An investigation of cepstral parameterisations for large vocabulary speech recognition. - Reinhold Haeb-Umbach; Marco Loog (EUROSPEECH, 1999)

Multi-Class Linear Feature Extraction by Nonlinear PCA. - Robert P. W. Duin; Marco Loog; Reinhold Haeb-Umbach (ICPR, 2000)

Multi-class linear dimension reduction by generalized Fisher criteria. - Marco Loog; Reinhold Haeb-Umbach (INTERSPEECH, 2000)

On the Behavior of Spatial Critical Points under Gaussian Blurring. A Folklore Theorem and Scale-Space Constraints. - Marco Loog; Johannes Jisse Duistermaat; Luc Florack (Scale-Space, 2001)

Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria. - Marco Loog; Robert P. W. Duin; Reinhold Haeb-Umbach (IEEE Trans. Pattern Anal. Mach. Intell., 2001)

Supervised Segmentation by Iterated Contextual Pixel Classification. - Marco Loog; Bram van Ginneken (ICPR (2), 2002)

Non-iterative Heteroscedastic Linear Dimension Reduction for Two-Class Data. - Marco Loog; Robert P. W. Duin (SSPR/SPR, 2002)

Multi-scale Nodule Detection in Chest Radiographs. - Arnold M. R. Schilham; Bram van Ginneken; Marco Loog (MICCAI (1), 2003)

Model-based segmentation of abdominal aortic aneurysms in CTA images. - Marleen de Bruijne; Bram van Ginneken; Wiro J. Niessen; Marco Loog; Max A. Viergever (Image Processing, 2003)

Interactive shape models. - Bram van Ginneken; Marleen de Bruijne; Marco Loog; Max A. Viergever (Image Processing, 2003)

Segmenting the posterior ribs in chest radiographs by iterated contextual pixel classification. - Marco Loog; Bram van Ginneken; Max A. Viergever (Image Processing, 2003)

Gaussian Scale Space from Insufficient Image Information. - Marco Loog; Martin Lillholm; Mads Nielsen; Max A. Viergever (Scale-Space, 2003)

Support Blob Machines. The Sparsification of Linear Scale Space. - Marco Loog (ECCV (4), 2004)

Dimensionality Reduction by Canonical Contextual Correlation Projections. - Marco Loog; Bram van Ginneken; Robert P. W. Duin (ECCV (1), 2004)

Integrating Automatic and Interactive Brain Tumor Segmentation. - Erik Dam; Marco Loog; Marloes M. J. Letteboer (ICPR (3), 2004)

Pixel Position Regression - Application to Medical Image Segmentation. - Bram van Ginneken; Marco Loog (ICPR (3), 2004)

Static Posterior Probability Fusion for Signal Detection: Applications in the Detection of Interstitial Diseases in Chest Radiographs. - Marco Loog; Bram van Ginneken (ICPR (1), 2004)

Local Fisher Embedding. - Dick de Ridder; Marco Loog; Marcel J. T. Reinders (ICPR (2), 2004)

Comparative study of retinal vessel segmentation methods on a new publicly available database. - Meindert Niemeijer; Joes Staal; Bram van Ginneken; Marco Loog; Michael D. Abràmoff (Image Processing, 2004)

The MDF discrimination measure: Fisher in disguise. - Marco Loog; Robert P. W. Duin; Max A. Viergever (Neural Networks, 2004)

Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion. - Marco Loog; Robert P. W. Duin (IEEE Trans. Pattern Anal. Mach. Intell., 2004)

Supervised dimensionality reduction and contextual pattern recognition in medical image processing. - Marco Loog (2004)

Enhanced Direct Linear Discriminant Analysis for Feature Extraction on High Dimensional Data. - A. Kai Qin; S. Y. M. Shi; Ponnuthurai N. Suganthan; Marco Loog (AAAI, 2005)

Maximum Likely Scale Estimation. - Marco Loog; Kim Steenstrup Pedersen; Bo Markussen (DSSCV, 2005)

A Scale Invariant Covariance Structure on Jet Space. - Bo Markussen; Kim Steenstrup Pedersen; Marco Loog (DSSCV, 2005)

Dimensionality reduction of image features using the canonical contextual correlation projection. - Marco Loog; Bram van Ginneken; Robert P. W. Duin (Pattern Recognit., 2005)

Uncorrelated heteroscedastic LDA based on the weighted pairwise Chernoff criterion. - A. Kai Qin; Ponnuthurai N. Suganthan; Marco Loog (Pattern Recognit., 2005)

Bony Structure Suppression in Chest Radiographs. - Marco Loog; Bram van Ginneken (CVAMIA, 2006)

