Publications

Journal and conference papers; book chapters.

Dai, Zhenyu; Moews, Ben; Vilalta, Ricardo; Davé, Romeel (2024).
Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter. Monthly Notices of the Royal Astronomical Society, Volume 527, Issue 2, pages 3381–3394.

Rajput, Kishansingh; Schram, Malachi; Blokland, Willem; Alanazi, Yasir; Ramuhalli, Pradeep; Zhukov, Alexander; Peters, Charles; Vilalta, Ricardo (2024).
Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators.
Machine Learning: Science and Technology (open access).

Mroczek, D.; Hjorth-Jensen, M.; Noronha-Hostler, J.; Parotto, P.; Ratti, C.; Vilalta, R. (2023).
Mapping out the thermodynamic stability of a QCD equation of state with a critical point using active learning. Phys. Rev. C vol. 107, issue 5. DOI: 10.1103/PhysRevC.107.054911.

Wilkinson, J.; Salvatore, M.; Vilalta, R. (2023).
Megafans on Mars: a fluvial analogue for the Sinus Meridiani layered sediments. In Wilkinson, J., & Gunnell, Y. (Eds.). Fluvial Megafans on Earth and Mars. Cambridge: Cambridge University Press.

Meskhi, Mikhail M.; Wolfe, Noah E.; Dai, Zhenyu; Fröhlich, Carla; Miller, Jonah, M.; Wong, Raymond K. W.; Vilalta, Ricardo (2022).
A New Constraint on the Nuclear Equation of State from Statistical Distributions of Compact Remnants of Supernovae. The Astrophysical Journal Letters, Volume 932, Number 1, American Astronomical Society. DOI: 10.3847/2041-8213/ac7054

Vilalta, Ricardo; Meskhi, Michael (2022).
Transfer of Knowledge Across Tasks. Book Chapter in Metalearning: Applications to Automated Machine Learning and Data Mining, edited by Brazdil, Pavel; van Rijn, Jan N.; Soares, Carlos Soares; Vanschoren, Joaquin; Springer International Publishing, Second Edition.

Deiana, A. M.; Tran, N.; Agar, J.; Blott, M.; Vilalta, R., et. al. (2022).
Applications and Techniques for Fast Machine Learning in Science. Frontiers in big data, 5, 787421. DOI: 10.3389/fdata.2022.787421

2022

2023

Meskhi, Mikhail M.; Rivolli, Adriano; Mantovani, Rafael G.; Vilalta, Ricardo (2021).
Learning Abstract Task Representations. Proceedings of Machine Learning Research, AAAI Workshop on Meta-Learning and MetaDL Challenge, Volume 140, pp. 127-136.

Aarrestad, T.; van Beekveld, M.; Bona, M.; Vilalta, R.; et. al. (2021).
The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider. SciPost Physics, Volume 12, Issue 1, pages 43 (FERMILAB-PUB-21-285-CMS).

2021

Ben Moews, Morgan A Schmitz, Andrew J Lawler, Joe Zuntz, Alex I Malz, Rafael S de Souza, Ricardo Vilalta, Alberto Krone-Martins, Emille E O Ishida (2020).

Ridges in the Dark Energy Survey for cosmic trough identification. Monthly Notices of the Royal Astronomical Society, Volume 500, Issue 1, pp. 859–870.

Noble Kennamer, Emille E. O. Ishida, Santiago Gonzalez-Gaitan, Rafael S. de Souza, Alexander Ihler, Kara Ponder, Ricardo Vilalta, Anais Moller, David O. Jones, Mi Dai, Alberto Krone-Martins, Bruno Quint, Sreevarsha Sreejith, Alex I. Malz, Lluis Galbany (2020). Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients. IEEE Symposium on Computational Intelligence and Data Mining (CIDM-20), Canberra, Australia.

