We present a novel approach to non-convex optimization with certificates...
We propose a novel non-parametric learning paradigm for the identificati...
This paper deals with the problem of efficient sampling from a stochasti...
Handling an infinite number of inequality constraints in infinite-dimens...
The workhorse of machine learning is stochastic gradient descent. To acc...
We consider potentially non-convex optimization problems, for which opti...
This paper studies an intriguing phenomenon related to the good
generali...
Measures of similarity (or dissimilarity) are a key ingredient to many
m...
Kernel mean embeddings are a powerful tool to represent probability
dist...
In many areas of applied statistics and machine learning, generating an
...
Mixability has been shown to be a powerful tool to obtain algorithms wit...
Finding a good way to model probability densities is key to probabilisti...
The theory of spectral filtering is a remarkable tool to understand the
...
The foundational concept of Max-Margin in machine learning is ill-posed ...
In this work we investigate the variation of the online kernelized ridge...
Machine learning approached through supervised learning requires expensi...
Discrete supervised learning problems such as classification are often
t...
We consider the global minimization of smooth functions based solely on
...
A powerful and flexible approach to structured prediction consists in
em...
Linear models have shown great effectiveness and flexibility in many fie...
Max-margin methods for binary classification such as the support vector
...
Kernel methods provide an elegant and principled approach to nonparametr...
We study the learning properties of nonparametric ridge-less least squar...
We present a novel approach to image restoration that leverages ideas fr...
We consider the setting of online logistic regression and consider the r...
Annotating datasets is one of the main costs in nowadays supervised lear...
We propose and analyze a novel theoretical and algorithmic framework for...
Within the framework of statistical learning theory it is possible to bo...
In this paper, we propose and study a Nyström based approach to efficien...
In this paper, we study large-scale convex optimization algorithms based...
We are interested in a framework of online learning with kernels for
low...
In this work we provide an estimator for the covariance matrix of a
heav...
We consider learning methods based on the regularization of a convex
emp...
In this work we provide a theoretical framework for structured predictio...
The Sinkhorn distance, a variant of the Wasserstein distance with entrop...
Leverage score sampling provides an appealing way to perform approximate...
Computing the quadratic transportation metric (also called the
2-Wassers...
The problem of devising learning strategies for discrete losses (e.g.,
m...
Sketching and stochastic gradient methods are arguably the most common t...
Structured prediction provides a general framework to deal with supervis...
Key to structured prediction is exploiting the problem structure to simp...
Applications of optimal transport have recently gained remarkable attent...
We consider stochastic gradient descent (SGD) for least-squares regressi...
Simulating the time-evolution of quantum mechanical systems is BQP-hard ...
We consider binary classification problems with positive definite kernel...
Kernel methods provide a principled way to perform non linear, nonparame...
Key to multitask learning is exploiting relationships between different ...
We propose and analyze a regularization approach for structured predicti...
We study the generalization properties of ridge regression with random
f...
Early stopping is a well known approach to reduce the time complexity fo...