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05/18/2023
Modified Gauss-Newton Algorithms under Noise
Gauss-Newton methods and their stochastic version have been widely used ...
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12/31/2022
Confidence Sets under Generalized Self-Concordance
This paper revisits a fundamental problem in statistical inference from ...
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12/10/2022
Stochastic Optimization for Spectral Risk Measures
Spectral risk objectives - also called L-risks - allow for learning syst...
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10/02/2022
Stochastic optimization on matrices and a graphon McKean-Vlasov limit
We consider stochastic gradient descents on the space of large symmetric...
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07/13/2022
Iterative Linear Quadratic Optimization for Nonlinear Control: Differentiable Programming Algorithmic Templates
We present the implementation of nonlinear control algorithms based on l...
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04/30/2022
Orthogonal Statistical Learning with Self-Concordant Loss
Orthogonal statistical learning and double machine learning have emerged...
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12/31/2021
Triangular Flows for Generative Modeling: Statistical Consistency, Smoothness Classes, and Fast Rates
Triangular flows, also known as Knöthe-Rosenblatt measure couplings, com...
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12/31/2021
Entropy Regularized Optimal Transport Independence Criterion
Optimal transport (OT) and its entropy regularized offspring have recent...
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12/17/2021
Federated Learning with Heterogeneous Data: A Superquantile Optimization Approach
We present a federated learning framework that is designed to robustly d...
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12/02/2021
Target Propagation via Regularized Inversion
Target Propagation (TP) algorithms compute targets instead of gradients ...
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08/16/2021
Stochastic optimization under time drift: iterate averaging, step decay, and high probability guarantees
We consider the problem of minimizing a convex function that is evolving...
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06/27/2021
Score-Based Change Detection for Gradient-Based Learning Machines
The widespread use of machine learning algorithms calls for automatic ch...
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06/15/2021
Divergence Frontiers for Generative Models: Sample Complexity, Quantization Level, and Frontier Integral
The spectacular success of deep generative models calls for quantitative...
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02/02/2021
MAUVE: Human-Machine Divergence Curves for Evaluating Open-Ended Text Generation
Despite major advances in open-ended text generation, there has been lim...
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12/31/2020
Differentiable Programming à la Moreau
The notion of a Moreau envelope is central to the analysis of first-orde...
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12/12/2020
Faster Policy Learning with Continuous-Time Gradients
We study the estimation of policy gradients for continuous-time systems ...
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11/17/2020
Asymptotics of Entropy-Regularized Optimal Transport via Chaos Decomposition
Consider the problem of estimating the optimal coupling (i.e., matching)...
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09/30/2020
First-order Optimization for Superquantile-based Supervised Learning
Classical supervised learning via empirical risk (or negative log-likeli...
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03/28/2020
Harmonic Decompositions of Convolutional Networks
We consider convolutional networks from a reproducing kernel Hilbert spa...
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02/28/2020
Risk Bounds for Multi-layer Perceptrons through Spectra of Integral Operators
We characterize the behavior of integral operators associated with multi...
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02/25/2020
Device Heterogeneity in Federated Learning: A Superquantile Approach
We propose a federated learning framework to handle heterogeneous client...
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02/20/2020
An Elementary Approach to Convergence Guarantees of Optimization Algorithms for Deep Networks
We present an approach to obtain convergence guarantees of optimization ...
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12/31/2019
Robust Aggregation for Federated Learning
We present a robust aggregation approach to make federated learning robu...
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12/30/2019
End-to-end Learning, with or without Labels
We present an approach for end-to-end learning that allows one to jointl...
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12/10/2019
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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04/08/2019
A Statistical Investigation of Long Memory in Language and Music
Representation and learning of long-range dependencies is a central chal...
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03/19/2019
Kernel-based Translations of Convolutional Networks
Convolutional Neural Networks, as most artificial neural networks, are c...
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02/08/2019
A Smoother Way to Train Structured Prediction Models
We present a framework to train a structured prediction model by perform...
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12/06/2018
Object Discovery in Videos as Foreground Motion Clustering
We consider the problem of providing dense segmentation masks for object...
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11/20/2018
Coupled Recurrent Models for Polyphonic Music Composition
This work describes a novel recurrent model for music composition, which...
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06/11/2018
Adaptive Denoising of Signals with Shift-Invariant Structure
We study the problem of discrete-time signal denoising, following the li...
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03/29/2018
Efficient First-Order Algorithms for Adaptive Signal Denoising
We consider the problem of discrete-time signal denoising, focusing on a...
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12/15/2017
Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice
We introduce a generic scheme for accelerating gradient-based optimizati...
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11/13/2017
Invariances and Data Augmentation for Supervised Music Transcription
This paper explores a variety of models for frame-based music transcript...
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03/31/2017
Catalyst Acceleration for Gradient-Based Non-Convex Optimization
We introduce a generic scheme to solve nonconvex optimization problems u...
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10/04/2016
A Generic Quasi-Newton Algorithm for Faster Gradient-Based Optimization
We propose a generic approach to accelerate gradient-based optimization ...
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08/03/2016
Fast and Simple Optimization for Poisson Likelihood Models
Poisson likelihood models have been prevalently used in imaging, social ...
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07/02/2016
Rademacher Complexity Bounds for a Penalized Multiclass Semi-Supervised Algorithm
We propose Rademacher complexity bounds for multiclass classifiers train...
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03/01/2016
Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach
Convolutional neural networks (CNNs) have recently received a lot of att...
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06/25/2015
DeepMatching: Hierarchical Deformable Dense Matching
We introduce a novel matching algorithm, called DeepMatching, to compute...
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06/05/2015
Learning to track for spatio-temporal action localization
We propose an effective approach for spatio-temporal action localization...
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03/30/2015
Label-Embedding for Image Classification
Attributes act as intermediate representations that enable parameter sha...
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01/12/2015
EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow
We propose a novel approach for optical flow estimation , targeted at la...
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06/12/2014
Convolutional Kernel Networks
An important goal in visual recognition is to devise image representatio...
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05/26/2014
Fast and Robust Archetypal Analysis for Representation Learning
We revisit a pioneer unsupervised learning technique called archetypal a...
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02/10/2013
Conditional Gradient Algorithms for Norm-Regularized Smooth Convex Optimization
Motivated by some applications in signal processing and machine learning...
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04/07/2008