M. van Beekveld, S. Caron, A. De Simone, A. Farbin, L. Hendriks, A. Jueid, A. Leinweber, J. Mamuzic, E. Merényi, A. Morandini, C. Nellist, S. Otten, M. Pierini, R. Ruiz de Austri, S. Sekmen, J. Schouwenberg, R. Vilalta, M. White (2020).
Model-Independent Signal Detection: A Challenge using Benchmark Monte Carlo Data and Machine Learning. Proceedings of the Les Houches: Physics at TEV Colliders New Physics Working Group Report.

R. Vilalta, Dhar Gupta K., Boumber, D. Meskhi, M. (2019).
A General Approach to Domain Adaptation with Applications in Astronomy. Publications of the Astronomical Society of the Pacific, 131:108008 (11pp).

Moews B., de Souza R., Ishida E., Malz A., Heneka C., Vilalta R., Zuntz J. (2019).
Stress testing the dark energy equation of state imprint on supernova data. Physical Review D 99 (12), 123529.

Bechtol K., Drlica-Wagner A., Vilalta R., et. al. (2019).
Dark Matter Science in the Era of LSST. Astro2020: Decadal Survey on Astronomy and Astrophysics, science white papers, no. 207; Bulletin of the American Astronomical Society, Vol. 51, Issue 3, id. 207.

Pisheh F., Vilalta R. (2019).
Filter-Based Information-Theoretic Feature Selection. Proceedings of the 3rd International Conference on Advances in Artificial Intelligence, Istanbul, Turkey.

Nord F., Connolly A., Kinney J., Kubica J., Vilalta R., et al. (2019).
Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era. arXiv preprint arXiv:1911.02479

Ishida E. E. O., Beck R., Gonzalez Gaitan, de Souza R., Krone-Martins A., Barrett J. W., Kennamer N., Vilalta R. Burgess J. M., Quint B., Vitorelli A. Z., Mahabal A., and Gangler E. (2018).
Optimizing Spectroscopic Follow-Up Strategies for Supernova Photometric Classification with Active Learning. Monthly Notices of the Royal Astronomical Society (MNRAS) Volume 483, Issue 1, pp. 2:18, Oxford University Press.

Sutrisno, R., Vilalta R., Renshaw A. (2018).
A Machine Learning Approach for Dark-Matter Particle Identification Under Extreme Class Imbalance. 28th Annual International Astronomical Data Analysis Software & Systems Conference (ADASS-18), Maryland, USA.

Mehrparvar B., Vilalta R. (2018).
Conceptual Domain Adaptation Using Deep Learning. arXiv:1808.05355v1.

De Souza R. S., Dantas M. L. L., Costa-Duarte M. V., Feigelson E. D., Killedar M., Lablanche P. Y., Vilalta R., Krone-Martins A., Beck R., Gieseke F., (2017).
A Probabilistic Approach to Emission-Line Galaxy Classification. Monthly Notices of the Royal Astronomical Society (MNRAS), 472, no. 3, pp. 2808-2822, Oxford University Press.

Vilalta, R., (2017).
Transfer Learning in Astronomy: A New Machine-learning Paradigm. 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT-17), Seattle, WA., USA

Vilalta R., Ishida E. E. O., Beck R., Sutrisno R., De Souza R. S., Mahabal A. (2017).
Photometric Redshift Estimation: An Active Learning Approach. IEEE Symposium on Computational Intelligence and Data Mining (CIDM-17), Hawaii, USA.

Rincon C. A., Paris J. F., Vilalta R., Cheng A. M. K., Long D. D. E., (2017).
Disk Failure Prediction in Heterogeneous Environments. International Symposium on Performance Evaluation of Computer and Telecommunication Systems, Seattle, WA, USA.

Sasdelli, M., Ishida, E. O., Vilalta, R., Aguena, M., Busti, V.C., Camacho, H., Trindade, A.M.M., Gieseke, F., Souza, R.S., Fantaye, Y.T., Mazzali, P.A. (2016).
Exploring the Spectroscopic Diversity of Type Ia Supernovae with DRACULA: A Machine Learning Approach. Monthly Notices of the Royal Astronomical Society (MNRAS), 461, no.2, pp. 2044-2059, Oxford University Press.

