Learning with fenchel-young losses

M Blondel, AFT Martins, V Niculae - Journal of Machine Learning Research, 2020 - jmlr.org
Over the past decades, numerous loss functions have been been proposed for a variety of
supervised learning tasks, including regression, classification, ranking, and more generally …

Loss factorization, weakly supervised learning and label noise robustness

G Patrini, F Nielsen, R Nock… - … conference on machine …, 2016 - proceedings.mlr.press
We prove that the empirical risk of most well-known loss functions factors into a linear term
aggregating all labels with a term that is label free, and can further be expressed by sums of …

RBoost: Label noise-robust boosting algorithm based on a nonconvex loss function and the numerically stable base learners

Q Miao, Y Cao, G Xia, M Gong, J Liu… - IEEE transactions on …, 2015 - ieeexplore.ieee.org
AdaBoost has attracted much attention in the machine learning community because of its
excellent performance in combining weak classifiers into strong classifiers. However …

Learning classifiers with fenchel-young losses: Generalized entropies, margins, and algorithms

M Blondel, A Martins, V Niculae - The 22nd International …, 2019 - proceedings.mlr.press
Abstract This paper studies Fenchel-Young losses, a generic way to construct convex loss
functions from a regularization function. We analyze their properties in depth, showing that …

Two-temperature logistic regression based on the tsallis divergence

E Amid, MK Warmuth… - The 22nd International …, 2019 - proceedings.mlr.press
We develop a variant of multiclass logistic regression that is significantly more robust to
noise. The algorithm has one weight vector per class and the surrogate loss is a function of …

Bias-variance decompositions for margin losses

D Wood, T Mu, G Brown - International Conference on …, 2022 - proceedings.mlr.press
We introduce a novel bias-variance decomposition for a range of strictly convex margin
losses, including the logistic loss (minimized by the classic LogitBoost algorithm) as well as …

Bayesian Inference

E Souza de Cursi - Uncertainty Quantification with R: Bayesian Methods, 2024 - Springer
This chapter presents the Bayesian approach for practical tasks, such as estimation,
hypothesis testing, model or variable selection, and regression. The choice of priors is …

Uncertainty Quantification with R

ES de Cursi - International Series in Operations Research and …, 2024 - Springer
This book is an independent companion volume to Uncertainty Quantification with R,
complementing certain of its topics and taking up others from a different angle–the Bayesian …

Sequential Bayesian Estimation

E Souza de Cursi - Uncertainty Quantification with R: Bayesian Methods, 2024 - Springer
Abstract This chapter presents Monte-Carlo Markov Chain methods and connected topics,
namely Importance Sampling, Metropolis-Hastings Algorithm, Kalman Filtering, Particle …

A motion classification model with improved robustness through deformation code integration

L Xia, J Lv, D Liu - Neural Computing and Applications, 2019 - Springer
During data acquisition, samples in a time series may contain noise, such as inconsistent
data ranges, inconsistent data, and incomplete data. Therefore, the classification model …