I am director of the KU Leuven ELLIS unit and a fellow in the ELLIS Health program, part of the European Laboratory for Learning and Intelligent Systems.

I am a Core PI in the Flanders AI Research Program, and workpackage lead for (i) Decision Support Systems and (ii) Medical Imaging.

I am a member of the KU Leuven Institute for Artificial Intelligence and one of the leaders of the working group on Machine Learning and Data Science.

I will be recruiting multiple PhD students via the ELLIS PhD program. If you apply there, you can express your interest in doing a PhD with me in the application form.

Technology developed in my research is incorporated into MONA, software for ophthalmic image analysis.

I co-organized the Commands 4 Autonomous Vehicles workshop and challenge at ECCV 2020.

I co-organized the 2nd Learning from Limited Labeled Data (LLD) Workshop: Representation Learning for Weak Supervision and Beyond at ICLR 2019.

I co-organized the NIPS 2017 workshop on Learning with Limited Labeled Data: Weak Supervision and Beyond.

Education: (Mathematics Genealogy)

I would like to thank the following funding sources for supporting my research:

Teaching:

• Statistische methoden voor biomedische signaal- en beeldanalyse (B-KUL-H0H61A). Course material is available via TOLEDO.
• Multimedia Technology and Coding (B-KUL-H09P9A). All course material is available via TOLEDO.
• CVIT 6th Summer School on AI, 2022.
• Computer Vision (B-KUL-H02A5A) - Spring 2020. All course material is available via TOLEDO.
• Data Science Summer School, École Polytechnique, 2019.
• Studium Generale 2018 - Artificial Intelligence. All course material is available via TOLEDO.
• International Summer School on Data Science, Split, Croatia, September 25-29, 2017.

Students and researchers:

• Marco Mezzina, KU Leuven, Ph.D. student.
• Mingshi Li, KU Leuven, M.Sc. 2022, Ph.D. student.
• Wangduo Xie, KU Leuven, Ph.D. student.
• Karel Moens, KU Leuven, Ph.D. student.
• Chang Tian, KU Leuven, Ph.D. student.
• Klara Gawor, KU Leuven, Ph.D. student.
• Margot Verhulst, KU Leuven, Ph.D. student.
• Huy Hoang Nguyen, University of Oulu, Ph.D. student.
• Christos Theodoropoulos, KU Leuven, Ph.D. student.
• Han Zhou, KU Leuven, Ph.D. student.
• Teodora Popordanoska, KU Leuven, Ph.D. student.
• Zifu Wang, KU Leuven, Ph.D. student.
• Junyi Zhu, KU Leuven, Ph.D. student.
• Jordy Van Landeghem, KU Leuven, Ph.D. 2024.
  Thesis: Intelligent Automation for AI-driven Document Understanding.
• Dušan Grujičić, KU Leuven, M.Sc. 2018, Ph.D. 2022, Postdoc 2022-2023.
  Thesis: Spatial Representation in Models of Images and Text with Applications to Medical Document Indexing and Autonomous Driving.
• Wim Looijmans, KU Leuven, M.Sc. 2023.
• Andrei Dragomir, KU Leuven, M.Sc. 2023.
• Yingshuo Xi, KU Leuven, M.Sc. 2023.
• Rik Bossuyt, KU Leuven, M.Sc. 2023.
• Ciem Cornelissen, KU Leuven, M.Sc. 2023.
• Zhongxi Li, KU Leuven, M.Sc. 2023.
• Xingchen Ma, KU Leuven, Ph.D. 2023.
  Thesis: Uncertainty Estimation in Machine Learning: Applications in Neural Network Compression and Calibration
• Giorgia Milli, KU Leuven, M.Sc. 2022.
• An Elen, KU Leuven, M.Sc. 2022.
• Arno Leenknegt, KU Leuven, M.Sc. 2022.
• Mahmoud Ibrahim, KU Leuven, M.Sc. 2022.
• Roberto Hernández Ruiz, KU Leuven, M.Sc. 2022.
• Raphael Sayer, KU Leuven, Research Assistant 2021-2022.
• Charalampos Kalavrytinos, KU Leuven, M.Sc. 2022.
• Ruben Hemelings, KU Leuven, M.Sc. 2017 [video], Ph.D. 2021.
  Thesis: Deep Learning in Glaucoma.
• Jonathan Lowe, KU Leuven, M.Sc. 2021.
• Gwenaël Van Looveren, KU Leuven, M.Sc. 2021.
• Joeri Jessen, KU Leuven, M.Sc. 2021.
• Stijn Jansen, KU Leuven, M.Sc. 2021.
• Aleksei Tiulpin, KU Leuven, Postdoc 2020-2021.
• Aida Ashrafi, KU Leuven, Research Assistant 2018-2020.
• Maxim Berman, KU Leuven, Ph.D. 2020.
  Thesis: Tractable Approximations for Achieving Higher Model Efficiency in Computer Vision
• Guojun Wu, KU Leuven, M.Sc. 2020.
• Guillaume Lamine, KU Leuven, M.Sc. 2020.
• Roshni Kamath, KU Leuven, M.Sc. 2020.
• Andreas Smolders, KU Leuven, M.Sc. 2020.
• Gaspar Pizarro, KU Leuven, M.Sc. 2020.
• Niels Verleysen, KU Leuven, M.Sc. 2020.
• Anthoula Mountzouri, KU Leuven, M.Sc. 2020.
• Toon Vanderschueren, KU Leuven, M.Sc. 2020.
• Marthe Vanhulst, KU Leuven, M.Sc. 2020.
• Amal Rannen Triki, KU Leuven, Ph.D. 2020.
  Thesis: Function Norms for Neural Networks: Theory and Applications
• Sinnu Thomas, KU Leuven, Postdoc 2018-2020.
• Ricardo Elizondo, KU Leuven, M.Sc. 2020.
• Dávid Kerekes, KU Leuven, M.Sc. 2020.
• Swarnalata Patra, KU Leuven, M.Sc. 2020.
• Shivangi Srivastava, Wageningen University, visiting Ph.D. student, 2018-2019.
• Axel-Jan Rousseau, KU Leuven, M.Sc. 2019.
• Hendrik Motmans, KU Leuven, M.Sc. 2019.
• Eugene Belilovsky, Université Paris-Saclay & KU Leuven, Ph.D. 2018.
  Thesis: Structured Sparse Learning on Graphs in High-Dimensional Data with Applications to Neuroimaging
• Alexander Van Rompaey, KU Leuven, M.Sc. 2018.
• Gilles Robijns, KU Leuven, M.Sc. 2018.
• Thomas Strypsteen, KU Leuven, M.Sc. 2018.
• Thomas Verelst, KU Leuven, M.Sc. 2018.
• Nick Seeuws, KU Leuven, M.Sc. 2018.
• Mathijs Schuurmans, KU Leuven, M.Sc. 2018.
• José Ignacio Orlando, Universidad Nacional del Centro de la Provincia de Buenos Aires, Ph.D. 2017.
  Thesis: Machine learning for ophthalmic screening and diagnostics from fundus images
• Arthur Talbot, ENS Paris-Saclay, visiting student 2017.
• Nele Gerrits, KU Leuven, M.Sc. 2017.
  
