Marco Loog

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Delft University of Technology

Assistant Professor in the Pattern Recognition Laboratory at Delft University of Technology and Associate Editor of Pattern Recognition and Honorary Full Professor in Pattern Recognition at the University of Copenhagen

A Brief Prehistory of Double Descent

In their thought-provoking paper [1], Belkin et al. illustrate and discu...
04/07/2020 ∙ by Marco Loog, et al. ∙ 0 ∙ share

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Making Learners (More) Monotone

Learning performance can show non-monotonic behavior. That is, more data...
11/25/2019 ∙ by Tom J. Viering, et al. ∙ 0 ∙ share

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Consistency and Finite Sample Behavior of Binary Class Probability Estimation

In this work we investigate to which extent one can recover class probab...
08/30/2019 ∙ by Alexander Mey, et al. ∙ 6 ∙ share

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Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results

Semi-supervised learning is a setting in which one has labeled and unlab...
08/26/2019 ∙ by Alexander Mey, et al. ∙ 0 ∙ share

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How to Manipulate CNNs to Make Them Lie: the GradCAM Case

Recently many methods have been introduced to explain CNN decisions. How...
07/25/2019 ∙ by Tom Viering, et al. ∙ 0 ∙ share

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Minimizers of the Empirical Risk and Risk Monotonicity

Plotting a learner's average performance against the number of training ...
07/11/2019 ∙ by Marco Loog, et al. ∙ 0 ∙ share

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A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization

Manifold regularization is a commonly used technique in semi-supervised ...
06/14/2019 ∙ by Alexander Mey, et al. ∙ 0 ∙ share

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Semi-Supervised Learning, Causality and the Conditional Cluster Assumption

While the success of semi-supervised learning (SSL) is still not fully u...
05/28/2019 ∙ by Julius von Kugelgen, et al. ∙ 7 ∙ share

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A review of single-source unsupervised domain adaptation

Domain adaptation has become a prominent problem setting in machine lear...
01/16/2019 ∙ by Wouter M. Kouw, et al. ∙ 0 ∙ share

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Learning an MR acquisition-invariant representation using Siamese neural networks

Generalization of voxelwise classifiers is hampered by differences betwe...
10/17/2018 ∙ by Wouter M. Kouw, et al. ∙ 0 ∙ share

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Distance Based Source Domain Selection for Sentiment Classification

Automated sentiment classification (SC) on short text fragments has rece...
08/28/2018 ∙ by Lex Razoux Schultz, et al. ∙ 0 ∙ share

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Semi-Generative Modelling: Domain Adaptation with Cause and Effect Features

This paper presents a novel, causally-inspired approach to domain adapta...
07/20/2018 ∙ by Julius von Kugelgen, et al. ∙ 0 ∙ share

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Target Contrastive Pessimistic Discriminant Analysis

Domain-adaptive classifiers learn from a source domain and aim to genera...
06/21/2018 ∙ by Wouter M. Kouw, et al. ∙ 0 ∙ share

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Single Shot Active Learning using Pseudo Annotators

Standard myopic active learning assumes that human annotations are alway...
05/17/2018 ∙ by Yazhou Yang, et al. ∙ 0 ∙ share

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Effects of sampling skewness of the importance-weighted risk estimator on model selection

Importance-weighting is a popular and well-researched technique for deal...
04/19/2018 ∙ by Wouter M. Kouw, et al. ∙ 0 ∙ share

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Supervised Classification: Quite a Brief Overview

The original problem of supervised classification considers the task of ...
10/25/2017 ∙ by Marco Loog, et al. ∙ 0 ∙ share

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On reducing sampling variance in covariate shift using control variates

Covariate shift classification problems can in principle be tackled by i...
10/17/2017 ∙ by Wouter Kouw, et al. ∙ 0 ∙ share

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MR Acquisition-Invariant Representation Learning

Voxelwise classification is a popular and effective method for tissue qu...
09/22/2017 ∙ by Wouter M. Kouw, et al. ∙ 0 ∙ share

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Object-Extent Pooling for Weakly Supervised Single-Shot Localization

In the face of scarcity in detailed training annotations, the ability to...
07/19/2017 ∙ by Amogh Gudi, et al. ∙ 0 ∙ share

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On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL

In various approaches to learning, notably in domain adaptation, active ...
07/13/2017 ∙ by Marco Loog, et al. ∙ 0 ∙ share

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Scale-Regularized Filter Learning

We start out by demonstrating that an elementary learning task, correspo...
07/10/2017 ∙ by Marco Loog, et al. ∙ 0 ∙ share