Conditional Linear Discriminant Analysis. - Marco Loog (ICPR (2), 2006)

Local Discriminant Analysis. - Marco Loog; Dick de Ridder (ICPR (3), 2006)

Efficient Feature Extraction Based on Regularized Uncorrelated Chernoff Discriminant Analysis. - A. Kai Qin; Ponnuthurai N. Suganthan; Marco Loog (ICPR (3), 2006)

Understanding Hessian-Based Density Scoring. - Jakob Raundahl; Marco Loog; Mads Nielsen (Digital Mammography / IWDM, 2006)

Mammographic density measured as changes in tissue structure caused by HRT. - Jakob Raundahl; Marco Loog; Mads Nielsen (Image Processing, 2006)

Generic Blind Source Separation Using Second-Order Local Statistics. - Marco Loog (SSPR/SPR, 2006)

Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. - Bram van Ginneken; Mikkel B. Stegmann; Marco Loog (Medical Image Anal., 2006)

Filter learning: Application to suppression of bony structures from chest radiographs. - Marco Loog; Bram van Ginneken; Arnold M. R. Schilham (Medical Image Anal., 2006)

A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database. - Arnold M. R. Schilham; Bram van Ginneken; Marco Loog (Medical Image Anal., 2006)

Generalized null space uncorrelated Fisher discriminant analysis for linear dimensionality reduction. - A. Kai Qin; Ponnuthurai N. Suganthan; Marco Loog (Pattern Recognit., 2006)

Recent submissions in linear dimensionality reduction and face recognition. - Robert P. W. Duin; Marco Loog; Tin Kam Ho (Pattern Recognit. Lett., 2006)

Segmentation of the posterior ribs in chest radiographs using iterated contextual pixel classification. - Marco Loog; Bram van Ginneken (IEEE Trans. Medical Imaging, 2006)

Localized Maximum Entropy Shape Modelling. - Marco Loog (IPMI, 2007)

A Family of Principal Component Analyses for Dealing with Outliers. - Juan Eugenio Iglesias; Marleen de Bruijne; Marco Loog; François Lauze; Mads Nielsen (MICCAI (2), 2007)

Quantifying Effect-Specific Mammographic Density. - Jakob Raundahl; Marco Loog; Paola Pettersen; Mads Nielsen (MICCAI (2), 2007)

Evaluation of four mammographic density measures on HRT data. - Jakob Raundahl; Marco Loog; Paola Pettersen; Mads Nielsen (Image Processing, 2007)

The Jet Metric. - Marco Loog (SSVM, 2007)

Blur Invariant Image Priors. - Marco Loog; François Lauze (SSVM, 2007)

Generic Maximum Likely Scale Selection. - Kim Steenstrup Pedersen; Marco Loog; Bo Markussen (SSVM, 2007)

A Complete Characterization of a Family of Solutions to a Generalized Fisher Criterion. - Marco Loog (J. Mach. Learn. Res., 2007)

Salient Point and Scale Detection by Minimum Likelihood. - Kim Steenstrup Pedersen; Marco Loog; Pieter van Dorst (Gaussian Processes in Practice, 2007)

On an alternative formulation of the Fisher criterion that overcomes the small sample problem. - Marco Loog (Pattern Recognit., 2007)

A variational method for automatic localization of the most pathological ROI in the knee cartilage. - Arish A. Qazi; Erik B. Dam; Marco Loog; Mads Nielsen; François Lauze; Claus Christiansen (Image Processing, 2008)

A note on an extreme case of the generalized optimal discriminant transformation. - Marco Loog; Xiaojun Wu 001; Jieping Lu; Jing-Yu Yang 001; Shitong Wang 001; Josef Kittler (Neurocomputing, 2008)

Second Order Structure of Scale-Space Measurements. - Bo Markussen; Kim Steenstrup Pedersen; Marco Loog (J. Math. Imaging Vis., 2008)

On Distributional Assumptions and Whitened Cosine Similarities. - Marco Loog (IEEE Trans. Pattern Anal. Mach. Intell., 2008)

Efficient Segmentation by Sparse Pixel Classification. - Erik B. Dam; Marco Loog (IEEE Trans. Medical Imaging, 2008)

Automated Effect-Specific Mammographic Pattern Measures. - Jakob Raundahl; Marco Loog; Paola Pettersen; László B. Tankó; Mads Nielsen (IEEE Trans. Medical Imaging, 2008)

Dense iterative contextual pixel classification using Kriging. - Melanie Ganz; Marco Loog; Sami S. Brandt; Mads Nielsen (CVPR Workshops, 2009)

Bicycle chain shape models. - Stefan Sommer; Aditya Tatu; Chen Chen; D. R. Jurgensen; Marleen de Bruijne; Marco Loog; Mads Nielsen; François Lauze (CVPR Workshops, 2009)