• Jiaqian Yu, Université Paris-Saclay, Ph.D. 2017.
  Thesis: Empirical risk minimization with non-modular loss functions
• Wacha Bounliphone, Université Paris-Saclay & KU Leuven, Ph.D. 2017.
  Thesis: Statistically and computationally efficient hypothesis tests for dependency and similarity
• Katerina Gkirtzou, École Centrale Paris, Ph.D. 2013.
  Thesis: Sparsity regularization and graph-based representation in medical imaging
• Wojciech Zaremba, École Polytechnique, M.Sc. 2012.
  Thesis: Modeling the variability of EEG/MEG data through statistical machine learning
• Ben Mather, University of Oxford, MEng 2011.
• Shah Ruhul Amin, University of Oxford, MEng 2010.
• Jacquelyn A. Shelton, Universität Tübingen, M.Sc. 2010.
  Thesis: Semi-supervised Subspace Learning and Application to Human Functional Magnetic Brain Resonance Imaging Data

Publications: (Google Scholar Profile)

• Kontras, K., C. Chatzichristos, M. B. Blaschko, and M. De Vos: Improving Multimodal Learning with Multi-Loss Gradient Modulation. arXiv:2405.07930, 2024. [bibtex]
   
• Shi, J., J. Zhu, D. M. Pelt, K. J. Batenburg, and M. B. Blaschko: Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction. arXiv:2405.02509, 2024. [bibtex]
   
• Hamed, O., S. Bakkali, M.-F. Moens, M. B. Blaschko, and J. Van Landeghem: Multimodal Adaptive Inference with Anytime Early Exiting. International Conference on Document Analysis and Recognition (ICDAR), 2024. [bibtex]
   
• Van Landeghem, J., S. Maity, A. Banerjee, M. B. Blaschko, M.-F. Moens, J. Llados, and S. Biswas: DistilDoc: Knowledge Distillation for Visually-Rich Document Applications. International Conference on Document Analysis and Recognition (ICDAR), 2024. [bibtex; code]
   
• Liu, E., J. Zhu, Z. Lin, X. Ning, M. B. Blaschko, S. Yekhanin, S. Yan, G. Dai, H. Yang, Y. Wang: Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better. arXiv:2404.02241, 2024. [bibtex; code]
   
• Moens, K., J. De Vylder, M. B. Blaschko, T. Tuytelaars: Laparoflow-SSL: Image Analysis From a Tiny Dataset Through Self-Supervised Transformers Leveraging Unlabeled Surgical Video. Medical Imaging with Deep Learning (MIDL), 2024. [bibtex]
   
• Maier-Hein, L., A. Reinke, P. Godau et al.: Metrics reloaded: recommendations for image analysis validation. Nature Methods 21, 195212, 2024. [bibtex; doi]
   
• Jha, A., M. B. Blaschko, Y. M. Asano, and T. Tuytelaars: The Common Stability Mechanism behind most Self-Supervised Learning Approaches. arXiv:2402.14957, 2024. [bibtex]
   
• Zhu, J., Z. Lin, E. Liu, X. Ning, and M. B. Blaschko: Rescaling Intermediate Features Makes Trained Consistency Models Perform Better. Tiny Papers @ ICLR 2024. [bibtex]
   
• Li, M., D. Grujicic, S. De Saeger, S. Heremans, B. Somers, and M. B. Blaschko: Biological Valuation Map of Flanders: A Sentinel-2 Imagery Analysis. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2024. [bibtex; code]
   
• Popordanoska, T., G. Radevski, T. Tuytelaars, M. B. Blaschko: Estimating calibration error under label shift without labels. arXiv:2312.08586, 2023. [bibtex]
   
• Gruber, S. G., T. Popordanoska, A. Tiulpin, F. Buettner, and M. B. Blaschko: Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors. 27th International Conference on Artificial Intelligence and Statistics (AISTATS), 2024. [bibtex; code]
   
• Popordanoska, T., A. Tiulpin, and M. B. Blaschko: Beyond Classification: Definition and Density-based Estimation of Calibration in Object Detection. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024. [bibtex; code]
   
• Van Landeghem, J., S. Biswas, M. Blaschko, and M.-F. Moens: Beyond Document Page Classification: Design, Datasets, and Challenges. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024. [bibtex]
   
• Wang, Z., X. Ning, and M. B. Blaschko: Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels. Neural Information Processing Systems (NeurIPS), 2023. [bibtex; code]
   
• Wang, Z., M. Berman, A. Rannen-Triki, P. Torr, D. Tuia, T. Tuytelaars, L. Van Gool, J. Yu, and M. B. Blaschko: Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union. Neural Information Processing Systems (NeurIPS), 2023. [bibtex; code]
   
• Zhou, H., X. Ma, and M. B. Blaschko: A Corrected Expected Improvement Acquisition Function Under Noisy Observations. Asian Conference on Machine Learning (ACML), 2023. [bibtex]
   
• Nguyen, H. H., M. B. Blaschko, S. Saarakkala, and A. Tiulpin: Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multimodal Data. IEEE Transactions on Medical Imaging, 2023. [bibtex; doi; code]
   
• Radevski, G., D. Grujicic, M. B. Blaschko, M.-F. Moens, and T. Tuytelaars: Multimodal Distillation for Egocentric Action Recognition. International Conference on Computer Vision (ICCV), 2023. [bibtex; code]
   