Toti, G., Vilalta, R., Lindnerd, P., Leferb, B., Maciase, C., Priced, D. (2016).
Analysis of Correlation Between Pediatric Asthma Exacerbation and Exposure to Pollutant Mixtures with Association Rule Mining. Artificial Intelligence in Medicine 74 pp. 44 to 52. Elsevier.

Vilalta, R., Giraud-Carrier, C., Brazdil, P., Soares, C. (2016).
Inductive Transfer. Encyclopedia of Machine Learning and Data Mining. Claude Sammut & Geoff Webb (Eds). Springer.

Brazdil, P., Vilalta, R., Giraud-Carrier, C., Soares, C. (2016).
Meta-Learning. Encyclopedia of Machine Learning and Data Mining. Claude Sammut & Geoff Webb (Eds). Springer.

Dhar Gupta K., Pampana R., Vilalta R., Ishida E. E. O., de Souza R. S. (2016).
Automated Supernova Ia Classification Using Adaptive Learning Techniques. IEEE Symposium on Computational Intelligence and Data Mining (CIDM-16), Athens, Greece.

Ishida E. O., Sasdelli, M., Vilalta, R. Aguena, M., Busti, V. C., Camacho, H., Trindade, A. M. M., Gieseke, F., de Souza, R. S., Fantaye, Y. T., Mazzali, P. A. (2016).
Exploring the Spectroscopic Diversity of Type Ia Supernovae with Deep Learning and Unsupervised Clustering. Astroinformatics, Proceedings of the IAU Symposium No. 325. M. Brescia, G. Djorgovski, E. Feigelson, G. Longo & S. Cavuoti, eds.

Toti E. G., Vilalta R., Lindner P., Price D. (2016).
Effect of the Definition of Non-Exposed Population in Risk Pattern Mining. SIAM Data Mining Conference, 5th Workshop on Data Mining in Medicine and Healthcare, Miami, Florida.

De Souza R. S., Cameron E., Killedar M., Hilbe J., Vilalta R., Maio U., Biffi V., Ciardi B., Riggs J. D. (2015).
The Overlooked Potential of Generalized Linear Models in Astronomy - I: Binomial Regression. Astronomy and Computing, Vol. 12, pp. 21-32, Elsevier.

Vilalta R., Dhar Gupta K., Mahabal A. (2015).
Star Classification Under Data Variability: An Emerging Challenge in Astroinformatics. European Conference on Machine Learning (ECML-15), Porto, Portugal.

Dvijesh S., Dcosta M., Vilalta R., Pavlidis I. (2015).
Perinasal Indicators of Deceptive Behavior. IEEE Conference on Automatic Face and Gesture Recognition (FG2015), Ljubljana, Slovenia.

Guillen P., Larrazabal G., Gonzalez G. Boumber D., Vilalta R. (2015).
Supervised Learning in Salt Body Detection. 85th Annual Meeting of the Society of Exploration Geophysicists, New Orleans, LA, USA.

Vilalta R., Dhar Gupta K., Macri L. (2014).
Domain Adaptation Under Data Misalignment: An Application to Cepheid Variable Star Classification. The 22nd International Conference on Pattern Recognition (ICPR-14), Stockholm, Sweden.

Valerio R., Vilalta R. (2014).
A Data Complexity Approach to Kernel Selection for Support Vector Machines (student abstract). The 28th AAAI Conference on Artificial Intelligence (AAAI-14), Quebec, Canada.

Valerio R., Vilalta R. (2014).
Kernel Selection in Support Vector Machines Using Gram-Matrix Properties. 27th International Conference on Advances in Neural Information Processing Systems (NIPS-14). Workshop on Modern Nonparametrics: Automating the Learning Pipeline. Quebec, Canada.

