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08/02/2023
Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning
In this paper, we investigate the impact of compression on stochastic gr...
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06/05/2023
Conformal Prediction with Missing Values
Conformal prediction is a theoretically grounded framework for construct...
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02/22/2023
Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning
Stochastic approximation (SA) is a classical algorithm that has had sinc...
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01/31/2023
Naive imputation implicitly regularizes high-dimensional linear models
Two different approaches exist to handle missing values for prediction: ...
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02/15/2022
Adaptive Conformal Predictions for Time Series
Uncertainty quantification of predictive models is crucial in decision-m...
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02/03/2022
Minimax rate of consistency for linear models with missing values
Missing values arise in most real-world data sets due to the aggregation...
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01/11/2022
PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python
PEPit is a Python package aiming at simplifying the access to worst-case...
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11/17/2021
Differentially Private Federated Learning on Heterogeneous Data
Federated Learning (FL) is a paradigm for large-scale distributed learni...
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11/03/2021
Federated Expectation Maximization with heterogeneity mitigation and variance reduction
The Expectation Maximization (EM) algorithm is the default algorithm for...
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06/01/2021
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning
Federated learning aims at conducting inference when data are decentrali...
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02/24/2021
Preserved central model for faster bidirectional compression in distributed settings
We develop a new approach to tackle communication constraints in a distr...
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07/01/2020
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent
Constant step-size Stochastic Gradient Descent exhibits two phases: a tr...
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06/25/2020
Artemis: tight convergence guarantees for bidirectional compression in Federated Learning
We introduce a new algorithm - Artemis - tackling the problem of learnin...
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02/21/2020
Debiasing Stochastic Gradient Descent to handle missing values
A major caveat of large scale data is their incom-pleteness. We propose ...
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04/25/2019
Communication trade-offs for synchronized distributed SGD with large step size
Synchronous mini-batch SGD is state-of-the-art for large-scale distribut...
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01/30/2019
Unsupervised Scalable Representation Learning for Multivariate Time Series
Time series constitute a challenging data type for machine learning algo...
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08/29/2018
Wasserstein is all you need
We propose a unified framework for building unsupervised representations...
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07/20/2017
Bridging the Gap between Constant Step Size Stochastic Gradient Descent and Markov Chains
We consider the minimization of an objective function given access to un...
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02/17/2016