• Van Landeghem, J., R. Tito, L. Borchmann, M. Pietruszka, P. Joziak, R. Powalski, D. Jurkiewicz, M. Coustaty, B. Ackaert, E. Valveny, M. Blaschko, S. Moens, and T. Stanislawek: Document Understanding Dataset and Evaluation (DUDE). International Conference on Computer Vision (ICCV), 2023. [bibtex]
   
• Popordanoska, T., A. Tiulpin, and M. B. Blaschko: To trust or not to trust: Assessing calibration error under covariate shift without labels. Women in Computer Vision Workshop at ICCV, 2023. [bibtex]
   
• Wang, Z., T. Popordanoska, J. Bertels, R. Lemmens, and M. B. Blaschko: Dice Semimetric Losses: Optimizing the Dice Score with Soft Labels. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023. [bibtex; code]
   
• Xie, W. and M. B. Blaschko: Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023. [bibtex]
   
• Zhu, J. and M. B. Blaschko: Improving Differentially Private SGD via Randomly Sparsified Gradients. Transactions on Machine Learning Research, 2023. [bibtex; code]
   
• Hemelings, R., B. Elen, A. K. Schuster, M. B. Blaschko, J. Barbosa-Breda, P. Hujanen, A. Junglas, S. Nickels, A. White, N. Pfeiffer, P. Mitchell, P. De Boever, A. Tuulonen, and I. Stalmans: A generalizable deep learning regression model for automated glaucoma screening from fundus images. npj Digital Medicine, 2023. [bibtex; doi]
   
• Zhu, J., R. Yao, and M. B. Blaschko: Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning. International Conference on Machine Learning (ICML), 2023. [bibtex; code]
   
• Zhu, J., X. Ma, and M. B. Blaschko: Confidence-aware Personalized Federated Learning via Variational Expectation Maximization. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. [bibtex; code]
   
• Thomas, S. S., G. Lamine, J. Palandri, M. Lakehal-ayat, P. Chakravarty, F. Wolf-Monheim, and M. B. Blaschko: Mitigating Bias in Bayesian Optimized Data While Designing MacPherson Suspension Architecture, in IEEE Transactions on Artificial Intelligence, 2023. [bibtex; doi]
   
• Rousseau, A.-J., T. Becker, S. Appeltans, M. B. Blaschko, and D. Valkenborg: A spatial extension to beta calibration for medical segmentation models. IEEE 20th International Symposium on Biomedical Imaging (ISBI), 2023. [bibtex]
   
• Reinke, A., et al.: Common Limitations of Image Processing Metrics: A Picture Story. arXiv:2104.05642, 2023. [bibtex]
   
• Reinke, A., et al.: Understanding metric-related pitfalls in image analysis validation. arXiv:2302.01790, 2023. [bibtex]
   
• Li, M., Z. Wang, and M. B. Blaschko: Improved Imagery Throughput via Cascaded Uncertainty Pruning on U-Net++. Northern Lights Deep Learning Conference 2023. [bibtex; doi]
   
• Popordanoska, T., R. Sayer, and M. B. Blaschko: A Consistent and Differentiable Lp Canonical Calibration Error Estimator. Neural Information Processing Systems (NeurIPS), 2022. [bibtex; code]
   
• Ma, X., J. Zhu, M. B. Blaschko: Tackling Personalized Federated Learning with Label Concept Drift via Hierarchical Bayesian Modeling. Workshop on Federated Learning: Recent Advances and New Challenges (in Conjunction with NeurIPS), 2022. [bibtex]
   
• Oniga, R., M. Blaschko, T. Eelbode, F. Maes, R. Bisschops, P. Bossuyt: Deep learning for prediction of future endoscopic disease activity in Ulcerative Colitis. Proceedings of the 5th International Conference XGEN, 2022. [bibtex]
   
• Deruyttere, T., D. Grujicic, M. B. Blaschko, and M.-F. Moens: Talk2Car: Predicting Physical Trajectories for Natural Language Commands. IEEE Access, 2022. [bibtex; doi]
   
• Radevski, G., D. Grujicic, M. Blaschko, M.-F. Moens, and T. Tuytelaars: Students taught by multimodal teachers are superior action recognizers. 2nd International Ego4D Workshop @ ECCV, 2022. [bibtex]
   
• Koper, M. J., S. O. Tomé, K. Gawor, A. Belet, E. Van Schoor, J. Schaeverbeke, R. Vandenberghe, M. Vandenbulcke, E. Ghebremedhin, M. Otto, C. A. F. von Arnim, S. Balusu, M. B. Blaschko, B. De Strooper, and D. R. Thal: LATE-NC aggravates GVD-mediated necroptosis in Alzheimers disease. Acta Neuropathologica Communications, 2022. [bibtex; doi]
   
• Tiulpin, A. and M. B. Blaschko: Greedy Bayesian Posterior Approximation with Deep Ensembles. Transactions on Machine Learning Research, 2022. [bibtex; code]
   
• Hemelings, R., B. Elen, J. Barbosa-Breda, E. Bellon, M. B. Blaschko, P. De Boever, I. Stalmans: Pointwise visual field estimation from optical coherence tomography in glaucoma using deep learning. Translational Vision Science & Technology, 2022. [bibtex; doi]
   
• Popordanoska, T. and M. B. Blaschko: KULeuven at LeQua 2022: Model Calibration in Quantification Learning. Conference and Labs of the Evaluation Forum (CLEF), 2022. [bibtex]
   
• Wang, Z. and M. B. Blaschko: MRF-UNets: Searching UNet with Markov Random Fields. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML), 2022. [bibtex; code]
   
• Grujicic, D. and M. B. Blaschko: 2-D latent space models: Layer-wise perceptual training and spatial grounding. International Conference on Pattern Recognition (ICPR), 2022. [bibtex]
   
• Zhou, H., A. Ashrafi, and M. B. Blaschko: Combinatorial optimization for low bit-width neural networks. International Conference on Pattern Recognition (ICPR), 2022. [bibtex]
   
• Van Landeghem, J., M. Blaschko, B. Anckaert and M.-F. Moens: Benchmarking Scalable Predictive Uncertainty in Text Classification. IEEE Access, 2022. [bibtex; code; doi]
   
• Nguyen, H. H., S. Saarakkala, M. B. Blaschko, and A. Tiulpin: CLIMAT: Clinically-Inspired Multi-Agent Transformers for Knee Osteoarthritis Trajectory Forecasting. IEEE International Symposium on Biomedical Imaging (ISBI), 2022. [bibtex; code; doi]
   
