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Found 6 papers, 1 papers with code
Computational Guarantees for Doubly Entropic Wasserstein Barycenters via Damped Sinkhorn Iterations
We study the computation of doubly regularized Wasserstein barycenters, a recently introduced family of entropic barycenters governed by inner and outer regularization strengths.
Local Risk Bounds for Statistical Aggregation
In this paper, we revisit and tighten classical results in the theory of aggregation in the statistical setting by replacing the global complexity with a smaller, local one.
Distribution-Free Robust Linear Regression
In this distribution-free regression setting, we show that boundedness of the conditional second moment of the response given the covariates is a necessary and sufficient condition for achieving nontrivial guarantees.
Suboptimality of Constrained Least Squares and Improvements via Non-Linear Predictors
We study the problem of predicting as well as the best linear predictor in a bounded Euclidean ball with respect to the squared loss.
The Statistical Complexity of Early-Stopped Mirror Descent
Recently there has been a surge of interest in understanding implicit regularization properties of iterative gradient-based optimization algorithms.
Implicit Regularization for Optimal Sparse Recovery
We investigate implicit regularization schemes for gradient descent methods applied to unpenalized least squares regression to solve the problem of reconstructing a sparse signal from an underdetermined system of linear measurements under the restricted isometry assumption.
