Learning with Fenchel-Young Losses

8 January 2019

André F. T. Martins

Papers citing "Learning with Fenchel-Young Losses"

28 / 28 papers shown

Title
Establishing Linear Surrogate Regret Bounds for Convex Smooth Losses via Convolutional Fenchel-Young Losses Yuzhou Cao Han Bao Lei Feng Bo An 24 0 0 14 May 2025
Primal-dual algorithm for contextual stochastic combinatorial optimization Louis Bouvier Thibault Prunet Vincent Leclère Axel Parmentier 37 0 0 07 May 2025
Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel $-$ Young Loss Perspective and Gap-Dependent Regret Analysis Shinsaku Sakaue Han Bao Taira Tsuchiya 45 1 0 23 Jan 2025
Soft Condorcet Optimization for Ranking of General Agents Marc Lanctot Kate Larson Michael Kaisers Quentin Berthet I. Gemp Manfred Diaz Roberto-Rafael Maura-Rivero Yoram Bachrach Anna Koop Doina Precup 49 0 0 31 Oct 2024
LPGD: A General Framework for Backpropagation through Embedded Optimization Layers Anselm Paulus Georg Martius Vít Musil AI4CE 55 1 0 08 Jul 2024
Building a stable classifier with the inflated argmax Jake A. Soloff Rina Foygel Barber Rebecca Willett 165 2 0 22 May 2024
Trading off Consistency and Dimensionality of Convex Surrogates for the Mode Enrique Nueve Bo Waggoner Dhamma Kimpara Jessie Finocchiaro 44 1 0 16 Feb 2024
Neural Collapse in Multi-label Learning with Pick-all-label Loss Pengyu Li Xiao Li Yutong Wang Qing Qu 35 8 0 24 Oct 2023
Differentiable Clustering with Perturbed Spanning Forests Lawrence Stewart Francis R. Bach Felipe Llinares-López Quentin Berthet 34 8 0 25 May 2023
A Statistical Learning Take on the Concordance Index for Survival Analysis Alex Nowak-Vila K. Elgui Geneviève Robin 20 1 0 23 Feb 2023
Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective Michael E. Sander J. Puigcerver Josip Djolonga Gabriel Peyré Mathieu Blondel 21 19 0 02 Feb 2023
Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming Chunlin Sun Shang Liu Xiaocheng Li 29 9 0 26 Jan 2023
On the inconsistency of separable losses for structured prediction Caio Corro 9 3 0 25 Jan 2023
SIMPLE: A Gradient Estimator for $k$ -Subset Sampling Kareem Ahmed Zhe Zeng Mathias Niepert Mathias Niepert BDL 48 25 0 04 Oct 2022
Rank-based Decomposable Losses in Machine Learning: A Survey Shu Hu Xin Wang Siwei Lyu 40 32 0 18 Jul 2022
Contrasting quadratic assignments for set-based representation learning A. Moskalev Ivan Sosnovik Volker Fischer A. Smeulders SSL 32 9 0 31 May 2022
Tutorial on amortized optimization Brandon Amos OffRL 78 43 0 01 Feb 2022
Taming Overconfident Prediction on Unlabeled Data from Hindsight Jing Li Yuangang Pan Ivor W. Tsang 21 1 0 15 Dec 2021
Multimodal Continuous Visual Attention Mechanisms António Farinhas André F. T. Martins P. Aguiar 22 7 0 07 Apr 2021
Self-Supervised Learning of Audio Representations from Permutations with Differentiable Ranking Andrew N. Carr Quentin Berthet Mathieu Blondel O. Teboul Neil Zeghidour SSL 24 24 0 17 Mar 2021
Fast rates in structured prediction Vivien A. Cabannes Alessandro Rudi Francis R. Bach 20 19 0 01 Feb 2021
Gradient Estimation with Stochastic Softmax Tricks Max B. Paulus Dami Choi Daniel Tarlow Andreas Krause Chris J. Maddison BDL 36 85 0 15 Jun 2020
Learning with Differentiable Perturbed Optimizers Quentin Berthet Mathieu Blondel O. Teboul Marco Cuturi Jean-Philippe Vert Francis R. Bach 29 106 0 20 Feb 2020
Structured Prediction with Projection Oracles Mathieu Blondel 19 33 0 24 Oct 2019
The Limited Multi-Label Projection Layer Brandon Amos V. Koltun J. Zico Kolter 24 36 0 20 Jun 2019
Soft-DTW: a Differentiable Loss Function for Time-Series Marco Cuturi Mathieu Blondel AI4TS 141 611 0 05 Mar 2017
Regularized Optimal Transport and the Rot Mover's Distance Arnaud Dessein Nicolas Papadakis Jean-Luc Rouas OT 56 84 0 20 Oct 2016
Metric Learning for Temporal Sequence Alignment Damien Garreau Rémi Lajugie Sylvain Arlot Francis R. Bach AI4TS 125 57 0 10 Sep 2014