
Efficient and Modular Implicit Differentiation
Automatic differentiation (autodiff) has revolutionized machine learning...
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Implicit differentiation for fast hyperparameter selection in nonsmooth convex learning
Finding the optimal hyperparameters of a model can be cast as a bilevel ...
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SelfSupervised Learning of Audio Representations from Permutations with Differentiable Ranking
Selfsupervised pretraining using socalled "pretext" tasks has recentl...
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Momentum Residual Neural Networks
The training of deep residual neural networks (ResNets) with backpropaga...
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Differentiable Divergences Between Time Series
Computing the discrepancy between time series of variable sizes is notor...
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Implicit differentiation of Lassotype models for hyperparameter optimization
Setting regularization parameters for Lassotype estimators is notorious...
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Fast Differentiable Sorting and Ranking
The sorting operation is one of the most basic and commonly used buildin...
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Learning with Differentiable Perturbed Optimizers
Machine learning pipelines often rely on optimization procedures to make...
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Structured Prediction with Projection Oracles
We propose in this paper a general framework for deriving loss functions...
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Geometric Losses for Distributional Learning
Building upon recent advances in entropyregularized optimal transport, ...
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Learning with FenchelYoung Losses
Over the past decades, numerous loss functions have been been proposed f...
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Learning Classifiers with FenchelYoung Losses: Generalized Entropies, Margins, and Algorithms
We study in this paper FenchelYoung losses, a generic way to construct ...
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Blind Source Separation with Optimal Transport Nonnegative Matrix Factorization
Optimal transport as a loss for machine learning optimization problems h...
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SparseMAP: Differentiable Sparse Structured Inference
Structured prediction requires searching over a combinatorial number of ...
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Differentiable Dynamic Programming for Structured Prediction and Attention
Dynamic programming (DP) solves a variety of structured combinatorial pr...
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LargeScale Optimal Transport and Mapping Estimation
This paper presents a novel twostep approach for the fundamental proble...
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Smooth and Sparse Optimal Transport
Entropic regularization is quickly emerging as a new standard in optimal...
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A Regularized Framework for Sparse and Structured Neural Attention
Modern neural networks are often augmented with an attention mechanism, ...
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Multioutput Polynomial Networks and Factorization Machines
Factorization machines and polynomial networks are supervised polynomial...
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SoftDTW: a Differentiable Loss Function for TimeSeries
We propose in this paper a differentiable learning loss between time ser...
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Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms
Polynomial networks and factorization machines are two recentlyproposed...
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HigherOrder Factorization Machines
Factorization machines (FMs) are a supervised learning approach that can...
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Mathieu Blondel
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