Publications related to Byzantine tolerant gradient descent for distributed machine learning with adversaries

Efficient Continual Finite-Sum Minimization

Volkan Cevher, Efstratios Panteleimon Skoulakis, Leello Tadesse Dadi

Given a sequence of functions

f_1,\ldots,f_n

with

f_i:\mathcal{D}\mapsto \mathbb{R}

, finite-sum minimization seeks a point

{x}^\star \in \mathcal{D}

minimizing

\sum_{j=1}^nf_j(x)/n

. In this work, we propose a key twist into the finite-sum minimizat ...

2024

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Pakshal Narendra Bohra

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

EPFL2024

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

David Atienza Alonso, Marina Zapater Sancho, Luis Maria Costero Valero, Darong Huang, Ali Pahlevan

Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limi ...

2024

Meta-learning to address diverse Earth observation problems across resolutions

Devis Tuia, Benjamin Alexander Kellenberger, Marc Conrad Russwurm

Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth ...

London2024

Partial discharge localization in power transformer tanks using machine learning methods

Marcos Rubinstein, Hamidreza Karami

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

2024

Machine learning-aided generative molecular design

Philippe Schwaller, Jeff Guo

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Nature Portfolio2024

Scalable constrained optimization

Maria-Luiza Vladarean

Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

EPFL2024

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Paul Scharnhorst

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

EPFL2024

Can Gas Consumption Data Improve the Performance of Electricity Theft Detection?

Wenlong Liao, Zhe Yang

Machine learning techniques have been extensively developed in the field of electricity theft detection. However, almost all typical models primarily rely on electricity consumption data to identify fraudulent users, often neglecting other pertinent househ ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Lakmal Buddika Meegahapola

A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

EPFL2024

Byzantine tolerant gradient descent for distributed machine learning with adversaries

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Efficient Continual Finite-Sum Minimization

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

Meta-learning to address diverse Earth observation problems across resolutions

Partial discharge localization in power transformer tanks using machine learning methods

Machine learning-aided generative molecular design

Scalable constrained optimization

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Can Gas Consumption Data Improve the Performance of Electricity Theft Detection?

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Efficient Continual Finite-Sum Minimization

Meta-learning to address diverse Earth observation problems across resolutions

Partial discharge localization in power transformer tanks using machine learning methods

Scalable constrained optimization

Can Gas Consumption Data Improve the Performance of Electricity Theft Detection?

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

Machine learning-aided generative molecular design

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life