Publications associées à COLA: Decentralized Linear Learning

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Fast Automatic Smoothing for Generalized Additive Models

Anthony Christopher Davison, Yousra El Bachir

Generalized additive models (GAMs) are regression models wherein parameters of probability distributions depend on input variables through a sum of smooth functions, whose degrees of smoothness are selected by L-2 regularization. Such models have become th ...

MICROTOME PUBL2019

Crowdsourcing with Fairness, Diversity and Budget Constraints

Boi Faltings, Naman Goel

Recent studies have shown that the labels collected from crowdworkers can be discriminatory with respect to sensitive attributes such as gender and race. This raises questions about the suitability of using crowdsourced data for further use, such as for tr ...

ASSOC COMPUTING MACHINERY2019

Machine Learning at the Atomic Scale

Michele Ceriotti, Félix Benedito Clément Musil

Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace as a tool to predict energy, forces and properties of molecules and condense ...

SWISS CHEMICAL SOC2019

Machine Learning at the Atomic Scale

Michele Ceriotti, Félix Benedito Clément Musil

2019

Multi-task additive models with shared transfer functions based on dictionary learning

Pascal Frossard, Alhussein Fawzi

Additive models form a widely popular class of regression models which represent the relation between covariates and response variables as the sum of low-dimensional transfer functions. Besides flexibility and accuracy, a key benefit of these models is the ...

Institute of Electrical and Electronics Engineers2017

Learning Active Learning from Data

Pascal Fua, Raphael Sznitman, Ksenia Konyushkova

In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to train a regressor that predicts the expected error reduction for a candidate sample in a particular learning state. By formulating the query selection proced ...

2017

COMPARISON OF LUMINANCE BASED METRICS IN DIFFERENT LIGHTING CONDITIONS

Marilyne Andersen, Jan Wienold, Mandana Sarey Khanie

In this study, we evaluate established and newly developed metrics for predicting glare using data from three different research studies. The evaluation covers two different targets: 1. How well the user’s perception of glare magnitude correlates to the pr ...

CIE, Vienna2017

Machine Learning Techniques to address classification issues in Reverse Engineering

Jonathan Dekhtiar

This paper aims to provide a road map for future works related to reverse engineering field of expertise. Reverse Engineering, in a mechanical context, relates to any process working in a bottom-up fashion, namely that it goes from a lower level concept or ...

Springer-Verlag Berlin2017

Structured Dimensionality Reduction for Additive Model Regression

Pascal Frossard, Alhussein Fawzi

Additive models are regression methods which model the response variable as the sum of univariate transfer functions of the input variables. Key benefits of additive models are their accuracy and interpretability on many real-world tasks. Additive models a ...

Institute of Electrical and Electronics Engineers2016

COLA: Decentralized Linear Learning

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Chattez avec Graph Search

Fast Automatic Smoothing for Generalized Additive Models

Crowdsourcing with Fairness, Diversity and Budget Constraints

Machine Learning at the Atomic Scale

Machine Learning at the Atomic Scale

Data-driven reduced order modeling for time-dependent problems

Multi-task additive models with shared transfer functions based on dictionary learning

Learning Active Learning from Data

COMPARISON OF LUMINANCE BASED METRICS IN DIFFERENT LIGHTING CONDITIONS

Machine Learning Techniques to address classification issues in Reverse Engineering

Structured Dimensionality Reduction for Additive Model Regression

Machine Learning at the Atomic Scale

Fast Automatic Smoothing for Generalized Additive Models

Crowdsourcing with Fairness, Diversity and Budget Constraints

COMPARISON OF LUMINANCE BASED METRICS IN DIFFERENT LIGHTING CONDITIONS

Data-driven reduced order modeling for time-dependent problems

Structured Dimensionality Reduction for Additive Model Regression

Multi-task additive models with shared transfer functions based on dictionary learning

Learning Active Learning from Data

Machine Learning Techniques to address classification issues in Reverse Engineering

Machine Learning at the Atomic Scale