Dhar Gupta K., Vilalta R., Asadourian V., Macri L. (2014).
Adapting Predictive Models For Cepheid Variable Star Classification Using Linear Regression And Maximum Likelihood. Statistical Challenges in 21st Century Cosmology (SCCC-14), Lisbon, Portugal. A Symposium of the International Astronomical Union IAU.

Shi W., Wen Y., Liu Z., Zhao X., Boumber D., Vilalta R., Xu L. (2014).
Fault Resilient Physical Neural Networks on a Single Chip. International Conference on Compilers, Architectures, and Synthesis of Embedded Systems (CAES-14).

Vilalta R., Dhar Gupta K., Macri L. (2013).
A Machine Learning Approach to Cepheid Variable Star Classification Using Data Alignment and Maximum Likelihood. Astronomy and Computing Journal, Vol. 2, pp. 46-53. Elsevier. DOI: 10.1016/j.ascom.2013.07.002.

Hoang S., Vilalta R., Pinsky L., Kroupa M., Stoffle N., Idarraga J. (2013).
Data Analysis of Tracks of Heavy Ion Particles in Timepix Detectors. The 15th International Workshop on Advanced Computing and Analysis Techniques in Physics (ACAT-13), Beijing, China.

Ocegueda-Hernandez F., Vilalta R. (2013).
An Empirical Study of the Suitability of Class Decomposition for Linear Models: When Does It Work Well? 13th SIAM International Conference on Data Mining, Austin, TX, USA.

Vilalta R., Kuchibhotla S., Hoang S., Valerio R., Ocegueda-Hernandez F., Pinsky L. (2012).
Classification of Sources of Ionizing Radiation in Space Missions: A Machine Learning Approach. Journal of the European Space Agency, Acta Futura 5, pp. 111-119, DOI: 10.2420/AF05.2012.111.

Stepinski T., Wei D., Vilalta R. (2012).
Detecting Impact Craters in Planetary Images Using Machine Learning. Handbook of Intelligent Data Analysis for Real-Life Applications: Theory and Practice. IGI Global.

Hoang S., Pinsky L., Vilalta R. (2012).
LET Estimation of Heavy Ion Particles based on a Timepix-Based Si Detector. The International Conference on Computing in High Energy and Nuclear Physics (CHEP-12), New York, USA.

Vilalta R., Real L. (2012).
Modeling Repetitive Patterns: A Bridge Between Pattern Theory and Data Mining. IEEE International Conference on Granular Computing and International Symposium on Foundations and Frontiers of Data Mining (FFDM-12), Hangzhou, China.

Vilalta R. (2012).
Commentary: Morphological Image Analysis and Sunspot Classification. In Statistical Challenges in Modern Astronomy V. Springer.

Ding W., Stepinski T., Bandeira L., Vilalta R., Wu Y., Lu Z., Cao T. (2011).
Sub-Kilometer Crater Discovery with Boosting and Transfer Learning.Meta-Learning. ACM Transactions on Intelligent Systems and Technology. Volume 2, Issue 4.

Vilalta R., Kuchibhotla S., Ocegueda-Hernandez F., Hoang S., Pinsky L. (2011).
Machine Learning for Identification of Sources of Ionizing Radiation During Space Missions. Workshop on AI in Space: Intelligence Beyond Planet Earth. International Joint Conference on Artificial Intelligence (IJCAI-11), Barcelona, Spain.

Brazdil P., Vilalta R., Soares C., Giraud-Carrier C. (2011).
Metalearning. Encyclopedia of the Sciences of Learning. Springer.

Stepinski T., Vilalta R. (2010).
Machine Learning Tools for Geomorphic Mapping of Planetary Surfaces. In Machine Learning, Yagang Zhang, Editor, Publisher: In-Tech 2010, ISBN 978-953-307-033-9, pp. 251-266.

Brazdil P., Vilalta R., Giraud-Carrier C., and Soares C. (2010).
Metalearning. Encyclopedia of Machine Learning. Claude Sammut and Geoffrey I. Webb, Editors. Springer.

