prove that the minimax rates for the regret are (i) $\sqrt{\log(K)K/T}$ for TBP, (ii) $\sqrt{\log(K)/T}$ for MTBP, (iii) $\sqrt{K/T}$ for UTBP and (iv) $\sqrt{\log\log K/T}$ for CTBP, where $K$ is the number of arms and $T$ is the budget.
Restless dependent bandits with fading memory
The analysis of slow mixing scenario is supported with a minmax lower bound, which (up to a $\log(T)$ factor) matches the obtained upper bound.
Local minimax rates for closeness testing of discrete distributions
We consider the closeness testing problem for discrete distributions.
Rotting bandits are not harder than stochastic ones
In stochastic multi-armed bandits, the reward distribution of each arm is assumed to be stationary.
A minimax near-optimal algorithm for adaptive rejection sampling
Rejection Sampling is a fundamental Monte-Carlo method.
Linear Bandits with Stochastic Delayed Feedback
Stochastic linear bandits are a natural and well-studied model for structured exploration/exploitation problems and are widely used in applications such as online marketing and recommendation.
An Adaptive Strategy for Active Learning with Smooth Decision Boundary
The problem of adaptivity (to unknown distributional parameters) has remained opened since the seminal work of Castro and Nowak (2007), which first established (active learning) rates for this setting.
Two-sample Hypothesis Testing for Inhomogeneous Random Graphs
Given a population of $m$ graphs from each model, we derive minimax separation rates for the problem of testing $P=Q$ against $d(P, Q)>\rho$.
Two-Sample Tests for Large Random Graphs Using Network Statistics
We consider a two-sample hypothesis testing problem, where the distributions are defined on the space of undirected graphs, and one has access to only one observation from each model.
Adaptivity to Noise Parameters in Nonparametric Active Learning
This work addresses various open questions in the theory of active learning for nonparametric classification.
Tight (Lower) Bounds for the Fixed Budget Best Arm Identification Bandit Problem
We consider the problem of \textit{best arm identification} with a \textit{fixed budget $T$}, in the $K$-armed stochastic bandit setting, with arms distribution defined on $[0, 1]$.
An optimal algorithm for the Thresholding Bandit Problem
We study a specific \textit{combinatorial pure exploration stochastic bandit problem} where the learner aims at finding the set of arms whose means are above a given threshold, up to a given precision, and \textit{for a fixed time horizon}.
Learning relationships between data obtained independently
The aim of this paper is to provide a new method for learning the relationships between data that have been obtained independently.
Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits
If the variance of the distributions were known, one could design an optimal sampling strategy by collecting a number of independent samples per distribution that is proportional to their variance.
Simple regret for infinitely many armed bandits
As in the cumulative regret setting of infinitely many armed bandits, the rate of the simple regret will depend on a parameter $\beta$ characterizing the distribution of the near-optimal arms.
Implementable confidence sets in high dimensional regression
This paper focuses on constructing a confidence set for \theta which contains \theta with high probability, whose diameter is adaptive to the unknown sparsity S, and which is implementable in practice.
Extreme bandits
In many areas of medicine, security, and life sciences, we want to allocate limited resources to different sources in order to detect extreme values.
Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions
We consider the problem of adaptive stratified sampling for Monte Carlo integration of a differentiable function given a finite number of evaluations to the function.
Online allocation and homogeneous partitioning for piecewise constant mean-approximation
We here consider an extension of this problem to the case when the arms are the cells of a finite partition P of a continuous sampling space X \subset \Real^d.
Bandit Algorithms boost Brain Computer Interfaces for motor-task selection of a brain-controlled button
A brain-computer interface (BCI) allows users to “communicate” with a computer without using their muscles.
Sparse Recovery with Brownian Sensing
We consider the problem of recovering the parameter alpha in R^K of a sparse function f, i. e. the number of non-zero entries of alpha is small compared to the number K of features, given noisy evaluations of f at a set of well-chosen sampling points.
Finite Time Analysis of Stratified Sampling for Monte Carlo
We consider the problem of stratified sampling for Monte-Carlo integration.