Discriminative Shape Alignment. - Marco Loog; Marleen de Bruijne (IPMI, 2009)

Quantitative Comparison of Spot Detection Methods in Live-Cell Fluorescence Microscopy Imaging. - Ihor Smal; Marco Loog; Wiro J. Niessen; Erik Meijering (ISBI, 2009)

Maximum Membership Scale Selection. - Marco Loog; Yan Li 009; David M. J. Tax (MCS, 2009)

Optimal Mean-Precision Classifier. - David M. J. Tax; Marco Loog; Robert P. W. Duin (MCS, 2009)

An experimental study on combining Euclidean distances. - Wan-Jui Lee; Robert P. W. Duin; Alessandro Ibba; Marco Loog (CIP, 2010)

Feature-Based Dissimilarity Space Classification. - Robert P. W. Duin; Marco Loog; Elzbieta Pekalska; David M. J. Tax (ICPR Contests, 2010)

Early diagnosis of dementia based on intersubject whole-brain dissimilarities. - Stefan Klein 001; Marco Loog; Fedde van der Lijn; Tom den Heijer; Alexander Hammers; Marleen de Bruijne; Aad van der Lugt; Robert P. W. Duin; Monique M. B. Breteler; Wiro J. Niessen (ISBI, 2010)

A Texton-Based Approach for the Classification of Lung Parenchyma in CT Images. - Mehrdad J. Gangeh; Lauge Sørensen; Saher B. Shaker; Mohamed S. Kamel; Marleen de Bruijne; Marco Loog (MICCAI (3), 2010)

Image Dissimilarity-Based Quantification of Lung Disease from CT. - Lauge Sørensen; Marco Loog; Pechin Lo; Haseem Ashraf; Asger Dirksen; Robert P. W. Duin; Marleen de Bruijne (MICCAI (1), 2010)

Constrained Parameter Estimation for Semi-supervised Learning: The Case of the Nearest Mean Classifier. - Marco Loog (ECML/PKDD (2), 2010)

Dissimilarity-Based Multiple Instance Learning. - Lauge Sørensen; Marco Loog; David M. J. Tax; Wan-Jui Lee; Marleen de Bruijne; Robert P. W. Duin (SSPR/SPR, 2010)

A framework for optimizing measurement weight maps to minimize the required sample size. - Arish A. Qazi; Dan R. Jørgensen; Martin Lillholm; Marco Loog; Mads Nielsen; Erik B. Dam (Medical Image Anal., 2010)

The Improbability of Harris Interest Points. - Marco Loog; François Lauze (IEEE Trans. Pattern Anal. Mach. Intell., 2010)

Using Multiscale Spectra in Regularizing Covariance Matrices for Hyperspectral Image Classification. - Are Charles Jensen; Marco Loog; Anne H. Schistad Solberg (IEEE Trans. Geosci. Remote. Sens., 2010)

Quantitative Comparison of Spot Detection Methods in Fluorescence Microscopy. - Ihor Smal; Marco Loog; Wiro J. Niessen; Erik Meijering (IEEE Trans. Medical Imaging, 2010)

Towards exaggerated image stereotypes. - Chen Chen; François Lauze; Christian Igel; Aasa Feragen; Marco Loog; Mads Nielsen (ACPR, 2011)

Information theoretic preattentive saliency: A closed-form solution. - Marco Loog (ICCV Workshops, 2011)

Generalized Augmentation of Multiple Kernels. - Wan-Jui Lee; Robert P. W. Duin; Marco Loog (MCS, 2011)

Semi-supervised Linear Discriminant Analysis Using Moment Constraints. - Marco Loog (PSL, 2011)

Supervised Scale-Invariant Segmentation (and Detection). - Yan Li 009; David M. J. Tax; Marco Loog (SSVM, 2011)

Forming Different-Complexity Covariance-Model Subspaces through Piecewise-Constant Spectra for Hyperspectral Image Classification. - Are Charles Jensen; Marco Loog (SCIA, 2011)

Bag Dissimilarities for Multiple Instance Learning. - David M. J. Tax; Marco Loog; Robert P. W. Duin; Veronika Cheplygina; Wan-Jui Lee (SIMBAD, 2011)

Question Classification by Weighted Combination of Lexical, Syntactic and Semantic Features. - Babak Loni; Gijs van Tulder; Pascal Wiggers; David M. J. Tax; Marco Loog (TSD, 2011)

Dissimilarity-based detection of schizophrenia. - Aydin Ulas; Robert P. W. Duin; Umberto Castellani; Marco Loog; Pasquale Mirtuono; Manuele Bicego; Vittorio Murino; Marcella Bellani; Stefania Cerruti; Michele Tansella; Paolo Brambilla (Int. J. Imaging Syst. Technol., 2011)