• Grujicic, D., T. Deruyttere, M.-F. Moens, and M. B. Blaschko: Predicting Physical World Destinations for Commands Given to Self-Driving Cars. Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022. [bibtex; data; doi]
   
• Wang, Z. and M. B. Blaschko: Optimizing Slimmable Networks for Multiple Target Platforms. Proceedings of the Northern Lights Deep Learning Conference, 2022. [bibtex; doi]
   
• Zhu, J. and M. B. Blaschko: Differentially Private SGD with Sparse Gradients. arXiv:2112.00845, 2021. [bibtex]
   
• Tiulpin, A. and M. B. Blaschko: Greedy Bayesian Posterior Approximation with Deep Ensembles. NeurIPS Workshop on Bayesian Deep Learning, 2021. [bibtex; code]
   
• Hemelings, R., B. Elen, J. Barbosa Breda, M. B. Blaschko, P. De Boever, and I. Stalmans: Deep learning on fundus images detects glaucoma beyond the optic disc. Scientific Reports, 2021. [bibtex; doi]
   
• Thomas, S. S., J. Palandri, M. Lakehal-ayat, P. Chakravarty, F. Wolf-Monheim, and M. B. Blaschko: Kinematics Design of a MacPherson Suspension Architecture based on Bayesian Optimization. IEEE Transactions on Cybernetics, 2021. [bibtex; doi]
   
• Popordanoska, T., J. Bertels, D. Vandermeulen, F. Maes, M. B. Blaschko: On the relationship between calibrated predictors and unbiased volume estimation. Medical Image Computing and Computer Assisted Interventions (MICCAI), 2021. [bibtex; code; doi]
   
• Popordanoska, T., A. Tiulpin, W. Bounliphone, and M. B. Blaschko: Distribution-Independent Confidence Intervals for the Eigendecomposition of Covariance Matrices via the Eigenvalue-Eigenvector Identity. ICML Workshop on Distribution Free Uncertainty Quantification, 2021.[bibtex; code]
   
• Tiulpin, A. and M. B. Blaschko: Greedy Bayesian Posterior Approximation with Deep Ensembles. arXiv:2105.14275, 2021. [bibtex, code]
   
• Ma, X. and M. B. Blaschko: Meta-Cal: Well-controlled Post-hoc Calibration by Ranking. International Conference on Machine Learning (ICML), 2021. [bibtex; code]
   
• Heremans, S., F. Turkelboom, M. Verhulst, M. B. Blaschko, B. Somers: Remote Sensing and Deep Learning for Environmental Policy Support: From Theory to Practice. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2021. [bibtex; doi]
   
• Hemelings, R., B. Elen, J. Barbosa Breda, M. B. Blaschko, P. De Boever, and I. Stalmans: Convolutional neural network predicts visual field threshold values from optical coherence tomography scans. The Association for Research in Vision and Ophthalmology Annual Meeting (ARVO), 2021. [bibtex]
   
• Zhu, J. and M. B. Blaschko: R-GAP: Recursive Gradient Attack on Privacy. International Conference on Learning Representations (ICLR), 2021. [bibtex; code]
   
• Rousseau, A.-J., T. Becker, J. Bertels, M. B. Blaschko, and D. Valkenborg: Post Training Uncertainty Calibration of Deep Networks for Medical Image Segmentation. IEEE International Symposium on Biomedical Imaging (ISBI), 2021. [bibtex; code; doi]
   
• Hemelings, R., B. Elen, M. B. Blaschko, J. Jacob, I. Stalmans, and P. De Boever: Pathological myopia classification with simultaneous lesion segmentation using deep learning. Computer Methods and Programs in Biomedicine, 2021. [bibtex; doi]
   
• Nguyen, H. H., S. Saarakkala, M. B. Blaschko, and A. Tiulpin: Annotation-Efficient Deep Semi-Supervised Learning for Automatic Knee Osteoarthritis Severity Diagnosis from Plain Radiographs. Medical Imaging meets NeurIPS Workshop, 2020. [bibtex; code]
   
• Ma, X. and M. B. Blaschko: Additive Tree-Structured Conditional Parameter Spaces in Bayesian Optimization: A Novel Covariance Function and a Fast Implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020. [bibtex; code; doi]
   
• Grujicic, D., G. Radevski, T. Tuytelaars, and M. B. Blaschko: Learning to ground medical text in a 3D human atlas. The SIGNLL Conference on Computational Natural Language Learning (CoNLL), 2020. [bibtex; code; doi]
   
• Deruyttere, T., S. Vandenhende, D. Grujicic, Y. Liu, L. Van Gool, M. Blaschko, T. Tuytelaars, and M.-F. Moens: Commands 4 Autonomous Vehicles (C4AV) Workshop Summary. European Conference on Computer Vision Workshop Proceedings, 2020. [bibtex; doi]
   
• Van Landeghem, J., M. B. Blaschko, B. Anckaert, and M.-F. Moens: Predictive Uncertainty for Probabilistic Novelty Detection in Text Classification. ICML Workshop on Uncertainty and Robustness in Deep Learning, 2020. [bibtex]
   
• Eelbode, T., J. Bertels, M. Berman, D. Vandermeulen, F. Maes, R. Bisschops, and M. B. Blaschko: Evaluation with Dice score and Jaccard index for Medical Image Segmentation: Optimization in Theory and Practice. IEEE Transactions on Medical Imaging, 2020. [bibtex; code; doi]
   
• Grujicic, D., G. Radevski, T. Tuytelaars, and M. B. Blaschko: Self-supervised context-aware Covid-19 document exploration through atlas grounding. ACL Workshop on Natural Language Processing for COVID-19, 2020. [bibtex; code; video]
   
• Nguyen, H. H., S. Saarakkala, M. B. Blaschko, and A. Tiulpin: Semixup: In- and Out-of-Manifold Regularization for Deep Semi-Supervised Knee Osteoarthritis Severity Grading from Plain Radiographs. IEEE Transactions on Medical Imaging, 2020. [bibtex; code; doi]
   
• Berman, M., L. Pishchulin, N. Xu, M. B. Blaschko, and G. Medioni: AOWS: Adaptive and Optimal Network Width Search with Latency Constraints. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. [bibtex; code; doi]
   
