Research

Journal Publications

  1. Attinger D., Champod C. & De Brabanter K., Using the Likelihood Ratio in Bloodstain Pattern Analysis, Journal of Forensic Sciences, Vol. 67, No.1, p. 33-43, Jan. 2022, https://doi.org/10.1111/1556-4029.14899

  2. De Brabanter K. & Sabzikar F., Asymptotic theory for regression models with fractional local to unity root errors, Metrika, 84, 997–1024 2021. https://doi.org/10.1007/s00184-021-00812-7

  3. S. McCleary, E. Liscio, K. De Brabanter & D. Attinger, Automated Reconstruction of Cast-off Blood Spatter Patterns based on
    Euclidean Geometry and Statistical Likelihood, Forensic Science International, Vol. 319, 110628,  2021

  4. De Brabanter K & De Brabanter J., Robustness by Reweighting for Kernel Estimators: An Overview, Statistical Science, Vol. 36, No. 4, 578-594, 2021

  5. Liu Y. & De Brabanter K., Smoothed Nonparametric Derivative Estimation using Weighted Difference Quotients, Journal of Machine Learning Research, vol. 21, no. 65, p. 1−45, 2020

  6. Liu Y., Attinger D. & De Brabanter K., Letter to the Editor based on Comment to "Automatic Classification of Bloodstain Patterns Caused by Gunshot and Blunt Impact At Various Distances", Journal of Forensic Sciences, vol. 65, no. 4, p. 1386-1387, July 2020

  7. Basulto-Elias G., Carriquiry A., De Brabanter K. and Nordman D. Bivariate kernel deconvolution with panel data, Sankhya B, April 2020 (https://doi.org/10.1007/s13571-020-00226-x)

  8. Liu Y., Attinger D. & De Brabanter K., Automatic Classification of Bloodstain Patterns Caused by Gunshot and Blunt Impact At Various Distances, Journal of Forensic Sciences, vol 65, no 3, p. 729-743, May 2020 (top cited)

  9. Attinger D., Comiskey P., Yarin A. & De Brabanter K. Determining the region of origin of blood spatters considering fluid dynamics and statistical uncertainties, Forensic Science International, vol. 298, pp. 323-331, May 2019

  10. Attinger D., Liu Y., Faflack R., Rao Y., Struttman B.A., De Brabanter K., Comiskey P.M. & Yarin A.L., A data set of bloodstain patterns for teaching and research in bloodstain pattern analysis: gunshot backspatters, Data in Brief, vol. 22, p. 269-278, Feb. 2019

  11. K. De Brabanter, F. Cao, I. Gijbels & J. Opsomer, Local Polynomial Regression with Correlated Errors in Random Design and Unknown Correlation Structure, Biometrika, vol 105, no. 3, 681–690, 2018

  12. D. Attinger, Y. Liu, T. Bybee & K. De Brabanter, A data set of bloodstain patterns for teaching and research in bloodstain pattern analysis:  impact beating spatters, Data in Brief, vol. 18, p. 648-654, 2018

  13. G. Basulto-Elias, A. Carriquiry, K. De Brabanter and D.J. Nordman, ``fourierin'': An R package to compute Fourier integrals, R Journal, vol. 9, no. 2, 72-83, 2017

  14. A. Alhasan, D. White & K. De Brabanter, Wavelet Filter Design for Pavement Roughness Analysis,  Computer-Aided Civil and Infrastructure Engineering, vol. 31, p. 907-920, Nov. 2016

  15. A. Alhasan, D. White & K. De Brabanter, Continuous Wavelet Analysis of Pavement Profiles, Automation in Construction, p. 134-143, March 2016

  16. De Brabanter K, Liu Y. & Hua C., Convergence Rates for Uniform Confidence Intervals Based on Local Polynomial Regression Estimators, Journal of Nonparametric Statistics, vol. 28, no. 1, p. 31- 48, Feb. 2016

  17.  A. Alhasan, D. White & K. De Brabanter, Spatial Pavement Roughness from Stationary Laser Scanning, Journal of Pavement Engineering, p. 1-14, DOI: 10.1080/10298436.2015.1065403, 2015

  18. A. Alhasan, D. White & K. De Brabanter, Quantifying Unpaved Road Roughness from Terrestrial Laser Scanning, accepted for publication in Transportation Research Record: Journal of the Transportation Research Board, 2015

  19. Minta T., De Brabanter K., Suykens J.A.K., De Moor B., Predicting Breast Cancer Using an Expression Values Weighted Clinical Classifier, BMC Bioinformatics, vol. 15:6603, Dec. 2014

