
Differential Privacy at Risk: Bridging Randomness and Privacy Budget
The calibration of noise for a privacypreserving mechanism depends on t...
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Reinforcement learning with human advice. A survey
In this paper, we provide an overview of the existing methods for integr...
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InfoCatVAE: Representation Learning with Categorical Variational Autoencoders
This paper describes InfoCatVAE, an extension of the variational autoenc...
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DistributionBased Invariant Deep Networks for Learning MetaFeatures
Recent advances in deep learning from probability distributions enable t...
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Universal Invariant and Equivariant Graph Neural Networks
Graph Neural Networks (GNN) come in many flavors, but should always be e...
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Principled Training of Neural Networks with Direct Feedback Alignment
The backpropagation algorithm has long been the canonical training metho...
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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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Deep Network classification by Scattering and Homotopy dictionary learning
We introduce a sparse scattering deep convolutional neural network, whic...
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Degrees of freedom for offthegrid sparse estimation
A central question in modern machine learning and imaging sciences is to...
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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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Entropy and mutual information in models of deep neural networks
We examine a class of deep learning models with a tractable method to co...
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On the Universality of Noiseless Linear Estimation with Respect to the Measurement Matrix
In a noiseless linear estimation problem, one aims to reconstruct a vect...
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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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Asymptotic Bayes risk for Gaussian mixture in a semisupervised setting
Semisupervised learning (SSL) uses unlabeled data for training and has ...
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Brain Tumor Segmentation with Deep Neural Networks
In this paper, we present a fully automatic brain tumor segmentation met...
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Wavelet Scattering on the Pitch Spiral
We present a new representation of harmonic sounds that linearizes the d...
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Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity
In machine learning, error backpropagation in multilayer neural networ...
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Improving Recurrent Neural Networks For Sequence Labelling
In this paper we study different types of Recurrent Neural Networks (RNN...
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A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
Sleep stage classification constitutes an important preliminary exam in ...
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Compressive Statistical Learning with Random Feature Moments
We describe a general framework compressive statistical learning for...
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GAN and VAE from an Optimal Transport Point of View
This short article revisits some of the ideas introduced in arXiv:1701.0...
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Multiwinner Approval Rules as Apportionment Methods
We establish a link between multiwinner elections and apportionment prob...
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Stochastic Composite LeastSquares Regression with convergence rate O(1/n)
We consider the minimization of composite objective functions composed o...
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Scaling the Scattering Transform: Deep Hybrid Networks
We use the scattering network as a generic and fixed initialization of ...
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Proposal Flow: Semantic Correspondences from Object Proposals
Finding image correspondences remains a challenging problem in the prese...
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Building a Regular Decision Boundary with Deep Networks
In this work, we build a generic architecture of Convolutional Neural Ne...
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NonBacktracking Spectrum of DegreeCorrected Stochastic Block Models
Motivated by community detection, we characterise the spectrum of the no...
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Robust Discriminative Clustering with Sparse Regularizers
Clustering highdimensional data often requires some form of dimensional...
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Truncated Variational Sampling for "Black Box" Optimization of Generative Models
We investigate the optimization of two generative models with binary hid...
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Optimal Rates of Statistical Seriation
Given a matrix the seriation problem consists in permuting its rows in s...
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Online Optimization of Smoothed Piecewise Constant Functions
We study online optimization of smoothed piecewise constant functions ov...
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Spectral Ranking using Seriation
We describe a seriation algorithm for ranking a set of items given pairw...
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Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling
We study parameter inference in largescale latent variable models. We f...
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Harder, Better, Faster, Stronger Convergence Rates for LeastSquares Regression
We consider the optimization of a quadratic objective function whose gra...
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Congruences and Concurrent Lines in MultiView Geometry
We present a new framework for multiview geometry in computer vision. A...
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Streaming, Memory Limited Matrix Completion with Noise
In this paper, we consider the streaming memorylimited matrix completio...
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From Averaging to Acceleration, There is Only a Stepsize
We show that accelerated gradient descent, averaged gradient descent and...
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On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions
We show that kernelbased quadrature rules for computing integrals can b...
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Combinatorial Bandits Revisited
This paper investigates stochastic and adversarial combinatorial multia...
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Reconstruction in the Labeled Stochastic Block Model
The labeled stochastic block model is a random graph model representing ...
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Sparse and spurious: dictionary learning with noise and outliers
A popular approach within the signal processing and machine learning com...
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Edge Label Inference in Generalized Stochastic Block Models: from Spectral Theory to Impossibility Results
The classical setting of community detection consists of networks exhibi...
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On The Sample Complexity of Sparse Dictionary Learning
In the synthesis model signals are represented as a sparse combinations ...
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Minimizing Finite Sums with the Stochastic Average Gradient
We propose the stochastic average gradient (SAG) method for optimizing t...
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MCMC Learning
The theory of learning under the uniform distribution is rich and deep, ...
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Nonstronglyconvex smooth stochastic approximation with convergence rate O(1/n)
We consider the stochastic approximation problem where a convex function...
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A Riemannian lowrank method for optimization over semidefinite matrices with blockdiagonal constraints
We propose a new algorithm to solve optimization problems of the form f...
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LargeMargin Metric Learning for Partitioning Problems
In this paper, we consider unsupervised partitioning problems, such as c...
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Duality between subgradient and conditional gradient methods
Given a convex optimization problem and its dual, there are many possibl...
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Forecasting electricity consumption by aggregating specialized experts
We consider the setting of sequential prediction of arbitrary sequences ...
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Cole Normale Suprieure
The École Normale Supérieure (ENS) is a higher institution for predoctoral and graduate studies and a high place for French research. It hosts fourteen teaching and research departments, covering the bulk of literary and scientific disciplines.