SEDMI: Saliency based edge detection in multispectral images. - Viet Cuong Dinh; Raimund Leitner; Pavel Paclík; Marco Loog; Robert P. W. Duin (Image Vis. Comput., 2011)

On Combining Computer-Aided Detection Systems. - Meindert Niemeijer; Marco Loog; Michael D. Abràmoff; Max A. Viergever; Mathias Prokop; Bram van Ginneken (IEEE Trans. Medical Imaging, 2011)

Does one rotten apple spoil the whole barrel? - Veronika Cheplygina; David M. J. Tax; Marco Loog (ICPR, 2012)

Metric learning by directly minimizing the k-NN training error. - Konstantin Chernoff; Marco Loog; Mads Nielsen (ICPR, 2012)

A study on semi-supervised dissimilarity representation. - Viet Cuong Dinh; Robert P. W. Duin; Marco Loog (ICPR, 2012)

Training data selection for cancer detection in multispectral endoscopy images. - Viet Cuong Dinh; Marco Loog; Raimund Leitner; Olga Rajadell; Robert P. W. Duin (ICPR, 2012)

Automated classification of local patches in colon histopathology. - Habil Kalkan; Marius Nap; Robert P. W. Duin; Marco Loog (ICPR, 2012)

Improving cross-validation based classifier selection using meta-learning. - Jesse H. Krijthe; Tin Kam Ho; Marco Loog (ICPR, 2012)

Combining multi-scale dissimilarities for image classification. - Yan Li 009; Robert P. W. Duin; Marco Loog (ICPR, 2012)

Scale-invariant sampling for supervised image segmentation. - Yan Li 009; Marco Loog (ICPR, 2012)

Towards exaggerated emphysema stereotypes. - C. Chen; Lauge Sørensen; François Lauze; Christian Igel; Marco Loog; Aasa Feragen; Marleen de Bruijne; Mads Nielsen (Computer-Aided Diagnosis, 2012)

Automated Colorectal Cancer Diagnosis for Whole-Slice Histopathology. - Habil Kalkan; Marius Nap; Robert P. W. Duin; Marco Loog (MICCAI (3), 2012)

Cascaded classifier for large-scale data applied to automatic segmentation of articular cartilage. - Adhish Prasoon; Christian Igel; Marco Loog; François Lauze; Erik Dam; Mads Nielsen (Image Processing, 2012)

Nearest neighbor-based importance weighting. - Marco Loog (MLSP, 2012)

Supervised localization of cell nuclei on TMA images. - Alessandro Ibba; Robert P. W. Duin; Marco Loog (MMBIA, 2012)

Class-Dependent Dissimilarity Measures for Multiple Instance Learning. - Veronika Cheplygina; David M. J. Tax; Marco Loog (SSPR/SPR, 2012)

Mode Seeking Clustering by KNN and Mean Shift Evaluated. - Robert P. W. Duin; Ana L. N. Fred; Marco Loog; Elzbieta Pekalska (SSPR/SPR, 2012)

The Dipping Phenomenon. - Marco Loog; Robert P. W. Duin (SSPR/SPR, 2012)

Constrained Log-Likelihood-Based Semi-supervised Linear Discriminant Analysis. - Marco Loog; Are Charles Jensen (SSPR/SPR, 2012)

Bridging Structure and Feature Representations in Graph Matching. - Wan-Jui Lee; Veronika Cheplygina; David M. J. Tax; Marco Loog; Robert P. W. Duin (Int. J. Pattern Recognit. Artif. Intell., 2012)

Scale selection for supervised image segmentation. - Yan Li 009; David M. J. Tax; Marco Loog (Image Vis. Comput., 2012)

Femoral cartilage segmentation in Knee MRI scans using two stage voxel classification. - Adhish Prasoon; Christian Igel; Marco Loog; François Lauze; Erik B. Dam; Mads Nielsen (EMBC, 2013)

Combining Instance Information to Classify Bags. - Veronika Cheplygina; David M. J. Tax; Marco Loog (MCS, 2013)

The Link between Multiple-Instance Learning and Learning from Only Positive and Unlabelled Examples. - Yan Li 009; David M. J. Tax; Robert P. W. Duin; Marco Loog (MCS, 2013)

Conditional Random Fields for Protein Function Prediction. - Thies Gehrmann; Marco Loog; Marcel J. T. Reinders; Dick de Ridder (PRIB, 2013)

On the Informativeness of Asymmetric Dissimilarities. - Yenisel Plasencia Calana; Veronika Cheplygina; Robert P. W. Duin; Edel B. García Reyes; Mauricio Orozco-Alzate; David M. J. Tax; Marco Loog (SIMBAD, 2013)

