
Weighted Linear Bandits for NonStationary Environments
We consider a stochastic linear bandit model in which the available acti...
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Online Active Linear Regression via Thresholding
We consider the problem of online active learning to collect data for re...
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Scalable Algorithms for Learning HighDimensional Linear Mixed Models
Linear mixed models (LMMs) are used extensively to model dependecies of ...
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Robust approximate linear regression without correspondence
Estimating regression coefficients using unordered multisets of covariat...
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Nonparametric Functional Approximation with Delaunay Triangulation
We propose a differentiable nonparametric algorithm, the Delaunay triang...
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A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives
In this paper we analyze boosting algorithms in linear regression from a...
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Iterative Least Trimmed Squares for Mixed Linear Regression
Given a linear regression setting, Iterative Least Trimmed Squares (ILTS...
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Active Linear Regression
We consider the problem of active linear regression where a decision maker has to choose between several covariates to sample in order to obtain the best estimate β̂ of the parameter β^ of the linear model, in the sense of minimizing Eβ̂β^^2. Using bandit and convex optimization techniques we propose an algorithm to define the sampling strategy of the decision maker and we compare it with other algorithms. We provide theoretical guarantees of our algorithm in different settings, including a O(T^2) regret bound in the case where the covariates form a basis of the feature space, generalizing and improving existing results. Numerical experiments validate our theoretical findings.
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