Search Results for author: Alex Nowak-Vila

Found 6 papers, 2 papers with code

On the Consistency of Max-Margin Losses

no code implementations31 May 2021 Alex Nowak-Vila, Alessandro Rudi, Francis Bach

The resulting loss is also a generalization of the binary support vector machine and it is consistent under milder conditions on the discrete loss.

Structured Prediction

Consistent Structured Prediction with Max-Min Margin Markov Networks

1 code implementation ICML 2020 Alex Nowak-Vila, Francis Bach, Alessandro Rudi

Max-margin methods for binary classification such as the support vector machine (SVM) have been extended to the structured prediction setting under the name of max-margin Markov networks ($M^3N$), or more generally structural SVMs.

Generalization Bounds Multi-class Classification +1

Structured and Localized Image Restoration

no code implementations16 Jun 2020 Thomas Eboli, Alex Nowak-Vila, Jian Sun, Francis Bach, Jean Ponce, Alessandro Rudi

We present a novel approach to image restoration that leverages ideas from localized structured prediction and non-linear multi-task learning.

Image Restoration Multi-Task Learning +1

A General Theory for Structured Prediction with Smooth Convex Surrogates

no code implementations5 Feb 2019 Alex Nowak-Vila, Francis Bach, Alessandro Rudi

In this work we provide a theoretical framework for structured prediction that generalizes the existing theory of surrogate methods for binary and multiclass classification based on estimating conditional probabilities with smooth convex surrogates (e. g. logistic regression).

General Classification Graph Matching +2

Sharp Analysis of Learning with Discrete Losses

no code implementations16 Oct 2018 Alex Nowak-Vila, Francis Bach, Alessandro Rudi

The problem of devising learning strategies for discrete losses (e. g., multilabeling, ranking) is currently addressed with methods and theoretical analyses ad-hoc for each loss.

Divide and Conquer Networks

1 code implementation ICLR 2018 Alex Nowak-Vila, David Folqué, Joan Bruna

Moreover, thanks to the dynamic aspect of our architecture, we can incorporate the computational complexity as a regularization term that can be optimized by backpropagation.

Inductive Bias

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