Vilalta R., Giraud-Carrier C., Brazdil P. and Soares C. (2010).
Inductive Transfer. Encyclopedia of Machine Learning. Claude Sammut and Geoffrey I. Webb, Editors. Springer.

McGarvey Kenny, Vilalta R., Samara Marilia, Michell Robert. (2010).
A Pattern Recognition System for the Automated Tracking and Classification of Meteors Using Digital Image Data. 20th Conference on Astronomical Data Analysis Software and Systems (ADASS-10), Boston, USA.

Ding W., Stepinski T., Bandeira L., Vilalta R., Wu Y., Lu Z., Cao T. (2010).
Robust Automatic Detection of Craters in Planetary Images. 19th ACM Conference on Information and Knowledge Management (CIKM-10), Toronto, Canada.

Vilalta R., Kuchibhotla S., Valerio R., Pinsky L. (2010).
Development of Pattern Recognition Software for Tracks of Ionizing Radiation In Medipix2-Based (TimePix) Pixel Detector Devices. 18th International Conference on Computing in High Energy and Nuclear Physics (CHEP-10). Taipei, Taiwan. Journal of Physics: Conference Series.

Vilalta R., Ocegueda-Hernandez F., Bagaria C. (2010).
A Conceptual Study of Model Selection in Classification: Multiple Local Models vs One Global Model. Second International Conference on Agents and Artificial Intelligence (ICAART-2010), Valencia, Spain.

Tosic P., Vilalta R. (2010).
A Unified Framework for Reinforcement Learning, Co-Learning and Meta-Learning: How to Coordinate in Collaborative Multi-Agent Systems. International Conference on Computational Science (ICCS 2010), Track on Cognitive Agents: Theory and Practice. Amsterdam, Netherlands.

Tosic P., Vilalta R. (2010).
Learning and Meta-Learning for Coordination of Autonomous Unmanned Vehicles: A Preliminary Analysis. 19th European Conference on Artificial Intelligence (ECAI-10) and Sixth Conference on Prestigious Applications of Intelligent Systems (PAIS-2010), Lisbon, Portugal. In Frontiers in Artificial Intelligence and Applications, Volume 215, pp. 163-168.

Ghosh S., Stepinski T., Vilalta R. (2010).
Automatic Annotation of Planetary Surfaces with Geomorphic Labels. IEEE Transactions on Geoscience and Remote Sensing. Volume 48, Issue 1.

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

Vilalta R., Valerio R., Ocegueda-Hernandez F., Watts G. (2009).
The Effect of the Fragmentation Problem in Decision Tree Learning Applied to the Search for Single Top Quark Production. 17th International Conference on Computing in High Energy and Nuclear Physics (CHEP-09). Prague, Czech Republic. Journal of Physics: Conference Series.

Jiamthapthaksin R., Eick C., Vilalta R. (2009).
A Framework for Multi-objective Clustering and its Application to Co-location Mining. International Conference on Advanced Data Mining and Applications (ADMA-09), Beijing, China.

Vilalta R., Stepinski T. (2008).
Cluster Validation. Encyclopedia of Data Warehousing and Data Mining, 2nd edition, J. Wang Ed.

Giraud-Carrier C., Brazdil P. and Soares C., Vilalta R. (2008).
Meta-learning. Encyclopedia of Data Warehousing and Data Mining, 2nd edition, J. Wang Ed.

Stepinski T., Vilalta R., Achari M. (2007).
Machine Learning Tools for Automatic Mapping of Martian Landforms. IIIE Intelligent Systems, 22(6).

Vilalta R., Stepinski T., Achari M. (2007).
An Efficient Approach to External Cluster Assessment with an Application to Martian Topography. Data Mining and Knowledge Discovery Journal. Vol. 14, pp. 1-23. Springer Netherlands.

Sun, C., Vilalta R. (2007).
Data Selection using SASH Trees for Support Vector Machines. 5th International Conference on Machine Learning and Data Mining (MLDM-07), Leipzig, Germany.

