b'Marco Loog'

research

∙ 03/23/2022

Enhancing Classifier Conservativeness and Robustness by Polynomiality

We illustrate the detrimental effect, such as overconfident decisions, t...

Ziqi Wang, et al. ∙

research

∙ 07/28/2021

Social Processes: Self-Supervised Forecasting of Nonverbal Cues in Social Conversations

The default paradigm for the forecasting of human behavior in social con...

Chirag Raman, et al. ∙

research

∙ 06/07/2021

Resolution learning in deep convolutional networks using scale-space theory

Resolution in deep convolutional neural networks (CNNs) is typically bou...

Silvia L. Pintea, et al. ∙

research

∙ 03/19/2021

The Shape of Learning Curves: a Review

Learning curves provide insight into the dependence of a learner's gener...

Tom Viering, et al. ∙

research

∙ 02/03/2021

Nearest Neighbor-based Importance Weighting

Importance weighting is widely applicable in machine learning in general...

Marco Loog, et al. ∙

research

∙ 04/07/2020

A Brief Prehistory of Double Descent

In their thought-provoking paper [1], Belkin et al. illustrate and discu...

Marco Loog, et al. ∙

research

∙ 11/25/2019

Making Learners (More) Monotone

Learning performance can show non-monotonic behavior. That is, more data...

Tom J. Viering, et al. ∙

research

∙ 08/30/2019

Consistency and Finite Sample Behavior of Binary Class Probability Estimation

In this work we investigate to which extent one can recover class probab...

Alexander Mey, et al. ∙

research

∙ 08/26/2019

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

Semi-supervised learning is a setting in which one has labeled and unlab...

Alexander Mey, et al. ∙

research

∙ 07/25/2019

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

Recently many methods have been introduced to explain CNN decisions. How...

Tom Viering, et al. ∙

research

∙ 07/11/2019

Minimizers of the Empirical Risk and Risk Monotonicity

Plotting a learner's average performance against the number of training ...

Marco Loog, et al. ∙

research

∙ 06/14/2019

A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization

Manifold regularization is a commonly used technique in semi-supervised ...

Alexander Mey, et al. ∙

research

∙ 05/28/2019

Semi-Supervised Learning, Causality and the Conditional Cluster Assumption

While the success of semi-supervised learning (SSL) is still not fully u...

Julius von Kügelgen, et al. ∙

research

∙ 01/16/2019

A review of single-source unsupervised domain adaptation

Domain adaptation has become a prominent problem setting in machine lear...

Wouter M. Kouw, et al. ∙

research

∙ 10/17/2018

Learning an MR acquisition-invariant representation using Siamese neural networks

Generalization of voxelwise classifiers is hampered by differences betwe...

Wouter M. Kouw, et al. ∙

research

∙ 08/28/2018

Distance Based Source Domain Selection for Sentiment Classification

Automated sentiment classification (SC) on short text fragments has rece...

Lex Razoux Schultz, et al. ∙

research

∙ 07/20/2018

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

This paper presents a novel, causally-inspired approach to domain adapta...

Julius von Kügelgen, et al. ∙

research

∙ 06/21/2018

Target Contrastive Pessimistic Discriminant Analysis

Domain-adaptive classifiers learn from a source domain and aim to genera...

Wouter M. Kouw, et al. ∙

research

∙ 05/17/2018

Single Shot Active Learning using Pseudo Annotators

Standard myopic active learning assumes that human annotations are alway...

Yazhou Yang, et al. ∙

research

∙ 04/19/2018

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

Importance-weighting is a popular and well-researched technique for deal...

Wouter M. Kouw, et al. ∙

research

∙ 10/25/2017

Supervised Classification: Quite a Brief Overview

The original problem of supervised classification considers the task of ...

Marco Loog, et al. ∙

research

∙ 10/17/2017

On reducing sampling variance in covariate shift using control variates

Covariate shift classification problems can in principle be tackled by i...

Wouter Kouw, et al. ∙

research

∙ 09/22/2017

MR Acquisition-Invariant Representation Learning

Voxelwise classification is a popular and effective method for tissue qu...

Wouter M. Kouw, et al. ∙

research

∙ 07/19/2017

Object-Extent Pooling for Weakly Supervised Single-Shot Localization

In the face of scarcity in detailed training annotations, the ability to...

