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Found 6 papers, 3 papers with code
Efficient Primal-Dual Algorithms for Large-Scale Multiclass Classification
We develop efficient algorithms to train $\ell_1$-regularized linear classifiers with large dimensionality $d$ of the feature space, number of classes $k$, and sample size $n$.
Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance
We consider learning methods based on the regularization of a convex empirical risk by a squared Hilbertian norm, a setting that includes linear predictors and non-linear predictors through positive-definite kernels.
Affine Invariant Covariance Estimation for Heavy-Tailed Distributions
Denoting $\text{cond}(\mathbf{S})$ the condition number of $\mathbf{S}$, the computational cost of the novel estimator is $O(d^2 n + d^3\log(\text{cond}(\mathbf{S})))$, which is comparable to the cost of the sample covariance estimator in the statistically interesing regime $n \ge d$.
Finite-sample analysis of M-estimators using self-concordance
We demonstrate how self-concordance of the loss allows to characterize the critical sample size sufficient to guarantee a chi-square type in-probability bound for the excess risk.
Adaptive Denoising of Signals with Local Shift-Invariant Structure
We discuss the problem of adaptive discrete-time signal denoising in the situation where the signal to be recovered admits a "linear oracle" -- an unknown linear estimate that takes the form of convolution of observations with a time-invariant filter.
Efficient First-Order Algorithms for Adaptive Signal Denoising
Our second contribution is a computational complexity analysis of the proposed procedures, which takes into account their statistical nature and the related notion of statistical accuracy.
