Concept

Bias

Related publications (171)

Towards Understanding Sharpness-Aware Minimization

Nicolas Henri Bernard Flammarion, Maksym Andriushchenko

Sharpness-Aware Minimization (SAM) is a recent training method that relies on worst-case weight perturbations which significantly improves generalization in various settings. We argue that the existing justifications for the success of SAM which are based ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Dynamics of the most common pathogenic mtDNA variant m.3243A > G demonstrate frequency-dependency in blood and positive selection in the germline

Konstantin Popadin

The A-to-G point mutation at position 3243 in the human mitochondrial genome (m.3243A > G) is the most common pathogenic mtDNA variant responsible for disease in humans. It is widely accepted that m.3243A > G levels decrease in blood with age, and an age c ...

OXFORD UNIV PRESS2022

Human- and machine-centred designs of molecules and materials for sustainability and decarbonization

Sokseiha Muy, Tian Xie

Data-driven approaches based on high-throughput capabilities and machine learning hold promise in revolutionizing human-centred materials discovery for sustainability and decarbonization. This Review examines the strengths and limitations of different trad ...

NATURE PORTFOLIO2022

Validation of spent nuclear fuel decay heat calculations using Polaris, ORIGEN and CASMO5

Andreas Pautz, Dimitri Rochman, Stefano Caruso

Decay heat calculations of spent nuclear fuel (SNF) using Polaris and ORIGEN codes in the SCALE code sys-tem, and CASMO5 code, are validated using measurements from the Clab and GE-Morris facilities. Multiple hypothesis testing, relying on permutations and ...

PERGAMON-ELSEVIER SCIENCE LTD2022

A Structured Dictionary Perspective on Implicit Neural Representations

Pascal Frossard, Guillermo Ortiz Jimenez, Gizem Yüce, Beril Besbinar

Implicit neural representations (INRs) have recently emerged as a promising alternative to classical discretized representations of signals. Nevertheless, despite their practical success, we still do not understand how INRs represent signals. We propose a ...

IEEE COMPUTER SOC2022

Countering Bias in Personalized Rankings From Data Engineering to Algorithm Development

Mirko Marras

This tutorial presents recent advances on the assessment and mitigation of data and algorithmic bias in personalized rankings. We first introduce fundamental concepts and definitions associated with bias issues, covering the state of the art and describing ...

IEEE COMPUTER SOC2021

How to detect and reduce potential sources of biases in studies of SARS-CoV-2 and COVID-19

Mats Julius Stensrud

In response to the coronavirus disease (COVID-19) pandemic, public health scientists have produced a large and rapidly expanding body of literature that aims to answer critical questions, such as the proportion of the population in a geographic area that h ...

2021

Towards a pragmatist dealing with algorithmic bias in medical machine learning

Machine Learning (ML) is on the rise in medicine, promising improved diagnostic, therapeutic and prognostic clinical tools. While these technological innovations are bound to transform health care, they also bring new ethical concerns to the forefront. One ...

2021

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

Nicolas Henri Bernard Flammarion, Scott William Pesme, Loucas Pillaud-Vivien

Understanding the implicit bias of training algorithms is of crucial importance in order to explain the success of overparametrised neural networks. In this paper, we study the dynamics of stochastic gradient descent over diagonal linear networks through i ...

2021

Advances in Bias-aware Recommendation on the Web

Mirko Marras

The goal of this tutorial is to provide the WSDM community with recent advances on the assessment and mitigation of data and algorithmic bias in recommender systems. We first introduce conceptual foundations, by presenting the state of the art and describi ...

ASSOC COMPUTING MACHINERY2021

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