Amogh Gudi, et al. ∙

research

∙ 07/13/2017

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

In various approaches to learning, notably in domain adaptation, active ...

Marco Loog, et al. ∙

research

∙ 07/10/2017

Scale-Regularized Filter Learning

We start out by demonstrating that an elementary learning task, correspo...

Marco Loog, et al. ∙

research

∙ 06/25/2017

Target contrastive pessimistic risk for robust domain adaptation

In domain adaptation, classifiers with information from a source domain ...

Wouter M. Kouw, et al. ∙

research

∙ 06/23/2017

A Variance Maximization Criterion for Active Learning

Active learning aims to train a classifier as fast as possible with as f...

Yazhou Yang, et al. ∙

research

∙ 06/08/2017

Nuclear Discrepancy for Active Learning

Active learning algorithms propose which unlabeled objects should be que...

Tom J. Viering, et al. ∙

research

∙ 03/15/2017

Label Stability in Multiple Instance Learning

We address the problem of instance label stability in multiple instance ...

Veronika Cheplygina, et al. ∙

research

∙ 03/15/2017

Classification of COPD with Multiple Instance Learning

Chronic obstructive pulmonary disease (COPD) is a lung disease where ear...

Veronika Cheplygina, et al. ∙

research

∙ 02/27/2017

Active Learning Using Uncertainty Information

Many active learning methods belong to the retraining-based approaches, ...

Yazhou Yang, et al. ∙

research

∙ 12/28/2016

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

We show that for linear classifiers defined by convex margin-based surro...

Jesse H. Krijthe, et al. ∙

research

∙ 12/27/2016

Reproducible Pattern Recognition Research: The Case of Optimistic SSL

In this paper, we discuss the approaches we took and trade-offs involved...

Jesse H. Krijthe, et al. ∙

research

∙ 10/17/2016

The Peaking Phenomenon in Semi-supervised Learning

For the supervised least squares classifier, when the number of training...

Jesse H. Krijthe, et al. ∙

research

∙ 10/12/2016

Optimistic Semi-supervised Least Squares Classification

The goal of semi-supervised learning is to improve supervised classifier...

Jesse H. Krijthe, et al. ∙

research

∙ 07/31/2016

On Regularization Parameter Estimation under Covariate Shift

This paper identifies a problem with the usual procedure for L2-regulari...

Wouter M. Kouw, et al. ∙

research

∙ 03/10/2016

Template Matching via Densities on the Roto-Translation Group

We propose a template matching method for the detection of 2D image obje...

Erik J. Bekkers, et al. ∙

research

∙ 02/25/2016

Projected Estimators for Robust Semi-supervised Classification

For semi-supervised techniques to be applied safely in practice we at le...

Jesse H. Krijthe, et al. ∙

research

∙ 12/27/2015

Robust Semi-supervised Least Squares Classification by Implicit Constraints

We introduce the implicitly constrained least squares (ICLS) classifier,...

Jesse H. Krijthe, et al. ∙

research

∙ 12/15/2015

Feature-Level Domain Adaptation

Domain adaptation is the supervised learning setting in which the traini...

Wouter M. Kouw, et al. ∙

research

∙ 03/01/2015

Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification

Improvement guarantees for semi-supervised classifiers can currently onl...

Marco Loog, et al. ∙

research

∙ 11/17/2014

Implicitly Constrained Semi-Supervised Linear Discriminant Analysis

Semi-supervised learning is an important and active topic of research in...

Jesse H. Krijthe, et al. ∙

research

∙ 06/02/2014

On Classification with Bags, Groups and Sets

Many classification problems can be difficult to formulate directly in t...

Veronika Cheplygina, et al. ∙

research

∙ 02/06/2014

Quantile Representation for Indirect Immunofluorescence Image Classification

In the diagnosis of autoimmune diseases, an important task is to classif...

David M. J. Tax, et al. ∙

research

∙ 02/06/2014

Dissimilarity-based Ensembles for Multiple Instance Learning

In multiple instance learning, objects are sets (bags) of feature vector...

Veronika Cheplygina, et al. ∙

research

∙ 09/22/2013

Multiple Instance Learning with Bag Dissimilarities

Multiple instance learning (MIL) is concerned with learning from sets (b...

Veronika Cheplygina, et al. ∙

Marco Loog

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