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06/26/2023
GloptiNets: Scalable Non-Convex Optimization with Certificates
We present a novel approach to non-convex optimization with certificates...
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05/24/2023
Non-Parametric Learning of Stochastic Differential Equations with Fast Rates of Convergence
We propose a novel non-parametric learning paradigm for the identificati...
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03/30/2023
Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models
This paper deals with the problem of efficient sampling from a stochasti...
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01/16/2023
Approximation of optimization problems with constraints through kernel Sum-Of-Squares
Handling an infinite number of inequality constraints in infinite-dimens...
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05/26/2022
Active Labeling: Streaming Stochastic Gradients
The workhorse of machine learning is stochastic gradient descent. To acc...
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04/11/2022
Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares
We consider potentially non-convex optimization problems, for which opti...
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02/28/2022
On the Benefits of Large Learning Rates for Kernel Methods
This paper studies an intriguing phenomenon related to the good generali...
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02/11/2022
Measuring dissimilarity with diffeomorphism invariance
Measures of similarity (or dissimilarity) are a key ingredient to many m...
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01/31/2022
Nyström Kernel Mean Embeddings
Kernel mean embeddings are a powerful tool to represent probability dist...
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10/20/2021
Sampling from Arbitrary Functions via PSD Models
In many areas of applied statistics and machine learning, generating an ...
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10/08/2021
Mixability made efficient: Fast online multiclass logistic regression
Mixability has been shown to be a powerful tool to obtain algorithms wit...
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06/30/2021
PSD Representations for Effective Probability Models
Finding a good way to model probability densities is key to probabilisti...
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06/16/2021
Beyond Tikhonov: Faster Learning with Self-Concordant Losses via Iterative Regularization
The theory of spectral filtering is a remarkable tool to understand the ...
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05/31/2021
Max-Margin is Dead, Long Live Max-Margin!
The foundational concept of Max-Margin in machine learning is ill-posed ...
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02/06/2021
Online nonparametric regression with Sobolev kernels
In this work we investigate the variation of the online kernelized ridge...
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02/04/2021
Disambiguation of weak supervision with exponential convergence rates
Machine learning approached through supervised learning requires expensi...
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02/01/2021
Fast rates in structured prediction
Discrete supervised learning problems such as classification are often t...
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12/22/2020
Finding Global Minima via Kernel Approximations
We consider the global minimization of smooth functions based solely on ...
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07/29/2020
Learning Output Embeddings in Structured Prediction
A powerful and flexible approach to structured prediction consists in em...
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07/08/2020
Non-parametric Models for Non-negative Functions
Linear models have shown great effectiveness and flexibility in many fie...
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07/02/2020
Consistent Structured Prediction with Max-Min Margin Markov Networks
Max-margin methods for binary classification such as the support vector ...
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06/18/2020
Kernel methods through the roof: handling billions of points efficiently
Kernel methods provide an elegant and principled approach to nonparametr...
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06/17/2020
Interpolation and Learning with Scale Dependent Kernels
We study the learning properties of nonparametric ridge-less least squar...
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06/16/2020
Structured and Localized Image Restoration
We present a novel approach to image restoration that leverages ideas fr...
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03/18/2020
Efficient improper learning for online logistic regression
We consider the setting of online logistic regression and consider the r...
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03/02/2020
Structured Prediction with Partial Labelling through the Infimum Loss
Annotating datasets is one of the main costs in nowadays supervised lear...
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02/13/2020
A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings
We propose and analyze a novel theoretical and algorithmic framework for...
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01/28/2020
Fast quantum learning with statistical guarantees
Within the framework of statistical learning theory it is possible to bo...
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07/11/2019
Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling
In this paper, we propose and study a Nyström based approach to efficien...
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07/03/2019
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses
In this paper, we study large-scale convex optimization algorithms based...
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02/26/2019
Efficient online learning with kernels for adversarial large scale problems
We are interested in a framework of online learning with kernels for low...
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02/08/2019
Affine Invariant Covariance Estimation for Heavy-Tailed Distributions
In this work we provide an estimator for the covariance matrix of a heav...
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02/08/2019
Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance
We consider learning methods based on the regularization of a convex emp...
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02/05/2019
A General Theory for Structured Prediction with Smooth Convex Surrogates
In this work we provide a theoretical framework for structured predictio...
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12/12/2018
Massively scalable Sinkhorn distances via the Nyström method
The Sinkhorn distance, a variant of the Wasserstein distance with entrop...
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10/31/2018
On Fast Leverage Score Sampling and Optimal Learning
Leverage score sampling provides an appealing way to perform approximate...
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10/23/2018
Approximating the Quadratic Transportation Metric in Near-Linear Time
Computing the quadratic transportation metric (also called the 2-Wassers...
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10/16/2018
Sharp Analysis of Learning with Discrete Losses
The problem of devising learning strategies for discrete losses (e.g., m...
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07/17/2018
Learning with SGD and Random Features
Sketching and stochastic gradient methods are arguably the most common t...
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06/26/2018
Manifold Structured Prediction
Structured prediction provides a general framework to deal with supervis...
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06/06/2018
Localized Structured Prediction
Key to structured prediction is exploiting the problem structure to simp...
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05/30/2018
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance
Applications of optimal transport have recently gained remarkable attent...
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05/25/2018
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes
We consider stochastic gradient descent (SGD) for least-squares regressi...
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04/06/2018
Approximating Hamiltonian dynamics with the Nyström method
Simulating the time-evolution of quantum mechanical systems is BQP-hard ...
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12/13/2017
Exponential convergence of testing error for stochastic gradient methods
We consider binary classification problems with positive definite kernel...
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05/31/2017
FALKON: An Optimal Large Scale Kernel Method
Kernel methods provide a principled way to perform non linear, nonparame...
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05/23/2017
Consistent Multitask Learning with Nonlinear Output Relations
Key to multitask learning is exploiting relationships between different ...
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05/24/2016
A Consistent Regularization Approach for Structured Prediction
We propose and analyze a regularization approach for structured predicti...
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02/14/2016
Generalization Properties of Learning with Random Features
We study the generalization properties of ridge regression with random f...
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10/19/2015