Ghosh S., Stepinski T., Vilalta R. (2007).
Automatic Mapping of Martian Landforms Using Segmentation-Based Classification. 38th Lunar and Planetary Science Conference. League City, Texas.

Stepinski T., Ghosh S., Vilalta R. (2007).
Machine Learning for Automatic Mapping of Planetary Surfaces. Nineteenth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-07).

Subhlok J., Johnson O., Subramaniam V., Vilalta R., Yun C. (2007).
Tablet PC Video based Hybrid Coursework in Computer Science: Report from a Pilot Project. 38th ACM Technical Symposium on Computer Science Education (SIGCSE-2007).

Eick C., Rouhana A., Bagherjeiran A., Vilalta R. (2006).
Using Clustering to Learn Distance Functions for Supervised Similarity Assessment. Journal of Engineering Applications of Artificial Intelligence. Vol. 19, Issue 4, pp. 395-401.

Vilalta R., Mutchler G., Taylor S., Knuteson B. (2006).
Automatic Signal Enhancement in Particle Physics using Multivariate Classification and Physical Constraints. Ninth Workshop on Mining Scientific and Engineering Datasets (MSD06), in conjunction with the Sixth SIAM International Conference on Data Mining, Bethesda, Maryland.

Stepinski T., Ghosh S., Vilalta R. (2006).
Automatic Recognition of Landforms on Mars Using Terrain Segmentation and Classification. Ninth International Conference on Discovery Science (DS-2006), Barcelona, Spain.

Vilalta R. (2006).
Identifying and Characterizing Class-Clusters to Explain Learning Performance. AAAI 2006 Spring Symposia: What Went Wrong and Why: Lessons from AI Research and Applications. Ed. Dan Shapiro. Stanford University, CA.

2005

Stepinski T., Vilalta R. (2005).
Digital Topography Models for Martian Surfaces. IEEE Geoscience and Remote Sensing Letters. Vol. 2 (3) pp. 260-264.

Vilalta R., Giraud-Carrier C., Brazdil P. (2005).
Meta-Learning: Concepts and Techniques. Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers. Oded Maimon and Lior Rokach, Editors. Springer Publishers.

Vilalta R., Sarda P., Mutchler G., Padley P. (2005).
Signal Enhancement Using Classification Techniques and Physical Constraints. Conference on Statistical Problems in Particle Physics, Astrophysics and Cosmology (PHYSTAT05), Oxford, U.K.

Bagherjeiran A., Eick C., Chen C., Vilalta R. (2005).
Adaptive Clustering: Obtaining Better Clusters Using Feedback and Past Experience. Fifth IEEE International Conference on Data Mining (ICDM05), New Orleans, Louisiana.

Knuteson B., Vilalta R. (2005).
Testing Theories in Particle Physics Using Maximum Likelihood and Adaptive Bin Allocation. 9th European Conference on Principles and Practices of Knowledge Discovery in Databases (PKDD05), Porto, Portugal.

Bagherjeiran A., Vilalta R. Eick C. (2005).
Content-Based Image Retrieval Through a Multi-Agent Meta-Learning Framework. 17th IEEE Conference on Tools with Artificial Intelligence (ICTAI05), Hong Kong.

Eick C., Rouhana A., Bagherjeiran A., Vilalta R. (2005).
Using Clustering to Learn Distance Functions for Supervised Similarity Assessment. International Conference on Machine Learning and Data Mining (MLDM05), Leipzig Germany.

2010

2009

2008

2007

2006

2004

Vilalta R., Giraud-Carrier C., Brazdil P. Soares C. (2004).
Using Meta-Learning to Support Data Mining. International Journal of Computer Science and Applications. Vol. 1, No. 1, pp. 31-45.

Vilalta R., Stepinski T., Achari M., Ocegueda-Hernandez F. (2004).
A Quantification of Cluster Novelty with an Application to Martian Topography. 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD04).