• Yu, J. and M. B. Blaschko: The Lovász Hinge: A Novel Convex Surrogate for Submodular Losses. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(3):735-748, 2020. [bibtex; code; doi]
   
• Berman, M. and M. B. Blaschko: Discriminative training of conditional random fields with probably submodular constraints. International Journal of Computer Vision, 2020. [bibtex; code; doi]
   
• Ma, X. and M. B. Blaschko: Additive tree-structured covariance function for conditional parameter spaces in Bayesian optimization. Artificial Intelligence and Statistics (AISTATS), 2020. [bibtex; code]
   
• Hemelings, R., B. Elen, J. Barbosa Breda, S. Lemmens, M. Meire, S. Pourjavan, E. Vandewalle, S. Van de Veire, M. B. Blaschko, P. De Boever, and I. Stalmans: Accurate prediction of glaucoma from color fundus images with a convolutional neural network that relies on active and transfer learning. Acta Ophthalmologica, 98(1):e94-e100, 2020. [bibtex; doi]
   
• Ashrafi, A. and M. B. Blaschko: Combinatorial discrete optimization of linear classifiers with binary weights. Women in Machine Learning Workshop (WiML), Vancouver, Canada 2019. [bibtex]
   
• Ma, X., A. Rannen Triki, M. Berman, C. Sagonas, J. Cali, and M. B. Blaschko: A Bayesian optimization framework for neural network compression. Proceedings of the International Conference on Computer Vision (ICCV), 2019. [bibtex; doi]
   
• Rannen Triki, A., M. Berman, V. Kolmogorov, and M. B. Blaschko: Function Norms for Neural Networks. Workshop on Statistical Deep Learning in Computer Vision at ICCV, 2019. [bibtex; doi]
   
• Oyallon, E., S. Zagoryuko, G. Huang, N. Komodakis, S. Lacoste-Julien, M. Blaschko, and E. Belilovsky: Scattering Networks for Hybrid Representation Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(9):2208-2221, 2019. [bibtex; code; doi]
   
• Srivastava, S., M. Berman, M. B. Blaschko, and D. Tuia: Adaptive Compression-based Lifelong Learning. British Machine Vision Conference (BMVC), 2019. [bibtex]
   
• Bertels, J., T. Eelbode, M. Berman, D. Vandermeulen, F. Maes, R. Bisschops, and M. B. Blaschko: Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory and Practice. Medical Image Computing and Computer Assisted Interventions (MICCAI), 2019. [bibtex; code; doi]
   
• Hemelings, R., B. Elen, I. Stalmans, P. De Boever, and M. B. Blaschko: A Fully Convolutional Network for Artery-Vein Segmentation in Fundus Images. Computerized Medical Imaging and Graphics, 2019. [bibtex; code and annotations; doi]
   
• Thomas, S. S., J. Palandri, M. Lakehal-ayat, P. Chakravarty, F. Wolf-Monheim, and M. B. Blaschko: Designing MacPherson Suspension Architectures using Bayesian Optimization. 28th Belgian Dutch Conference on Machine Learning (Benelearn), 2019. [bibtex]
   
• Verelst, T., M. B. Blaschko, and M. Berman: Generating superpixels using deep image representations. arXiv:1903.04586, 2019. [bibtex]
   
• Berman, M., M. B. Blaschko, A. Rannen Triki, and J. Yu: Yes, IoU loss is submodular - as a function of the mispredictions. arXiv:1809.01845, 2018. [bibtex]
   
• Berman, M. and M. B. Blaschko: Supermodular Locality Sensitive Hashes. arXiv:1807.06686, 2018. [bibtex]
   
• Rannen Triki, A., M. B. Blaschko, Y. M. Jung, S. Song, H. J. Han, S. I. Kim, C. Joo: Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks. Computerized Medical Imaging and Graphics, 2018. [bibtex; code; doi]
   
• Schuurmans, M., M. Berman, and M. B. Blaschko: Efficient semantic image segmentation with superpixel pooling. arXiv:1806.02705, 2018. [bibtex; code]
   
• Orlando, J. I., J. Barbosa Breda, K. van Keer, M. B. Blaschko, P. J. Blanco, and C. A. Bulant: Towards a glaucoma risk index based on simulated hemodynamics from fundus images. Medical Image Computing and Computer Assisted Intervention (MICCAI), 2018. [bibtex; code; doi]
   
• Berman, M., A. Rannen Triki, and M. B. Blaschko: The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. Computer Vision and Pattern Recognition (CVPR), 2018. [bibtex; code; video; doi]
   
• Verelst, T., M. Berman, and M. B. Blaschko: Generating superpixels with deep representations. CVPR Workshop on DeepVision: Deep Learning in Computer Vision, 2018. [bibtex]
   
• Orlando, J. I., E. Prokofyeva, M. del Fresno, and M. B. Blaschko: An Ensemble Deep Learning Based Approach for Red Lesion Detection in Fundus Images. Computer Methods and Programs in Biomedicine, 153:115-127, 2018. [bibtex; code; doi]
   
• Mehrkanoon, S., M. B. Blaschko, J. A. K. Suykens: Shallow and Deep Models for Domain Adaptation. European Symposium on Artificial Neural Networks (ESANN), 2018. [bibtex]
   
• Rannen Triki, A., M. Berman, and M. B. Blaschko: Stochastic Weighted Function Norm Regularization. arXiv:1710.06703, 2017. [bibtex]
   
• Rannen Triki, A., R. Aljundi, M. B. Blaschko, and T. Tuytelaars: Encoder Based Lifelong Learning. International Conference on Computer Vision (ICCV), 2017. [bibtex; code; doi]
   
• Orlando, J. I., K. van Keer, J. Barbosa Breda, H. L. Manterola, M. B. Blaschko, and A. Clausse: Proliferative Diabetic Retinopathy Characterization based on Fractal Features: Evaluation on a Publicly Available Data Set. Medical Physics, 44(12):6425-6434, 2017. [bibtex; code; doi]
   
• Blaschko, M. B.: Slack and Margin Rescaling as Convex Extensions of Supermodular Functions. International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2017. [bibtex; code; doi]
   
• Berman, M. and M. B. Blaschko: Optimization of the Jaccard index for image segmentation with the Lovász hinge. arXiv:1705.08790, 2017. [bibtex; code]
   
