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Found 19 papers, 0 papers with code
A Stochastic Gradient Method with an Exponential Convergence _Rate for Finite Training Sets
We propose a new stochastic gradient method for optimizing the sum of a finite set of smooth functions, where the sum is strongly convex.
Multiple Operator-valued Kernel Learning
We study this problem in the case of kernel ridge regression for functional responses with an lr-norm constraint on the combination coefficients.
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning
We consider the minimization of a convex objective function defined on a Hilbert space, which is only available through unbiased estimates of its gradients.
Shaping Level Sets with Submodular Functions
We consider a class of sparsity-inducing regularization terms based on submodular functions.
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization
We consider the problem of optimizing the sum of a smooth convex function and a non-smooth convex function using proximal-gradient methods, where an error is present in the calculation of the gradient of the smooth term or in the proximity operator with respect to the second term.
Trace Lasso: a trace norm regularization for correlated designs
This norm, called the trace Lasso, uses the trace norm of the selected covariates, which is a convex surrogate of their rank, as the criterion of model complexity.
Structured sparsity-inducing norms through submodular functions
Sparse methods for supervised learning aim at finding good linear predictors from as few variables as possible, i. e., with small cardinality of their supports.
Online Learning for Latent Dirichlet Allocation
We develop an online variational Bayes (VB) algorithm for Latent Dirichlet Allocation (LDA).
Network Flow Algorithms for Structured Sparsity
Our algorithm scales up to millions of groups and variables, and opens up a whole new range of applications for structured sparse models.
Efficient Optimization for Discriminative Latent Class Models
To avoid this problem, we introduce a local approximation of this cost function, which leads to a quadratic non-convex optimization problem over a product of simplices.
Asymptotically Optimal Regularization in Smooth Parametric Models
Many types of regularization schemes have been employed in statistical learning, each one motivated by some assumption about the problem domain.
Data-driven calibration of linear estimators with minimal penalties
This paper tackles the problem of selecting among several linear estimators in non-parametric regression; this includes model selection for linear regression, the choice of a regularization parameter in kernel ridge regression or spline smoothing, and the choice of a kernel in multiple kernel learning.
Kernel Change-point Analysis
Change-point analysis of an (unlabelled) sample of observations consists in, first, testing whether a change in the distribution occurs within the sample, and second, if a change occurs, estimating the change-point instant after which the distribution of the observations switches from one distribution to another different distribution.
Clustered Multi-Task Learning: A Convex Formulation
In multi-task learning several related tasks are considered simultaneously, with the hope that by an appropriate sharing of information across tasks, each task may benefit from the others.
Supervised Dictionary Learning
It is now well established that sparse signal models are well suited to restoration tasks and can effectively be learned from audio, image, and video data.
Sparse probabilistic projections
We present a generative model for performing sparse probabilistic projections, which includes sparse principal component analysis and sparse canonical correlation analysis as special cases.
Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning
For supervised and unsupervised learning, positive definite kernels allow to use large and potentially infinite dimensional feature spaces with a computational cost that only depends on the number of observations.
DIFFRAC: a discriminative and flexible framework for clustering
We present a novel linear clustering framework (Diffrac) which relies on a linear discriminative cost function and a convex relaxation of a combinatorial optimization problem.
Testing for Homogeneity with Kernel Fisher Discriminant Analysis
This provides us with a consistent nonparametric test statistic, for which we derive the asymptotic distribution under the null hypothesis.