Stratified Generalized Procrustes Analysis. - Adrien Bartoli; Daniel Pizarro 001; Marco Loog (Int. J. Comput. Vis., 2013)

FIDOS: A generalized Fisher based feature extraction method for domain shift. - Viet Cuong Dinh; Robert P. W. Duin; Ignacio Piqueras-Salazar; Marco Loog (Pattern Recognit., 2013)

Multiple-instance learning as a classifier combining problem. - Yan Li 009; David M. J. Tax; Robert P. W. Duin; Marco Loog (Pattern Recognit., 2013)

Multi-spectral video endoscopy system for the detection of cancerous tissue. - Raimund Leitner; Martin De Biasio; Thomas Arnold; Viet Cuong Dinh; Marco Loog; Robert P. W. Duin (Pattern Recognit. Lett., 2013)

Non-Euclidean Dissimilarities: Causes, Embedding and Informativeness. - Robert P. W. Duin; Elzbieta Pekalska; Marco Loog (Similarity-Based Pattern Analysis and Recognition, 2013)

Training of Templates for Object Recognition in Invertible Orientation Scores: Application to Optic Nerve Head Detection in Retinal Images. - Erik J. Bekkers; Remco Duits; Marco Loog (EMMCVPR, 2014)

Classification of COPD with Multiple Instance Learning. - Veronika Cheplygina; Lauge Sørensen; David M. J. Tax; Jesper Johannes Holst Pedersen; Marco Loog; Marleen de Bruijne (ICPR, 2014)

Implicitly Constrained Semi-supervised Linear Discriminant Analysis. - Jesse H. Krijthe; Marco Loog (ICPR, 2014)

Network-Guided Group Feature Selection for Classification of Autism Spectrum Disorder. - Veronika Cheplygina; David M. J. Tax; Marco Loog; Aasa Feragen (MLMI, 2014)

Metric Learning in Dissimilarity Space for Improved Nearest Neighbor Performance. - Robert P. W. Duin; Manuele Bicego; Mauricio Orozco-Alzate; Sang-Woon Kim; Marco Loog (S+SSPR, 2014)

Quantile Representation for Indirect Immunofluorescence Image Classification. - David M. J. Tax; Veronika Cheplygina; Marco Loog (CoRR, 2014)

Semi-hidden target recognition in gated viewer images fused with thermal IR images. - Menno A. Smeelen; Piet B. W. Schwering; Alexander Toet; Marco Loog (Inf. Fusion, 2014)

Semi-supervised linear discriminant analysis through moment-constraint parameter estimation. - Marco Loog (Pattern Recognit. Lett., 2014)

Implicitly Constrained Semi-supervised Least Squares Classification. - Jesse H. Krijthe; Marco Loog (IDA, 2015)

Label Stability in Multiple Instance Learning. - Veronika Cheplygina; Lauge Sørensen; David M. J. Tax; Marleen de Bruijne; Marco Loog (MICCAI (1), 2015)

Single- vs. multiple-instance classification. - Ethem Alpaydin; Veronika Cheplygina; Marco Loog; David M. J. Tax (Pattern Recognit., 2015)

Multiple instance learning with bag dissimilarities. - Veronika Cheplygina; David M. J. Tax; Marco Loog (Pattern Recognit., 2015)

On classification with bags, groups and sets. - Veronika Cheplygina; David M. J. Tax; Marco Loog (Pattern Recognit. Lett., 2015)

Semi-Supervised Nearest Mean Classification Through a Constrained Log-Likelihood. - Marco Loog; Are Charles Jensen (IEEE Trans. Neural Networks Learn. Syst., 2015)

On Measuring and Quantifying Performance: error rates, surrogate Loss, and an Example in Semi-Supervised Learning. - Marco Loog; Jesse H. Krijthe; Are Charles Jensen (Handbook of Pattern Recognition and Computer Vision, 2016)

A Compact Representation of Multiscale Dissimilarity Data by Prototype Selection. - Yenisel Plasencia Calana; Yan Li 009; Robert P. W. Duin; Mauricio Orozco-Alzate; Marco Loog; Edel B. García Reyes (CIARP, 2016)

Learning Algorithms for Digital Reconstruction of Van Gogh's Drawings. - Yuan Zeng 001; Jiexiong Tang; Jan C. A. van der Lubbe; Marco Loog (EuroMed (1), 2016)

Weighted K-Nearest Neighbor revisited. - Manuele Bicego; Marco Loog (ICPR, 2016)

On regularization parameter estimation under covariate shift. - Wouter M. Kouw; Marco Loog (ICPR, 2016)

