
VarianceReduced Methods for Machine Learning
Stochastic optimization lies at the heart of machine learning, and its c...
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Deep Equals Shallow for ReLU Networks in Kernel Regimes
Deep networks are often considered to be more expressive than shallow on...
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Nonparametric Models for Nonnegative Functions
Linear models have shown great effectiveness and flexibility in many fie...
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Consistent Structured Prediction with MaxMin Margin Markov Networks
Maxmargin methods for binary classification such as the support vector ...
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DualFree Stochastic Decentralized Optimization with Variance Reduction
We consider the problem of training machine learning models on distribut...
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Structured and Localized Image Restoration
We present a novel approach to image restoration that leverages ideas fr...
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Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model
In the context of statistical supervised learning, the noiseless linear ...
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Principled Analyses and Design of FirstOrder Methods with Inexact Proximal Operators
Proximal operations are among the most common primitives appearing in bo...
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ARIANN: LowInteraction PrivacyPreserving Deep Learning via Function Secret Sharing
We propose ARIANN, a lowinteraction framework to perform private traini...
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An Optimal Algorithm for Decentralized Finite Sum Optimization
Modern largescale finitesum optimization relies on two key aspects: di...
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Explicit Regularization of Stochastic Gradient Methods through Duality
We consider stochastic gradient methods under the interpolation regime w...
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On the Convergence of Adam and Adagrad
We provide a simple proof of the convergence of the optimization algorit...
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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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Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization
We consider the setting of distributed empirical risk minimization where...
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Safe Screening for the Generalized Conditional Gradient Method
The conditional gradient method (CGM) has been widely used for fast spar...
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Stochastic Optimization for Regularized Wasserstein Estimators
Optimal transport is a foundational problem in optimization, that allows...
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Learning with Differentiable Perturbed Optimizers
Machine learning pipelines often rely on optimization procedures to make...
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Implicit Bias of Gradient Descent for Wide Twolayer Neural Networks Trained with the Logistic Loss
Neural networks trained to minimize the logistic (a.k.a. crossentropy) ...
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On the Effectiveness of Richardson Extrapolation in Machine Learning
Richardson extrapolation is a classical technique from numerical analysi...
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Music Source Separation in the Waveform Domain
Source separation for music is the task of isolating contributions, or s...
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UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization
We propose a novel adaptive, accelerated algorithm for the stochastic co...
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Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed
We study the problem of source separation for music using deep learning ...
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Towards closing the gap between the theory and practice of SVRG
Among the very first variance reduced stochastic methods for solving the...
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Globally Convergent Newton Methods for Illconditioned Generalized Selfconcordant Losses
In this paper, we study largescale convex optimization algorithms based...
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MaxPlus Matching Pursuit for Deterministic Markov Decision Processes
We consider deterministic Markov decision processes (MDPs) and apply max...
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Fast Decomposable Submodular Function Minimization using Constrained Total Variation
We consider the problem of minimizing the sum of submodular set function...
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An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums
Modern largescale finitesum optimization relies on two key aspects: di...
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Partially Encrypted Machine Learning using Functional Encryption
Machine learning on encrypted data has received a lot of attention thank...
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Implicit Regularization of Discrete Gradient Dynamics in Deep Linear Neural Networks
When optimizing overparameterized models, such as deep neural networks,...
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Unsupervised Image Matching and Object Discovery as Optimization
Learning with complete or partial supervision is powerful but relies on ...
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Efficient PrimalDual Algorithms for LargeScale Multiclass Classification
We develop efficient algorithms to train ℓ_1regularized linear classifi...
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Beyond LeastSquares: Fast Rates for Regularized Empirical Risk Minimization through SelfConcordance
We consider learning methods based on the regularization of a convex emp...
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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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A Universal Algorithm for Variational Inequalities Adaptive to Smoothness and Noise
We consider variational inequalities coming from monotone operators, a s...
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Stochastic firstorder methods: nonasymptotic and computeraided analyses via potential functions
We provide a novel computerassisted technique for systematically analyz...
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Asynchronous Accelerated Proximal Stochastic Gradient for Strongly Convex Distributed Finite Sums
In this work, we study the problem of minimizing the sum of strongly con...
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Overcomplete Independent Component Analysis via SDP
We present a novel algorithm for overcomplete independent components ana...
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A Note on Lazy Training in Supervised Differentiable Programming
In a series of recent theoretical works, it has been shown that strongly...
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Massively scalable Sinkhorn distances via the Nyström method
The Sinkhorn distance, a variant of the Wasserstein distance with entrop...
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Marginal Weighted Maximum Loglikelihood for Efficient Learning of PerturbandMap models
We consider the structuredoutput prediction problem through probabilist...
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Approximating the Quadratic Transportation Metric in NearLinear Time
Computing the quadratic transportation metric (also called the 2Wassers...
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SING: SymboltoInstrument Neural Generator
Recent progress in deep learning for audio synthesis opens the way to mo...
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Fast and Faster Convergence of SGD for OverParameterized Models and an Accelerated Perceptron
Modern machine learning focuses on highly expressive models that are abl...
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Sharp Analysis of Learning with Discrete Losses
The problem of devising learning strategies for discrete losses (e.g., m...
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Finitesample Analysis of Mestimators using Selfconcordance
We demonstrate how selfconcordance of the loss can be exploited to obta...
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Sample Complexity of Sinkhorn divergences
Optimal transport (OT) and maximum mean discrepancies (MMD) are now rout...
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Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex Objectives
In this paper, we study the problem of minimizing a sum of smooth and st...
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Localized Structured Prediction
Key to structured prediction is exploiting the problem structure to simp...
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Nonlinear Acceleration of CNNs
The Regularized Nonlinear Acceleration (RNA) algorithm is an acceleratio...
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Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes
We consider stochastic gradient descent (SGD) for leastsquares regressi...
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