Publications associées à Geometric median

Publications associées (26)

Spectral Estimators for High-Dimensional Matrix Inference

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

EPFL2024

On the Strategyproofness of the Geometric Median

Rachid Guerraoui, El Mahdi El Mhamdi, Le Nguyen Hoang, Sadegh Farhadkhani

The geometric median, an instrumental component of the secure machine learning toolbox, is known to be effective when robustly aggregating models (or gradients), gathered from potentially malicious (or strategic) users. What is less known is the extent to ...

2023

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Ntorina Thanou, Nicola Parolini, Benjamin Bejar Haro, Daniel Rodrigo Lopez, Ekaterina Krymova, Andrea Kraus, David Kraus

Background:Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive p ...

eLIFE SCIENCES PUBL LTD2023

Assessing normalization methods in mass spectrometry-based proteome profiling of clinical samples

Loïc Dayon

Background: Large-scale proteomic studies have to deal with unwanted variability, especially when samples originate from different centers and multiple analytical batches are needed. Such variability is typically added throughout all the steps of a clinica ...

ELSEVIER SCI LTD2022

Byzantine Fault-Tolerant Distributed Machine Learning with Norm-Based Comparative Gradient Elimination

Nirupam Gupta, Shuo Liu

This paper considers the Byzantine fault-tolerance problem in distributed stochastic gradient descent (D-SGD) method - a popular algorithm for distributed multi-agent machine learning. In this problem, each agent samples data points independently from a ce ...

IEEE COMPUTER SOC2021

Gamma Knife radiosurgery as salvage therapy for gangliogliomas after initial microsurgical resection

Constantin Tuleasca

Introduction: Gangliogliomas (GG) are considered WHO grade I rare tumors. While they commonly manifest as temporal lobe epilepsy, they can be located anywhere in the brain. Primary treatment is complete microsurgical resection. Remnant or recurrent GG can ...

2021

Nearly-Tight and Oblivious Algorithms for Explainable Clustering

Ola Nils Anders Svensson, Adam Teodor Polak, Buddhima Ruwanmini Gamlath Gamlath Ralalage, Xinrui Jia

We study the problem of explainable clustering in the setting first formalized by Dasgupta, Frost, Moshkovitz, and Rashtchian (ICML 2020). A k-clustering is said to be explainable if it is given by a decision tree where each internal node splits data point ...

2021

Better Guarantees for k-Means and Euclidean k-Median by Primal-Dual Algorithms

Ola Nils Anders Svensson, Ashkan Norouzi Fard, Justin Dean Ward

Clustering is a classic topic in optimization with k-means being one of the most fundamental such problems. In the absence of any restrictions on the input, the best-known algorithm for k-means in Euclidean space with a provable guarantee is a simple local ...

SIAM PUBLICATIONS2020

Establishment of a Therapeutic Ratio for Gamma Knife Radiosurgery of Trigeminal Neuralgia: The Critical Importance of Biologically Effective Dose Versus Physical Dose

Constantin Tuleasca, Adrien Jean Régis Maurice Hilaire

OBJECTIVE: How variations of treatment time affect the safety and efficacy of Gamma Knife (GK) radiosurgery is a matter of considerable debate. With the relative simplicity of treatment planning for trigeminal neuralgia (TN), this question has been address ...

2020

Automated Detection and Segmentation of Multiple Sclerosis Lesions Using Ultra-High-Field MP2RAGE

Tobias Kober, Meritxell Bach Cuadra, Cristina Granziera, Kieran O'Brien

Objectives The aim of this study was to develop a new automated segmentation method of white matter (WM) and cortical multiple sclerosis (MS) lesions visible on magnetization-prepared 2 inversion-contrast rapid gradient echo (MP2RAGE) images acquired at 7 ...

LIPPINCOTT WILLIAMS & WILKINS2019

A Self-Learning Methodology for Epileptic Seizure Detection with Minimally Supervised Edge Labeling

David Atienza Alonso, Amir Aminifar, Damián Pascual Ortiz

Epilepsy is one of the most common neurological disorders and affects over 65 million people worldwide. Despite the continuing advances in anti-epileptic treatments, one third of the epilepsy patients live with drug resistant seizures. Besides, the mortali ...

2019

How much can we save? Impact of different emission scenarios on future snow cover in the Alps

Michael Lehning, Sebastian Schlögl

This study focuses on an assessment of the future snow depth for two larger Alpine catchments. Automatic weather station data from two diverse regions in the Swiss Alps have been used as input for the Alpine3D surface process model to compute the snow cove ...

Copernicus GmbH2017

APPROXIMATING k-MEDIAN VIA PSEUDO-APPROXIMATION

Ola Nils Anders Svensson

We present a novel approximation algorithm for k-median that achieves an approximation guarantee of 1 + root 3 + epsilon, improving upon the decade-old ratio of 3 + epsilon. Our improved approximation ratio is achieved by exploiting the power of pseudo-app ...

Siam Publications2016

Evaluation of the performance characteristics of the European Lightning Detection Network EUCLID in the Alps region for upward negative flashes using direct measurements at the instrumented Säntis Tower

Farhad Rachidi-Haeri, Marcos Rubinstein, Mario Paolone, Davide Pavanello, Mohammad Azadifar, Carlos Alberto Romero Romero

In this paper, we present a performance analysis of the European Cooperation for Lightning Detection (EUCLID) lightning detection network using data obtained on lightning currents measured at the Santis Tower (located in northeastern of Switzerland) from J ...

Amer Geophysical Union2016

How to learn a graph from smooth signals

Vasilis Kalofolias

We propose a framework that learns the graph structure underlying a set of smooth signals. Given a real m by n matrix X whose rows reside on the vertices of an unknown graph, we learn the edge weights w under the smoothness assumption that trace(X^TLX) is ...

2016

Models and Algorithms for Comparative Genomics

Mingfu Shao

The deluge of sequenced whole-genome data has motivated the study of comparative genomics, which provides global views on genome evolution, and also offers practical solutions in deciphering the functional roles of components of genomes. A fundamental comp ...

EPFL2015

Progressive Geometric Algorithms

Timur Bagautdinov

Progressive algorithms are algorithms that, on the way to computing a complete solution to the problem at hand, output intermediate solutions that approximate the complete solution increasingly well. We present a framework for analyzing such algorithms, an ...

Carleton Univ, Dept Mathematics & Statistics2015

On the DCJ Median Problem

Bernard Moret, Mingfu Shao

As many whole genomes are sequenced, comparative genomics is moving from pairwise comparisons to multiway comparisons framed within a phylogenetic tree. A central problem in this process is the inference of data for internal nodes of the tree from data giv ...

Springer-Verlag Berlin2014

Trigeminal Neuralgia Related to Megadolichobasilar Artery Compression: A Prospective Series of Twenty-Nine Patients Treated with Gamma Knife Surgery, with More Than One Year of Follow-Up

Constantin Tuleasca

Background: Trigeminal neuralgia (TN) secondary to megadolichobasilar artery (MBA) compression is considerably difficult to manage surgically. Objective: This study aims to evaluate the safety/efficacy of Gamma Knife surgery (GKS) in this special group of ...

Karger2014

On the Development of a Dynamic Non-linear Closure for Large-Eddy Simulation of the Atmospheric Boundary Layer

Fernando Porté Agel, Hao Lu

A dynamic procedure is developed to compute the model coefficients in the recently introduced modulated gradient models for both momentum and scalar fluxes. The magnitudes of the subgrid-scale (SGS) stress and the SGS flux are estimated using the local equ ...

Springer2014