Reproducible Pattern Recognition Research: The Case of Optimistic SSL. - Jesse H. Krijthe; Marco Loog (RRPR@ICPR, 2016)

Optimistic semi-supervised least squares classification. - Jesse H. Krijthe; Marco Loog (ICPR, 2016)

An empirical investigation into the inconsistency of sequential active learning. - Marco Loog; Yazhou Yang (ICPR, 2016)

A soft-labeled self-training approach. - Alexander Mey; Marco Loog (ICPR, 2016)

Active learning using uncertainty information. - Yazhou Yang; Marco Loog (ICPR, 2016)

The Peaking Phenomenon in Semi-supervised Learning. - Jesse H. Krijthe; Marco Loog (S+SSPR, 2016)

Template Matching on the Roto-Translation Group. - Erik J. Bekkers; Marco Loog; Bart M. ter Haar Romeny; Remco Duits (CoRR, 2016)

The Pessimistic Limits of Margin-based Losses in Semi-supervised Learning. - Jesse H. Krijthe; Marco Loog (CoRR, 2016)

Feature-Level Domain Adaptation. - Wouter M. Kouw; Laurens J. P. van der Maaten; Jesse H. Krijthe; Marco Loog (J. Mach. Learn. Res., 2016)

Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification. - Marco Loog (IEEE Trans. Pattern Anal. Mach. Intell., 2016)

Dissimilarity-Based Ensembles for Multiple Instance Learning. - Veronika Cheplygina; David M. J. Tax; Marco Loog (IEEE Trans. Neural Networks Learn. Syst., 2016)

Object-Extent Pooling for Weakly Supervised Single-Shot Localization. - Amogh Gudi; Nicolai van Rosmalen; Marco Loog; Jan C. van Gemert (BMVC, 2017)

Supervised Scale-Regularized Linear Convolutionary Filters. - Marco Loog; François Lauze (BMVC, 2017)

Target contrastive pessimistic risk for robust domain adaptation. - Wouter M. Kouw; Marco Loog (CoRR, 2017)

On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL. - Marco Loog; Jesse H. Krijthe; Are Charles Jensen (CoRR, 2017)

Scale-Regularized Filter Learning. - Marco Loog; François Lauze (CoRR, 2017)

Nuclear Discrepancy for Active Learning. - Tom J. Viering; Jesse H. Krijthe; Marco Loog (CoRR, 2017)

MR Acquisition-Invariant Representation Learning. - Wouter M. Kouw; Marco Loog; Lambertus W. Bartels; Adriënne M. Mendrik (CoRR, 2017)

On reducing sampling variance in covariate shift using control variates. - Wouter M. Kouw; Marco Loog (CoRR, 2017)

Supervised Classification: Quite a Brief Overview. - Marco Loog (CoRR, 2017)

Projected estimators for robust semi-supervised classification. - Jesse H. Krijthe; Marco Loog (Mach. Learn., 2017)

Robust semi-supervised least squares classification by implicit constraints. - Jesse H. Krijthe; Marco Loog (Pattern Recognit., 2017)

Editorial of the Special Issue on Multi-instance Learning in Pattern Recognition and Vision. - Jianxin Wu 001; Xiang Bai; Marco Loog; Fabio Roli; Zhi-Hua Zhou (Pattern Recognit., 2017)

Effects of sampling skewness of the importance-weighted risk estimator on model selection. - Wouter M. Kouw; Marco Loog (ICPR, 2018)

Contextual loss functions for optimization of convolutional neural networks generating pseudo CTs from MRI. - Marijn van Stralen; Y. Zhou; P. J. Wozny; Peter R. Seevinck; Marco Loog (Image Processing, 2018)

The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning. - Jesse H. Krijthe; Marco Loog (NeurIPS, 2018)

Gradient Descent for Gaussian Processes Variance Reduction. - Lorenzo Bottarelli; Marco Loog (S+SSPR, 2018)

Protein Remote Homology Detection Using Dissimilarity-Based Multiple Instance Learning. - Antonella Mensi; Manuele Bicego; Pietro Lovato; Marco Loog; David M. J. Tax (S+SSPR, 2018)

Target Contrastive Pessimistic Discriminant Analysis. - Wouter M. Kouw; Marco Loog (CoRR, 2018)

Semi-Generative Modelling: Domain Adaptation with Cause and Effect Features. - Julius von Kügelgen; Alexander Mey; Marco Loog (CoRR, 2018)

Distance Based Source Domain Selection for Sentiment Classification. - Lex Razoux Schultz; Marco Loog; Peyman Mohajerin Esfahani (CoRR, 2018)