  20. Minta T., De Brabanter K., De Moor B., New Bandwidth Selection Criterion for Kernel PCA: Approach to Dimensionality Reduction and Classification Problems, BMC Bioinformatics, vol. 15:137, May 2014

  21. De Brabanter K., Suykens J.A.K., De Moor B., Nonparametric Regression via StatLSSVM, Journal of Statistical Software, vol. 55, no. 2, Oct. 2013, pp. 1-21

  22. De Brabanter K., De Brabanter J., Gijbels I., De Moor B., Derivative Estimation with Local Polynomial Fitting, Journal of Machine Learning Research, vol. 14, Jan. 2013, pp. 281-301

  23. Falck T., Dreesen P., De Brabanter K., Pelckmans K., De Moor B., Suykens J.A.K., Least-Squares Support Vector Machines for the Identification of Wiener-Hammerstein Systems, Control Engineering Practice, vol. 20, no. 11, Nov. 2012, pp. 1165-1174

  24. Sahhaf S., Degraeve R., Srividya V., De Brabanter K., Schram T., Gilbert M., Vandervorst W., Groeseneken G., HfSiO Bulk Trap Density Controls the Initial Vth in nMOSFETs, IEEE Transactions on Device and Materials Reliability, vol. 12, no. 2, June 2012, pp. 323-334

  25. Sahhaf S., De Brabanter K., Degraeve R., Suykens J.A.K., De Moor B., Groeseneken G., Modelling of Charge Trapping/De-trapping Induced Voltage Instability in High-k Gate Dielectrics, IEEE Transactions on Device and Materials Reliability, vol. 12, no. 1, Mar. 2012, pp. 152-157

  26. De Brabanter K., Karsmakers P., De Brabanter J., Suykens J.A.K., De Moor B., Confidence Bands for Least Squares Support Vector Machine Classifiers: A Regression Approach, Pattern Recognition, vol. 45, no. 6, Feb. 2012, pp. 2280-2287

  27. De Brabanter K., De Brabanter J., Suykens J.A.K., De Moor B., Kernel Regression in the Presence of Correlated Errors, Journal of Machine Learning Research, vol. 12, June 2011, pp. 1955-1976

  28. Karsmakers P., Pelckmans K., De Brabanter K., Van hamme H., Suykens J.A.K., Sparse Conjugate Directions Pursuit with Application to Fixed-size Kernel Models, Machine Learning, Special Issue on Model Selection and Optimization in Machine Learning, vol. 85, no. 1, Sept. 2011, pp 109-148

  29. De Brabanter K., De Brabanter J., Suykens J.A.K., De Moor B., Approximate Confidence and Prediction Intervals for Least Squares Support Vector Regression, IEEE Transactions on Neural Networks, vol. 22, no. 1, Jan. 2011, pp. 110-120

  30. Sahhaf S., Degraeve R., Cho M., De Brabanter K., Roussel Ph.J., Zahid M.B., Groeseneken G., Detailed Analysis of Charge Pumping and IdVg Hysteresis for Profiling Traps in SiO2/HfSiO(N), Microelectronic Engineering, vol. 87, no. 12, Dec. 2010, pp. 2614-2619

  31. De Brabanter K., De Brabanter J., Suykens J.A.K., De Moor B., Optimized Fixed-Size Kernel Models for Large Data Sets, Computational Statistics & Data Analysis, vol. 54, no. 6, Jun. 2010, pp. 1484-1504

 

Book Chapters

  1. Kris De Brabanter & Y. Liu, Smoothed Nonparametric Derivative Estimation Based on Weighted Difference Sequences, In Stochastic Models, Statistics and Their Applications, A. Steland, E. Rafajłowicz, K. Szajowski (Eds.), Chapter 4 (p. 31 - 38), Springer, February 2015

  2. Kris De Brabanter, Paola Gloria Ferrario & László Györfi. , Detecting ineffective features for nonparametric regression. In Regularization, Optimization, Kernels, and Support Vector Machines, Johan A.K. Suykens, Marco Signoretto, Andreas Argyriou, (eds), Chapter 8 (p. 177-194),  Chapman & Hall/CRC Machine Learning and Pattern Recognition Series, 2014

 

Conference proceedings

  1. M. Fili, K. De Brabanter, L. BI & G. Hu. Prediction of New COVID-19 Cases Considering Mitigation Policies and Weather Data for European Countries, INFORMS, pages 425-438, Dec. 2022.

  2. Yu Liu & Kris De Brabanter. Derivative Estimation in Random Design. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds), Advances in Neural Information Processing Systems 31, pages 3449–3458, Montreal, Canada, 2018.

