## Reflections on reciprocity in research

Published in Machine Learning

Published in Machine Learning

Data preprocessing is an important component of machine learning pipelines, which requires ample time and resources. An integral part of preprocessing is data transformation into the format required by a given learning algorithm. This paper outlines some of the modern data processing techniques used in relational learning that enable data fusion fr...

Published in Machine Learning

We present an extension of Chervonenkis and Vapnik’s classical empirical risk minimization (ERM) where the empirical risk is replaced by a median-of-means (MOM) estimator of the risk. The resulting new estimators are called MOM minimizers. While ERM is sensitive to corruption of the dataset for many classical loss functions used in classification, ...

Published in Machine Learning

We consider the problem of learning a binary classifier from only positive and unlabeled observations (called PU learning). Recent studies in PU learning have shown superior performance theoretically and empirically. However, most existing algorithms may not be suitable for large-scale datasets because they face repeated computations of a large Gra...

Published in Machine Learning

We develop the setting of sequential prediction based on shifting experts and on a “smooth” version of the method of specialized experts. To aggregate expert predictions, we use the AdaHedge algorithm, which is a version of the Hedge algorithm with adaptive learning rate, and extend it by the meta-algorithm Fixed Share. Due to this, we combine the ...

Published in Machine Learning

A methodology to estimate from samples the probability density of a random variable x conditional to the values of a set of covariates {zl}\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt}...

Published in Machine Learning

Learning from large-scale and high-dimensional data still remains a computationally challenging problem, though it has received increasing interest recently. To address this issue, randomized reduction methods have been developed by either reducing the dimensionality or reducing the number of training instances to obtain a small sketch of the origi...

Published in Machine Learning

Gene network reconstruction is a bioinformatics task that aims at modelling the complex regulatory activities that may occur among genes. This task is typically solved by means of link prediction methods that analyze gene expression data. However, the reconstructed networks often suffer from a high amount of false positive edges, which are actually...

Published in Machine Learning

Testing for equality of two high-dimensional distributions is a challenging problem, and this becomes even more challenging when the sample size is small. Over the last few decades, several graph-based two-sample tests have been proposed in the literature, which can be used for data of arbitrary dimensions. Most of these test statistics are compute...

Published in Machine Learning

In this paper we seek to identify data instances with a low value of some objective (or cost) function. Normally posed as optimisation problems, our interest is in problems that have the following characteristics: (a) optimal, or even near-optimal solutions are very rare; (b) it is expensive to obtain the value of the objective function for large n...