• Belilovsky, E., K. Kastner, G. Varoquaux, and M. B. Blaschko: Learning to Discover Sparse Graphical Models. International Conference on Machine Learning (ICML), 2017. [bibtex; code; video]
   
• Belilovsky, E., M. B. Blaschko, J. R. Kiros, R. Urtasun, R. Zemel: Joint Embeddings of Scene Graphs and Images. International Conference on Learning Representations Workshop Track (ICLR), 2017. [bibtex]
   
• Belilovsky, E., K. Kastner, G. Varoquaux, and M. B. Blaschko: Learning to Discover Sparse Graphical Models. International Conference on Learning Representations Workshop Track (ICLR), 2017. [bibtex; code]
   
• Yu, J. and M. B. Blaschko: An Efficient Decomposition Framework for Discriminative Segmentation with Supermodular Losses. arXiv:1702.03690, 2017. [bibtex; code]
   
• Orlando, J. I., E. Prokofyeva, and M. B. Blaschko: A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images. IEEE Transactions on Biomedical Engineering, 64(1):16-27, 2017. [bibtex; code and data; doi]
   
• Belilovsky, E., G. Varoquaux, and M. B. Blaschko: Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity. Neural Information Processing Systems (NIPS), 2016. [bibtex; code]
   
• Rannen Triki, A. and M. B. Blaschko: Stochastic Function Norm Regularization of DNNs. NIPS Workshop on Optimization for Machine Learning (OPT), 2016. [bibtex; code]
   
• Berman, M. and M. B. Blaschko: Efficient optimization for probably submodular constraints in CRFs. NIPS Workshop on Constructive Machine Learning, 2016. [bibtex; code]
   
• Rannen Triki, A. and M. B. Blaschko: Stochastic Function Norm Regularization of Deep Networks. In Women in Machine Learning Workshop. Barcelona, Spain, 2016. [bibtex; code]
   
• Yu, J. and M. B. Blaschko: Efficient Learning for Discriminative Segmentation with Supermodular Losses. In Women in Machine Learning Workshop. Barcelona, Spain, 2016. [bibtex; code]
   
• Bounliphone, W., E. Belilovsky, A. Tenenhaus, I. Antonoglou, A. Gretton, and M. B. Blaschko: Fast Non-Parametric Tests of Relative Dependency and Similarity. arXiv:1611.05740, 2016. [bibtex; code; code]
   
• Orlando, J. I., E. Prokofyeva, M. del Fresno, and M. B. Blaschko: Convolutional Neural Network Transfer for Automated Glaucoma Identification. International Symposium on Medical Information Processing and Analysis, 2016. [bibtex; code; doi]
   
• Yu, J. and M. B. Blaschko: Efficient Learning for Discriminative Segmentation with Supermodular Losses. British Machine Vision Conference (BMVC), 2016. [bibtex; code; doi]
   
• Yu, J. and M. B. Blaschko: A Convex Surrogate Operator for General Non-Modular Loss Functions. Benelearn. Kortrijk, Belgium, 2016. [bibtex; code]
   
• Rannen Triki, A. and M. B. Blaschko: Stochastic Function Norm Regularization of Deep Networks. arXiv:1605.09085, 2016. [bibtex; code]
   
• Bounliphone, W. and M. B. Blaschko: A U-statistic Approach to Hypothesis Testing for Structure Discovery in Undirected Graphical Models. arXiv:1604.01733, 2016. [bibtex; code]
   
• Ghafarianzadeh, M., M. B. Blaschko, and G. Sibley: Efficient, Dense, Object-based Segmentation from RGBD Video. IEEE International Conference on Robotics and Automation (ICRA), 2016. [bibtex; doi]
   
• Yu, J. and M. B. Blaschko: A Convex Surrogate Operator for General Non-Modular Loss Functions. International Conference on Artificial Intelligence and Statistics (AISTATS), 2016. [bibtex; code]
   
• Zaremba, W. and M. B. Blaschko: Discriminative training of CRF models with probably submodular constraints. IEEE Winter Conference on Applications of Computer Vision (WACV), 2016. [bibtex; code; doi]
   
• Bounliphone, W., E. Belilovsky, M. B. Blaschko, I. Antonoglou, and A. Gretton: A Test of Relative Similarity for Model Selection in Generative Models. International Conference on Learning Representations (ICLR), 2016. [Note: Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award; bibtex; code]
   
• Gkirtzou, K. and M. B. Blaschko: The Pyramid Quantized Weisfeiler-Lehman Graph Representation. Neurocomputing, 173(3):1495-1507, 2016. [bibtex; code; doi]
   
• Sidahmed, H., E. Prokofyeva, and M. B. Blaschko: Discovering Predictors of Mental Health Service Utilization with k-support Regularized Logistic Regression. Information Sciences, 329:937-949, 2016. [bibtex; code; doi]
   
• Blaschko, M. B. and J. Yu: Hardness Results for Structured Learning and Inference with Multiple Correct Outputs. Constructive Machine Learning Workshop at ICML, 2015. [bibtex]
   
• Belilovsky, E., A. Argyriou, G. Varoquaux, and M. B. Blaschko: Convex Relaxations of Penalties for Sparse Correlated Variables With Bounded Total Variation. Machine Learning, 100(2-3):533-553, 2015. [bibtex; code; doi]
   
• Yu, J. and M. B. Blaschko: Learning Submodular Losses with the Lovász Hinge. International Conference on Machine Learning (ICML), 2015. [bibtex; code]
   
• Bounliphone, W., A. Gretton, A. Tenenhaus, and M. B. Blaschko: A low variance consistent test of relative dependency. International Conference on Machine Learning (ICML), 2015. [bibtex; code]
   
• Belilovsky, E., K. Gkirtzou, M. Misyrlis, A. B. Konova, J. Honorio, N. Alia-Klein, R. Z. Goldstein, D. Samaras, and M. B. Blaschko: Predictive sparse modeling of fMRI data for improved classication, regression, and visualization using the k-support norm. Computerized Medical Imaging and Graphics, 46(1):40-46, 2015. [bibtex; code; doi]
   
• Belilovsky, E., A. Argyriou, and M. B. Blaschko: Approximating Combined Discrete Total Variation and Correlated Sparsity With Convex Relaxations. NIPS Workshop on Discrete and Combinatorial Problems in Machine Learning, 2014. [bibtex; code]
   