  3. Passe, U., Anderson, N., De Brabanter, K., Dorneich, M., Krejci, C., Poplin, A., Shenk, L., Methodologies for Studying Human-Microclimate Interactions for Resilient, Smart City Decision-Making, in: Proceedings of PLEA 2016 Los Angeles - Cities, Buildings, People: Towards Regenerative Environments, 11-13 July 2016

  4. Alhasan A., White D., De Brabanter K., Quantifying Unpaved Road Roughness from Terrestrial Laser Scanning, Transportation Research Board, 94th Annual Meeting, Washington D.C., 2015

  5. De Brabanter K., Györfi L., Feature Selection via Detecting Ineffective Features, ROKS 2013, Heverlee, Belgium

  6. De Brabanter K. & De Moor B., Deconvolution in Nonparametric Statistics, in Proc. of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Brugge, Belgium, Apr. 2012, pp. 341-350

  7. De Brabanter K., De Brabanter J., Suykens J.A.K., Vandewalle J., De Moor B., Robustness in Kernel Based Regression: Influence and Weight Functions, IEEE World Congress on Computational Intelligence (IEEE WCCI/IJCNN 2012), pp. 3387-3394, Brisbane, Australia, June 2012.

  8. De Brabanter K., De Brabanter J., De Moor B., Nonparametric Derivative Estimation, in Proc. of the 23rd Benelux Conference on Artificial Intelligence (BNAIC), Gent, Belgium, pp. 75-81, Nov. 2011

  9. Lopez J., De Brabanter K., Dorronsoro J.R., Suykens J.A.K, Sparse LS-SVMs with L0-Norm Minimization, 2010, in Proc. of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, Apr. 2011, pp. 189-194

  10. De Brabanter K., Karsmakers P., De Brabanter J., Pelckmans K., Suykens J.A.K., De Moor B., On Robustness in Kernel Based Regression, in NIPS 2010 Workshop Robust Statistical Learning, Whistler, Canada, Dec. 2010

  11. Huyck B., De Brabanter K., Logist F., De Brabanter J., Van Impe J., De Moor B., Identification of a Pilot Scale Distillation Column: A Kernel Based Approach, in Proc. of the 18th World Congress of the International Federation of Automatic Control (IFAC), Milan, Italy, Sep. 2011, pp. 471-476

  12. De Brabanter K., Sahhaf S., Karsmakers P., De Brabanter J., Suykens J.A.K., De Moor B., Nonparametric Comparison of Densities Based on Statistical Bootstrap, in Proc. of the Fourth European Conference on the Use of Modern Information and Communication Technologies (ECUMICT), Gent, Belgium, Mar. 2010, pp. 179-190

  13. De Brabanter K., De Brabanter J., Suykens J.A.K., De Moor B., Kernel Regression with Correlated Errors, in Proc. of the 11th International Symposium on Computer Applications in Biotechnology (CAB), Leuven, Belgium, Jul. 2010, pp. 13-18

  14. De Brabanter K., Pelckmans K., De Brabanter J., Debruyne M., Suykens J.A.K., Hubert M., De Moor B., Robustness of Kernel Based Regression: a Comparison of Iterative Weighting Schemes, in Proc. of the 19th International Conference on Artificial Neural Networks (ICANN), Limassol, Cyprus, Sep. 2009, pp. 100-110

  15. De Brabanter K., Dreesen P., Karsmakers P., Pelckmans K., De Brabanter J., Suykens J.A.K., De Moor B., Fixed-Size LS-SVM Applied to the Wiener-Hammerstein Benchmark, in Proc. of the 15th IFAC Symposium on System Identification (SYSID 2009), Saint-Malo, France, Jul. 2009, pp. 826-831

 

Book of abstracts

  1. Suykens J.A.K., Argyriou A., De Brabanter K., Diehl M., Pelckmans K., Signoretto M., Van Belle V., Vandewalle J., (eds.), International workshop on advances in Regularization, Optimization, Kernel Methods and Support Vector Machines: theory and applications (ROKS 2013), Book of Abstracts, KU Leuven (Leuven, Belgium), 2013, 128 p.

  2. De Brabanter K., De Brabanter J., Gijbels I., Van Impe J., Vandewalle J., (eds.) , Workshop on Modern Nonparametric Methods for Time Series, Reliability & Optimization, Book of Abstracts, KU Leuven (Leuven, Belgium), 2012, 28 p

 

Posters

  1. Friedberg I. , De Brabanter K., Hayes D., Jayashankar P., Kelly J., Kling C., Mueller D., Nettleton D., Rasmussen M., Sarkar S., Schnable P., Singh A., Srinivasan S., Singh A., Zhu Z., Cho K.-T., Sjolund L., Ganapathysubramanian B., Lawrence-Dill C., D3AI: Data Driven Discovery for Agricultural Innovation, ISMB 2016, Orlando, FL