no code implementations • 23 Feb 2023 • Alex Nowak-Vila, Kevin Elgui, Genevieve Robin
The introduction of machine learning (ML) techniques to the field of survival analysis has increased the flexibility of modeling approaches, and ML based models have become state-of-the-art.
no code implementations • 31 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.
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.
no code implementations • 16 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.
no code implementations • 5 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).
no code implementations • 16 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.
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.