Learning an MR acquisition-invariant representation using Siamese neural networks. - Wouter M. Kouw; Marco Loog; Wilbert Bartels; Adriënne M. Mendrik (CoRR, 2018)

Template Matching via Densities on the Roto-Translation Group. - Erik J. Bekkers; Marco Loog; Bart M. ter Haar Romeny; Remco Duits (IEEE Trans. Pattern Anal. Mach. Intell., 2018)

A variance maximization criterion for active learning. - Yazhou Yang; Marco Loog (Pattern Recognit., 2018)

A benchmark and comparison of active learning for logistic regression. - Yazhou Yang; Marco Loog (Pattern Recognit., 2018)

Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features. - Julius von Kügelgen; Alexander Mey; Marco Loog (AISTATS, 2019)

Open Problem: Monotonicity of Learning. - Tom J. Viering; Alexander Mey; Marco Loog (COLT, 2019)

Learning An Mr Acquisition-Invariant Representation Using Siamese Neural Networks. - Wouter M. Kouw; Marco Loog; Lambertus W. Bartels; Adriënne M. Mendrik (ISBI, 2019)

Robust Importance-Weighted Cross-Validation Under Sample Selection Bias. - Wouter M. Kouw; Jesse H. Krijthe; Marco Loog (MLSP, 2019)

Minimizers of the Empirical Risk and Risk Monotonicity. - Marco Loog; Tom J. Viering; Alexander Mey (NeurIPS, 2019)

On the Statistical Detection of Adversarial Instances over Encrypted Data. - Mina Sheikhalishahi; Majid Nateghizad; Fabio Martinelli; Zekeriya Erkin; Marco Loog (STM, 2019)

PRECISE: a domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors. - Soufiane Mourragui; Marco Loog; Mark A. van de Wiel; Marcel J. T. Reinders; Lodewyk F. A. Wessels (Bioinform., 2019)

A review of single-source unsupervised domain adaptation. - Wouter M. Kouw; Marco Loog (CoRR, 2019)

How to Manipulate CNNs to Make Them Lie: the GradCAM Case. - Tom J. Viering; Ziqi Wang 004; Marco Loog; Elmar Eisemann (CoRR, 2019)

Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results. - Alexander Mey; Marco Loog (CoRR, 2019)

Nuclear discrepancy for single-shot batch active learning. - Tom J. Viering; Jesse H. Krijthe; Marco Loog (Mach. Learn., 2019)

Multi-scale convolutional neural network for pixel-wise reconstruction of Van Gogh's drawings. - Yuan Zeng 001; Jan C. A. van der Lubbe; Marco Loog (Mach. Vis. Appl., 2019)

Single shot active learning using pseudo annotators. - Yazhou Yang; Marco Loog (Pattern Recognit., 2019)

Gaussian process variance reduction by location selection. - Lorenzo Bottarelli; Marco Loog (Pattern Recognit. Lett., 2019)

A dissimilarity-based multiple instance learning approach for protein remote homology detection. - Antonella Mensi; Manuele Bicego; Pietro Lovato; Marco Loog; David M. J. Tax (Pattern Recognit. Lett., 2019)

Black Magic in Deep Learning: How Human Skill Impacts Network Training. - Kanav Anand; Ziqi Wang 004; Marco Loog; Jan van Gemert (BMVC, 2020)

Bayesian Active Learning for Maximal Information Gain on Model Parameters. - Kasra Arnavaz; Aasa Feragen; Oswin Krause; Marco Loog (ICPR, 2020)

Respecting Domain Relations: Hypothesis Invariance for Domain Generalization. - Ziqi Wang 004; Marco Loog; Jan van Gemert (ICPR, 2020)

A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization. - Alexander Mey; Tom Julian Viering; Marco Loog (IDA, 2020)

Making Learners (More) Monotone. - Tom Julian Viering; Alexander Mey; Marco Loog (IDA, 2020)

Target Robust Discriminant Analysis. - Wouter M. Kouw; Marco Loog (S+SSPR, 2020)

Semi-supervised learning, causality, and the conditional cluster assumption. - Julius von Kügelgen; Alexander Mey; Marco Loog; Bernhard Schölkopf (UAI, 2020)

A Brief Prehistory of Double Descent. - Marco Loog; Tom J. Viering; Alexander Mey; Jesse H. Krijthe; David M. J. Tax (CoRR, 2020)

Consistency and Finite Sample Behavior of Binary Class Probability Estimation. - Alexander Mey; Marco Loog (AAAI, 2021)

ReproducedPapers.org: Openly Teaching and Structuring Machine Learning Reproducibility. - Burak Yildiz; Hayley Hung; Jesse H. Krijthe; Cynthia C. S. Liem; Marco Loog; Gosia Migut; Frans A. Oliehoek; Annibale Panichella; Przemyslaw Pawelczak; Stjepan Picek; Mathijs de Weerdt; Jan van Gemert (RRPR, 2021)