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Target contrastive pessimistic risk for robust domain adaptation

In domain adaptation, classifiers with information from a source domain ...
06/25/2017 ∙ by Wouter M. Kouw, et al. ∙ 0 ∙ share

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A Variance Maximization Criterion for Active Learning

Active learning aims to train a classifier as fast as possible with as f...
06/23/2017 ∙ by Yazhou Yang, et al. ∙ 0 ∙ share

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Nuclear Discrepancy for Active Learning

Active learning algorithms propose which unlabeled objects should be que...
06/08/2017 ∙ by Tom J. Viering, et al. ∙ 0 ∙ share

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Label Stability in Multiple Instance Learning

We address the problem of instance label stability in multiple instance ...
03/15/2017 ∙ by Veronika Cheplygina, et al. ∙ 0 ∙ share

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Classification of COPD with Multiple Instance Learning

Chronic obstructive pulmonary disease (COPD) is a lung disease where ear...
03/15/2017 ∙ by Veronika Cheplygina, et al. ∙ 0 ∙ share

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Active Learning Using Uncertainty Information

Many active learning methods belong to the retraining-based approaches, ...
02/27/2017 ∙ by Yazhou Yang, et al. ∙ 0 ∙ share

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The Pessimistic Limits of Margin-based Losses in Semi-supervised Learning

We show that for linear classifiers defined by convex margin-based surro...
12/28/2016 ∙ by Jesse H. Krijthe, et al. ∙ 0 ∙ share

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Reproducible Pattern Recognition Research: The Case of Optimistic SSL

In this paper, we discuss the approaches we took and trade-offs involved...
12/27/2016 ∙ by Jesse H. Krijthe, et al. ∙ 0 ∙ share

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The Peaking Phenomenon in Semi-supervised Learning

For the supervised least squares classifier, when the number of training...
10/17/2016 ∙ by Jesse H. Krijthe, et al. ∙ 0 ∙ share

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Optimistic Semi-supervised Least Squares Classification

The goal of semi-supervised learning is to improve supervised classifier...
10/12/2016 ∙ by Jesse H. Krijthe, et al. ∙ 0 ∙ share

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On Regularization Parameter Estimation under Covariate Shift

This paper identifies a problem with the usual procedure for L2-regulari...
07/31/2016 ∙ by Wouter M. Kouw, et al. ∙ 0 ∙ share

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Template Matching via Densities on the Roto-Translation Group

We propose a template matching method for the detection of 2D image obje...
03/10/2016 ∙ by Erik J. Bekkers, et al. ∙ 0 ∙ share

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Projected Estimators for Robust Semi-supervised Classification

For semi-supervised techniques to be applied safely in practice we at le...
02/25/2016 ∙ by Jesse H. Krijthe, et al. ∙ 0 ∙ share

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Robust Semi-supervised Least Squares Classification by Implicit Constraints

We introduce the implicitly constrained least squares (ICLS) classifier,...
12/27/2015 ∙ by Jesse H. Krijthe, et al. ∙ 0 ∙ share

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Feature-Level Domain Adaptation

Domain adaptation is the supervised learning setting in which the traini...
12/15/2015 ∙ by Wouter M. Kouw, et al. ∙ 0 ∙ share

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Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification

Improvement guarantees for semi-supervised classifiers can currently onl...
03/01/2015 ∙ by Marco Loog, et al. ∙ 0 ∙ share

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Implicitly Constrained Semi-Supervised Linear Discriminant Analysis

Semi-supervised learning is an important and active topic of research in...
11/17/2014 ∙ by Jesse H. Krijthe, et al. ∙ 0 ∙ share

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On Classification with Bags, Groups and Sets

Many classification problems can be difficult to formulate directly in t...
06/02/2014 ∙ by Veronika Cheplygina, et al. ∙ 0 ∙ share

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Quantile Representation for Indirect Immunofluorescence Image Classification

In the diagnosis of autoimmune diseases, an important task is to classif...
02/06/2014 ∙ by David M. J. Tax, et al. ∙ 0 ∙ share

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Dissimilarity-based Ensembles for Multiple Instance Learning

In multiple instance learning, objects are sets (bags) of feature vector...
02/06/2014 ∙ by Veronika Cheplygina, et al. ∙ 0 ∙ share

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Multiple Instance Learning with Bag Dissimilarities

Multiple instance learning (MIL) is concerned with learning from sets (b...
09/22/2013 ∙ by Veronika Cheplygina, et al. ∙ 0 ∙ share

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Marco Loog

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