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07/10/2023
Temporal network compression via network hashing
Pairwise temporal interactions between entities can be represented as te...
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07/03/2023
Butterfly factorization by algorithmic identification of rank-one blocks
Many matrices associated with fast transforms posess a certain low-rank ...
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06/30/2023
Abide by the Law and Follow the Flow: Conservation Laws for Gradient Flows
Understanding the geometric properties of gradient descent dynamics is a...
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06/26/2023
About the Cost of Global Privacy in Density Estimation
We study non-parametric density estimation for densities in Lipschitz an...
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06/05/2023
Does a sparse ReLU network training problem always admit an optimum?
Given a training set, a loss function, and a neural network architecture...
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02/14/2023
Private Statistical Estimation of Many Quantiles
This work studies the estimation of many statistical quantiles under dif...
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10/05/2022
On the Statistical Complexity of Estimation and Testing under Privacy Constraints
Producing statistics that respect the privacy of the samples while still...
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07/28/2022
Self-supervised learning with rotation-invariant kernels
A major paradigm for learning image representations in a self-supervised...
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06/13/2022
Compressive Clustering with an Optical Processing Unit
We explore the use of Optical Processing Units (OPU) to compute random F...
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05/24/2022
Approximation speed of quantized vs. unquantized ReLU neural networks and beyond
We consider general approximation families encompassing ReLU neural netw...
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02/15/2022
Private Quantiles Estimation in the Presence of Atoms
We address the differentially private estimation of multiple quantiles (...
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12/01/2021
Controlling Wasserstein distances by Kernel norms with application to Compressive Statistical Learning
Comparing probability distributions is at the crux of many machine learn...
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12/01/2021
Spurious Valleys, Spurious Minima and NP-hardness of Sparse Matrix Factorization With Fixed Support
The problem of approximating a dense matrix by a product of sparse facto...
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10/04/2021
Identifiability in Exact Two-Layer Sparse Matrix Factorization
Sparse matrix factorization is the problem of approximating a matrix Z b...
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10/04/2021
Identifiability in Exact Multilayer Sparse Matrix Factorization
Many well-known matrices Z are associated to fast transforms correspondi...
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09/20/2021
Nonsmooth convex optimization to estimate the Covid-19 reproduction number space-time evolution with robustness against low quality data
Daily pandemic surveillance, often achieved through the estimation of th...
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07/20/2021
An Embedding of ReLU Networks and an Analysis of their Identifiability
Neural networks with the Rectified Linear Unit (ReLU) nonlinearity are d...
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04/29/2021
Fast Multiscale Diffusion on Graphs
Diffusing a graph signal at multiple scales requires computing the actio...
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01/05/2021
Minibatch optimal transport distances; analysis and applications
Optimal transport distances have become a classic tool to compare probab...
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09/14/2020
Continuous dictionaries meet low-rank tensor approximations
In this short paper we bridge two seemingly unrelated sparse approximati...
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08/04/2020
Sketching Datasets for Large-Scale Learning (long version)
This article considers "sketched learning," or "compressive learning," a...
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05/19/2020
Sparsity-based audio declipping methods: overview, new algorithms, and large-scale evaluation
Recent advances in audio declipping have substantially improved the stat...
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04/17/2020
Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling
We provide statistical learning guarantees for two unsupervised learning...
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04/15/2020
Training with Quantization Noise for Extreme Model Compression
We tackle the problem of producing compact models, maximizing their accu...
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04/15/2020
Training with Quantization Noise for Extreme Fixed-Point Compression
We tackle the problem of producing compact models, maximizing their accu...
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10/09/2019
Learning with minibatch Wasserstein : asymptotic and gradient properties
Optimal transport distances are powerful tools to compare probability di...
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07/12/2019
And the Bit Goes Down: Revisiting the Quantization of Neural Networks
In this paper, we address the problem of reducing the memory footprint o...
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07/03/2019
Don't take it lightly: Phasing optical random projections with unknown operators
In this paper we tackle the problem of recovering the phase of complex l...
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05/03/2019
Approximation spaces of deep neural networks
We study the expressivity of deep neural networks. Measuring a network's...
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04/12/2019
When does OMP achieve support recovery with continuous dictionaries?
This paper presents new theoretical results on sparse recovery guarantee...
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04/12/2019
When does OMP achieves support recovery with continuous dictionaries?
This paper presents new theoretical results on sparse recovery guarantee...
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02/27/2019
Equi-normalization of Neural Networks
Modern neural networks are over-parametrized. In particular, each rectif...
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12/17/2018
Stable safe screening and structured dictionaries for faster ℓ_1 regularization
In this paper, we propose a way to combine two acceleration techniques f...
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10/31/2018
MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval
This paper addresses the general problem of blind echo retrieval, i.e., ...
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10/18/2018
Concentration of the Frobenius norm of generalized matrix inverses
In many applications it is useful to replace the Moore-Penrose pseudoinv...
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10/18/2018
Concentration of the Frobenius norms of generalized matrix inverses
In many applications it is useful to replace the Moore-Penrose pseudoinv...
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10/18/2018
Concentration of the Frobenius norms of pseudoinverses
In many applications it is useful to replace the Moore-Penrose pseudoinv...
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07/11/2018
On Bayesian Estimation And Proximity Operators
There are two major routes to address the ubiquitous family of inverse p...
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06/21/2018
Is the 1-norm the best convex sparse regularization?
The 1-norm is a good convex regularization for the recovery of sparse ve...
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02/27/2018
Instance Optimal Decoding and the Restricted Isometry Property
In this paper, we study the preservation of information in ill-posed non...
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12/12/2017
Learning a Complete Image Indexing Pipeline
To work at scale, a complete image indexing system comprises two compone...
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12/07/2017
Sketched Clustering via Hybrid Approximate Message Passing
In sketched clustering, the dataset is first sketched down to a vector o...
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11/30/2017
A modeling and algorithmic framework for (non)social (co)sparse audio restoration
We propose a unified modeling and algorithmic framework for audio restor...
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11/01/2017
Analyzing the Approximation Error of the Fast Graph Fourier Transform
The graph Fourier transform (GFT) is in general dense and requires O(n^2...
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08/09/2017
SUBIC: A supervised, structured binary code for image search
For large-scale visual search, highly compressed yet meaningful represen...
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06/22/2017
Compressive Statistical Learning with Random Feature Moments
We describe a general framework --compressive statistical learning-- for...
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10/27/2016
Compressive K-means
The Lloyd-Max algorithm is a classical approach to perform K-means clust...
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08/10/2016
Approximate search with quantized sparse representations
This paper tackles the task of storing a large collection of vectors, su...
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06/09/2016
Sketching for Large-Scale Learning of Mixture Models
Learning parameters from voluminous data can be prohibitive in terms of ...
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02/05/2016