Social Processes: Self-Supervised Forecasting of Nonverbal Cues in Social Conversations. - Chirag Raman; Hayley Hung; Marco Loog (CoRR, 2021)

A Review of Domain Adaptation without Target Labels. - Wouter M. Kouw; Marco Loog (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Robust domain-adaptive discriminant analysis. - Wouter M. Kouw; Marco Loog (Pattern Recognit. Lett., 2021)

Resolution Learning in Deep Convolutional Networks Using Scale-Space Theory. - Silvia L. Pintea; Nergis Tomen; Stanley F. Goes; Marco Loog; Jan C. van Gemert (IEEE Trans. Image Process., 2021)

Explaining Two Strange Learning Curves. - Zhiyi Chen; Marco Loog; Jesse H. Krijthe (BNAIC/BENELEARN, 2022)

A View on Model Misspecification in Uncertainty Quantification. - Yuko Kato; David M. J. Tax; Marco Loog (BNAIC/BENELEARN, 2022)

Model-Based Reinforcement Learning with State Abstraction: A Survey. - Rolf A. N. Starre; Marco Loog; Frans A. Oliehoek (BNAIC/BENELEARN, 2022)

Enhancing Classifier Conservativeness and Robustness by Polynomiality. - Ziqi Wang 004; Marco Loog (CVPR, 2022)

Social Processes: Self-supervised Meta-learning Over Conversational Groups for Forecasting Nonverbal Social Cues. - Chirag Raman; Hayley Hung; Marco Loog (ECCV Workshops (3), 2022)

LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks. - Felix Mohr; Tom J. Viering; Marco Loog; Jan N. van Rijn (ECML/PKDD (5), 2022)

Why Did This Model Forecast This Future? Closed-Form Temporal Saliency Towards Causal Explanations of Probabilistic Forecasts. - Chirag Raman; Hayley Hung; Marco Loog (CoRR, 2022)

On the reusability of samples in active learning. - Gijs van Tulder; Marco Loog (CoRR, 2022)

An Analysis of Abstracted Model-Based Reinforcement Learning. - Rolf A. N. Starre; Marco Loog; Frans A. Oliehoek (CoRR, 2022)

A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance. - Marco Loog; Tom J. Viering (CoRR, 2022)

To Actively Initialize Active Learning. - Yazhou Yang; Marco Loog (Pattern Recognit., 2022)

A Review of Nonconformity Measures for Conformal Prediction in Regression. - Yuko Kato; David M. J. Tax; Marco Loog (COPA, 2023)

Why Did This Model Forecast This Future? Information-Theoretic Saliency for Counterfactual Explanations of Probabilistic Regression Models. - Chirag Raman; Alec Nonnemaker; Amelia Villegas-Morcillo; Hayley Hung; Marco Loog (NeurIPS, 2023)

Percolate: An Exponential Family JIVE Model to Design DNA-Based Predictors of Drug Response. - Soufiane Mourragui; Marco Loog; Mirrelijn M. van Nee; Mark A. van de Wiel; Marcel J. T. Reinders; Lodewyk F. A. Wessels (RECOMB, 2023)

Also for k-means: more data does not imply better performance. - Marco Loog; Jesse H. Krijthe; Manuele Bicego (Mach. Learn., 2023)

An Analysis of Model-Based Reinforcement Learning From Abstracted Observations. - Rolf A. N. Starre; Marco Loog; Elena Congeduti; Frans A. Oliehoek (Trans. Mach. Learn. Res., 2023)

Improved Generalization in Semi-Supervised Learning: A Survey of Theoretical Results. - Alexander Mey; Marco Loog (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

The Shape of Learning Curves: A Review. - Tom J. Viering; Marco Loog (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Inductive Conformal Prediction under Data Scarcity: Exploring the Impacts of Nonconformity Measures. - Yuko Kato; David M. J. Tax; Marco Loog (CoRR, 2024)

Counterintuitive Behavior of Clustering Quality: Findings for K-Means on Synthetic and Real Data. - Marco Loog; Jesse H. Krijthe; Manuele Bicego (IDA, 2025)

Abstraction for Bayesian Reinforcement Learning in Factored POMDPs. - Rolf A. N. Starre; Sammie Katt; Mustafa Mert Çelikok; Marco Loog; Frans A. Oliehoek (Trans. Mach. Learn. Res., 2025)

Learning Learning Curves. - O. Taylan Turan; David M. J. Tax; Tom J. Viering; Marco Loog (Pattern Anal. Appl., 2025)