• Bounliphone, W., A. Gretton, and M. B. Blaschko: Kernel non-parametric tests of relative dependency. NIPS Workshop on Modern Nonparametrics 3: Automating the Learning Pipeline, 2014. [bibtex; code]
   
• Yu, J. and M. B. Blaschko: Lovasz Hinge for Learning Submodular Losses. NIPS Workshop on Representation and Learning Methods for Complex Outputs, 2014. [bibtex; code]
   
• Blaschko, M. B.: Advances in Empirical Risk Minimization for Image Analysis and Pattern Recognition. Mémoire d'habilitation à diriger des recherches, École Normale Supérieure de Cachan, 2014. [bibtex]
   
• Orlando, J. I. and M. B. Blaschko: Learning fully-connected CRFs for blood vessel segmentation in retinal images. Medical Image Computing and Computer Assisted Intervention (MICCAI), 2014. [bibtex; data; doi]
   
• Ghafarianzadeh, M., M. B. Blaschko, and G. Sibley: Unsupervised Spatio-Temporal Segmentation with Sparse Spectral-Clustering. British Machine Vision Conference (BMVC), 2014. [bibtex; doi]
   
• Blaschko, M. B., A. Mittal, and E. Rahtu: An O(n log n) Cutting Plane Algorithm for Structured Output Ranking. 36th German Conference on Pattern Recognition (GCPR), 2014. [bibtex; doi]
   
• Misyrlis, M., A. B. Konova, M. B. Blaschko, J. Honorio, N. Alia-Klein, R. Z. Goldstein, D. Samaras: Predicting cross-task behavioral variables from fMRI data using the k-support norm. Sparsity Techniques in Medical Imaging, 2014. [Note: Best paper award; bibtex; code]
   
• Vedaldi, A., S. Mahendran, S. Tsogkas, S. Maji, R. Girshick, J. Kannala, E. Rahtu, I. Kokkinos, M. B. Blaschko, D. Weiss, B. Taskar, K. Simonyan, N. Saphra, and S. Mohamed: Understanding Objects in Detail with Fine-grained Attributes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. [bibtex; code; data; doi]
   
• Blaschko, M. B.: Machine Learning for Neurological Disorders. Centraliens, 632 (2014) 40-42. [bibtex]
   
• Zaremba, W., A. Gretton, and M. Blaschko: B-tests: Low Variance Kernel Two-Sample Tests. Neural Information Processing Systems (NIPS), 2013. [bibtex; code]
   
• Gkirtzou, K., J. Honorio, D. Samaras, R. Goldstein, and M. Blaschko: fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics. International Workshop on Machine Learning in Medical Imaging (MLMI), 2013. [bibtex; code; doi]
   
• Gkirtzou, K., J.-F. Deux, G. Bassez, A. Sotiras, A. Rahmouni, T. Varacca, N. Paragios, and M. B. Blaschko: Sparse classification with MRI based markers for neuromuscular disease categorization. International Workshop on Machine Learning in Medical Imaging (MLMI), 2013.[bibtex; code; doi]
   
• Maji, S., E. Rahtu, J. Kannala, M. Blaschko, and A. Vedaldi: Fine-Grained Visual Classification of Aircraft. arXiv:1306.5151, 2013. [bibtex; code and data]
   
• Blaschko, M. B., W. Zaremba, and A. Gretton: Taxonomic Prediction with Tree-Structured Covariances. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2013. [bibtex; code and data; doi]
   
• Blaschko, M. B., J. Kannala, and E. Rahtu: Non Maximal Suppression in Cascaded Ranking Models. Scandinavian Conference on Image Analysis (SCIA), 2013. [bibtex; code and data; doi]
   
• Blaschko, M. B.: A Note on k-support Norm Regularized Risk Minimization. arXiv:1303.6390, 2013. [bibtex; code]
   
• Zaremba, W., M. P. Kumar, A. Gramfort, and M. B. Blaschko: Learning from M/EEG data with variable brain activation delays. International Conference on Information Processing in Medical Imaging (IPMI), 2013. [bibtex; code and data; doi]
   
• Gkirtzou, K., J. Honorio, D. Samaras, R. Goldstein, and M. B. Blaschko: fMRI Analysis of Cocaine Addiction Using k-support Sparsity. International Symposium on Biomedical Imaging (ISBI), 2013. [bibtex; code; doi]
   
• Flint, A. and M. B. Blaschko: Perceptron Learning of SAT. Neural Information Processing Systems (NIPS), 2012. [bibtex]
   
• Mittal, A., M. B. Blaschko, A. Zisserman, P. H. S. Torr: Taxonomic Multi-class Prediction and Person Layout using Efficient Structured Ranking. European Conference on Computer Vision (ECCV), 2012. [bibtex; code; doi]
   
• Blaschko, M. B. and C. H. Lampert: Guest Editorial: Special Issue on Structured Prediction and Inference. International Journal of Computer Vision (IJCV), 99(3):257-258, 2012. [bibtex; doi]
   
• Rahtu, E., J. Kannala, and M. B. Blaschko: Learning a Category Independent Object Detection Cascade. International Conference on Computer Vision (ICCV), 2011. [bibtex; code and data; doi]
   
• Vedaldi, A., M. B. Blaschko, and A. Zisserman: Learning Equivariant Structured Output SVM Regressors. International Conference on Computer Vision (ICCV), 2011. [bibtex; doi]
   
• Blaschko, M. B.: Branch and Bound Strategies for Non-maximal Suppression in Object Detection. International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2011. [bibtex; doi]
   
• Blaschko, M. B., J. A. Shelton, A. Bartels, C. H. Lampert and A. Gretton: Semi-supervised Kernel Canonical Correlation Analysis with Application to Human fMRI. Pattern Recognition Letters, 32(11):1572-1583, 2011. [bibtex; doi]
   
• Blaschko, M. B., A. Vedaldi and A. Zisserman: Simultaneous Object Detection and Ranking with Weak Supervision. Proceedings of the Twenty-Fourth Annual Conference on Neural Information Processing Systems (NIPS 2010) [bibtex]
   
• Shelton, J. A., M. B. Blaschko, A. Gretton, J. Müller, E. Fischer and A. Bartels, Similarities in Resting State and Feature-driven Activity: Non-parametric Evaluation of Human fMRI. NIPS'10 Workshop on Learning and Planning from Batch Time Series Data, 2010.[bibtex]
   
