Publications associées à Distributed Inference Over Networks Under Subspace Constraints

Publications associées (47)

Scalable kernel logistic regression with Nyström approximation: Theoretical analysis and application to discrete choice modelling

The application of kernel-based Machine Learning (ML) techniques to discrete choice modelling using large datasets often faces challenges due to memory requirements and the considerable number of parameters involved in these models. This complexity hampers ...

2025

Boosting Resource-Constrained Federated Learning Systems with Guessed Updates

Mohamed Yassine Boukhari, Anne-Marie Kermarrec, Rafael Pereira Pires, Rishi Sharma, Akash Balasaheb Dhasade

Federated learning (FL) enables a set of client devices to collaboratively train a model without sharing raw data. This process, though, operates under the constrained computation and communication resources of edge devices. These constraints combined with ...

Institute of Electrical and Electronics Engineers (IEEE)2025

Secure and federated genome-wide association studies for biobank-scale datasets

Jean-Pierre Hubaux, Apostolos Pyrgelis

Sharing data across institutions for genome-wide association studies (GWAS) would enhance the discovery of genetic variation linked to health and disease1,2. However, existing data-sharing regulations limit the scope of such collaborations3. Although crypt ...

NATURE PORTFOLIO2025

How Far Can a 1-Pixel Camera Go? Solving Vision Tasks Using Photoreceptors and Computationally Designed Visual Morphology

Jiawei Fu, Isabella Lucile Yu, Amir Roshan Zamir, Andrei Atanov, Rishubh Singh

A de facto standard approach in solving computer vision tasks is to use a common high-resolution camera and choose its placement on an agent based on human intuition. On the other hand, extremely simple and well-designed visual sensors found throughout nat ...

Springer Science and Business Media Deutschland GmbH2025

K-space physics-informed neural network (k-PINN) for compressed spectral mapping and efficient inversion of vibrations in thin composite laminates

Olga Fink

The vibrational response of structural components carries valuable information about their underlying mechanical properties, health status and operational conditions. This underscores the need for the development of efficient physics-based inversion algori ...

Elsevier BV2025

A learning-based approach to stochastic optimal control under reach-avoid constraint

Maryam Kamgarpour, Tingting Ni

We develop a model-free approach to optimally control stochastic, Markovian systems subject to a reach-avoid constraint. Specifically, the state trajectory must remain within a safe set while reaching a target set within a finite time horizon. Due to the t ...

Association for Computing Machinery2025

Overcoming Distribution Shifts and Imbalance Challenges in Representation Learning for Deep Regression Models

Ismail Nejjar

Deep learning has shown remarkable potential for industrial applications, particularly in predictive maintenance and condition monitoring. A key challenge in this domain is prognostics, specifically predicting the Remaining Useful Life (RUL) of assets - a ...

EPFL2025

Sequential Reasoning with Socially Caused Beliefs

Mert Kayaalp

Machine learning and artificial intelligence methods have achieved remarkable success, matching and even surpassing human capabilities in various complex tasks. However, many demonstrations have generally neglected a critical part of the intelligence that ...

EPFL2025

Stochastic adaptive resampling for the estimation of discrete choice models

Michel Bierlaire, Matthieu Marie Cochon de Lapparent, Nicola Marco Ortelli

In the field of choice modeling, the availability of ever-larger datasets has the potential to significantly expand our understanding of human behavior, but this prospect is limited by the poor scalability of discrete choice models (DCMs): as sample sizes ...

2023

Riemannian acceleration with preconditioning for symmetric eigenvalue problems

Nian Shao

The analysis of the acceleration behavior of gradient-based eigensolvers with preconditioning presents a substantial theoretical challenge. In this work, we present a novel framework for preconditioning on Riemannian manifolds and introduce a metric, the l ...

Springer2025

Universal and adaptive methods for robust stochastic optimization

Ali Kavis

Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...

EPFL2023

Fair Decentralized Learning

Anne-Marie Kermarrec, Rishi Sharma, Marinus Abraham de Vos, Sayan Biswas

Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level of hete ...

IEEE2025

Robust Collaborative Learning with Linear Gradient Overhead

Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot, Le Nguyen Hoang, Sadegh Farhadkhani, John Stephan

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

PLMR2023

Phenomenological theory of variational quantum ground-state preparation

Ivano Tavernelli, Giuseppe Carleo

The variational approach is a cornerstone of computational physics, considering both conventional and quantum computing computational platforms. The variational quantum eigensolver algorithm aims to prepare the ground state of a Hamiltonian exploiting para ...

Amer Physical Soc2023

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Mehmet Fatih Sahin

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

Results on Sparse Integer Programming and Geometric Independent Sets

Jana Tabea Cslovjecsek

An integer linear program is a problem of the form max{c^T x : Ax=b, x >= 0, x integer}, where A is in Z^(n x m), b in Z^m, and c in Z^n. Solving an integer linear program is NP-hard in general, but there are several assumptions for which it becomes fixed ...

EPFL2023

Asymptotics of feature learning in two-layer networks after one gradient-step

Lenka Zdeborová, Florent Gérard Krzakala, Bruno Loureiro, Hugo Chao Cui, Yatin Dandi, Luca Pesce, Yue Lu

In this manuscript, we investigate the problem of how two-layer neural networks learn features from data, and improve over the kernel regime, after being trained with a single gradient descent step. Leveraging the insight from (Ba et al., 2022), we model t ...

2024

Building Strongly-Consistent Systems Resilient to Failures, Partitions, and Slowdowns

Cristina Basescu

Distributed systems designers typically strive to improve performance and preserve availability despite failures or attacks; but, when strong consistency is also needed, they encounter fundamental limitations. The bottleneck is in replica coordination, whi ...

EPFL2023

On the Generalization of Stochastic Gradient Descent with Momentum

Volkan Cevher, Kimon Antonakopoulos

While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

Microtome Publishing2024

Fairness and Explainability in Clustering Problems

Xinrui Jia

In this thesis we present and analyze approximation algorithms for three different clustering problems. The formulations of these problems are motivated by fairness and explainability considerations, two issues that have recently received attention in the ...

EPFL2023