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Found 5 papers, 1 papers with code
Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits
Assuming access to the distribution of the covariates, we propose a novel low-rank matrix estimation method called LowPopArt and provide its recovery guarantee that depends on a novel quantity denoted by B(Q) that characterizes the hardness of the problem, where Q is the covariance matrix of the measurement distribution.
Fixed Confidence Best Arm Identification in the Bayesian Setting
This problem aims to find the arm of the largest mean with a fixed confidence level when the bandit model has been sampled from the known prior.
Better-than-KL PAC-Bayes Bounds
In this paper, we consider the problem of proving concentration inequalities to estimate the mean of the sequence.
Tighter PAC-Bayes Bounds Through Coin-Betting
We consider the problem of estimating the mean of a sequence of random elements $f(X_1, \theta)$ $, \ldots, $ $f(X_n, \theta)$ where $f$ is a fixed scalar function, $S=(X_1, \ldots, X_n)$ are independent random variables, and $\theta$ is a possibly $S$-dependent parameter.
PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits
In this paper, we propose a simple and computationally efficient sparse linear estimation method called PopArt that enjoys a tighter $\ell_1$ recovery guarantee compared to Lasso (Tibshirani, 1996) in many problems.