• Shelton, J. A., M. B. Blaschko, and A. Bartels: Augmentation of fMRI Data Analysis using Resting State Activity and Semi-supervised Canonical Correlation Analysis. Women in Machine Learning Workshop (WiML 2010), Vancouver, BC, Canada 2010. [bibtex]
   
• Tuytelaars, T., C. H. Lampert, M. B. Blaschko and W. Buntine: Unsupervised Object Discovery: A Comparison. International Journal of Computer Vision (IJCV), 88(2):284-302, 2010. [bibtex; code; doi]
   
• Blaschko, M. B., J. A. Shelton and A. Bartels: Augmenting Feature-driven fMRI Analyses: Semi-supervised Learning and Resting State Activity. Proceedings of the Twenty-Third Annual Conference on Neural Information Processing Systems (NIPS 2009) [bibtex]
   
• Blaschko, M. B. and C. H. Lampert: Object Localization with Global and Local Context Kernels. British Machine Vision Conference (BMVC), 2009. [bibtex; video; doi]
   
• Lampert, C. H., M. B. Blaschko and T. Hofmann: Efficient Subwindow Search: A Branch and Bound Framework for Object Localization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(12):2129-2142, 2009. [bibtex; code; doi]
   
• Shelton, J. A., M. B. Blaschko, C. H. Lampert and A. Bartels: Semi-supervised Analysis of Human fMRI. Berlin Brain-Computer Interface Workshop (BBCI), 2009. [bibtex]
   
• Shelton, J., M. B. Blaschko and A. Bartels: Semi-supervised Subspace Analysis of Human Functional Magnetic Resonance Imaging Data. Max Planck Institute for Biological Cybernetics Tech Report (185) (05 2009) [bibtex]
   
• Lampert, C. H. and M. B. Blaschko: Structured Prediction by Joint Kernel Support Estimation. Machine Learning, 77(2-3):249-269, 2009. [bibtex; doi]
   
• Blaschko, M. B.: Kernel Methods in Computer Vision: Object Localization, Clustering, and Taxonomy Discovery. Doctoral Thesis, Max Planck Institute for Biological Cybernetics, Awarded by the Technische Universität Berlin, 2009. [bibtex; doi]
   
• Blaschko, M. B. and A. Gretton: Learning Taxonomies by Dependence Maximization. Proceedings of the Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008), 1-8. (Eds.) Koller, D., D. Schuurmans, Y. Bengio, L. Bottou (01 2009) [bibtex]
   
• Lampert, C. H. and M. Blaschko: Joint Kernel Support Estimation for Structured Prediction. NIPS 2008 Workshop on "Structured Input - Structured Output" 2008, 76 (12 2008) [bibtex]
   
• Blaschko, M. B. and A. Gretton: Taxonomy Inference Using Kernel Dependence Measures. Max Planck Institute for Biological Cybernetics Tech Report (181) (11 2008) [bibtex]
   
• Blaschko, M. B. and C. H. Lampert: Learning to Localize Objects with Structured Output Regression. Computer Vision: ECCV 2008, 2-15. (Eds.) Forsyth, D. A., P. H.S. Torr, A. Zisserman, Springer, Berlin, Germany (10 2008) [Note: Best Student Paper Award; bibtex; code; doi]
   
• Blaschko, M. B., C. H. Lampert and A. Gretton: Semi-Supervised Laplacian Regularization of Kernel Canonical Correlation Analysis. Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008, 133-145. (Eds.) Daelemans, W., B. Goethals, K. Morik, Springer, Berlin, Germany (08 2008) [bibtex; doi]
   
• Blaschko, M. B. and A. Gretton: A Hilbert-Schmidt Dependence Maximization Approach to Unsupervised Structure Discovery. Proceedings of the 6th International Workshop on Mining and Learning with Graphs (MLG 2008), 1-3 (07 2008) [bibtex]
   
• Blaschko, M. B. and C. H. Lampert: Correlational Spectral Clustering. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 1-8, IEEE Computer Society, Los Alamitos, CA, USA (06 2008) [bibtex; code; doi]
   
• Lampert, C. H., M. B. Blaschko and T. Hofmann: Beyond Sliding Windows: Object Localization by Efficient Subwindow Search. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 1-8, IEEE Computer Society, Los Alamitos, CA, USA (06 2008) [Note: Best paper award; bibtex; code; doi]
   
• Lampert, C. and M. B. Blaschko: A Multiple Kernel Learning Approach to Joint Multi-Class Object Detection. Pattern Recognition: Proceedings of the 30th DAGM Symposium, 31-40. (Eds.) Rigoll, G. Springer, Berlin, Germany (06 2008) [Note: Main Award DAGM 2008; bibtex; doi]
   
• Blaschko, M. B., T. Hofmann and C. H. Lampert: Efficient Subwindow Search for Object Localization. Max Planck Institute for Biological Cybernetics Tech Report (164) (08 2007) [bibtex]
   
• Blaschko, M. B. and T. Hofmann: Conformal Multi-Instance Kernels. NIPS 2006 Workshop on Learning to Compare Examples, 1-6 (12 2006) [bibtex]
   
• Lisin, D. A., M. A. Mattar, M. B. Blaschko, M. C. Benfield and E. G. Learned-Miller: Combining Local and Global Image Features for Object Class Recognition. Proceedings of IEEE Workshop on Learning in Computer Vision and Pattern Recognition (in conjunction with CVPR), 47 (06 2005) [bibtex; doi]
   
• Blaschko, M. B.: Support Vector Classification of Images with Local Features. M.S. Thesis, University of Massachusetts Amherst, Dept. of Computer Science (05 2005) [bibtex]
   
• Blaschko, M. B., G. Holness, M. A. Mattar, D. Lisin, P. E. Utgoff, A. R. Hanson, H. Schultz, E. M. Riseman, M. E. Sieracki, W. M. Balch and B. Tupper: Automatic In Situ Identification of Plankton. Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05), 79 - 86 (01 2005) [bibtex; doi]
   
• Shapiro, M. D. and M. B. Blaschko: Stability of Hausdorff-based Distance Measures. Visualization, Imaging, and Image Processing, 1-6 (09 2004) [bibtex]
   
• Shapiro, M. D. and M. B. Blaschko: On Hausdorff Distance Measures. University of Massachusetts Amherst, Computer Science Dept. Technical report (08 2004) [bibtex]