Giraud-Carrier C., Vilalta R., Brazdil P. (2004).
Introduction to the Special Issue on Meta-Learning. Machine Learning, Vol. 54, No. 3 pp. 187-193.

Eick C., Zeidat N., Vilalta R. (2004).
Using Representative-Based Clustering for Nearest Neighbor Dataset Editing. Fourth IEEE International Conference on Data Mining (ICDM04), Brighton U.K.

Vilalta R., Achari M., Eick C. (2004).
Piece-Wise Model Fitting Using Local Data Patterns. Sixteenth European Conference on Artificial Intelligence (ECAI04). Valencia, Spain.

Vilalta R., Stepinski T. (2004).
Thematic Maps of Martian Topography Generated by a Clustering Algorithm. 35th Lunar and Planetary Science Conference (LPSC04). League City, Texas.

2003

Vilalta R., Oblinger D. (2003).
Evaluation Metrics in Classification: A Quantification of Distance-Bias. Computational Intelligence, Vol. 29, No. 3, pp. 264-283.

Vilalta R., Achari M., Eick C. (2003).
Class Decomposition Via Clustering: A New Framework For Low-Variance Classifiers. Proceedings of the Third IEEE International Conference on Data Mining (ICDM03), Melbourne, Florida.

Vilalta R., Rish I. (2003).
A Decomposition Of Classes Via Clustering To Explain And Improve Naive Bayes. Proceedings of the European Conference on Machine Learning (ECML03). Cavtat-Dubrovnik, Croatia. (Best Paper Award).

Vilalta R., Achari M. (2003).
A Hierarchical Approach to Classification for Systems with Complex Low-level Interactions. Proceedings of the IEEE International Symposium on Intelligent Control (ISIC03), Houston, TX.

Vilalta R., Padley P., Lee S.J., Sun C., Bagherjeiran A. (2003).
Evaluating and Constructing Features For Identification of Tau Leptons. Proceedings of the Conference on Statistical Problems in Particle Physics, Astrophysics, and Cosmology (PHYSTAT03). Stanford CA.

Sahoo R. K., Oliner A. J., Rish I., Gupta M., Moreira J. E., Ma S., Vilalta R. (2003).
Critical Event Prediction for Proactive Management in Large-scale Computer Clusters. The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD03), Washington DC.

Stepinski T., Vilalta R., Achari M., McGovern P.J. (2003).
Algorithmic Classification of Drainage Networks on Mars and its Relation to Martial Geological Units. 34th Lunar and Planetary Science Conference (LPSC03). League City, Texas.

Vilalta R., Stepinski T. (2003).
Classification of Martian Terrain Using Automated Discovery of Structure Algorithm Applied to Digital Topography. Meeting of the American Geophysical Union (AGU03), San Francisco, CA.

2002

Vilalta R., Drissi Y. (2002).
A Perspective View and Survey Of Meta-Learning. Journal of Artificial Intelligence Review, 18, No. 2, pp. 77-95.

Vilalta R., Apte C., Hellerstein J., Ma S., Weiss S. (2002).
Predictive Algorithms in the Management of Computer Systems. IBM Systems Journal, Special Issue on Artificial Intelligence, Vol 41, No. 3.

Vilalta R., Ma Sheng (2002).
Predicting Rare Events in Temporal Domains. Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 02), Maebashi Japan.

Domeniconi C., Perng C., Vilalta R., Ma S. (2002).
A Classification Approach for Prediction of Target Events in Temporal Sequences. Proceedings of the 6th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'02), Helsinki, Finland.

Vilalta R., Drissi Y. (2002).
A Characterization of Difficult Problems in Classification. Proceedings of the International Conference on Machine Learning and Applications (ICMLA02), Las Vegas, Nevada.

Sahoo R., Bae M., Vilalta R., Moreira J., Ma S., Gupta M. (2002).
Providing Persistent and Consistent Resources Through Event Log Analysis and Prediction for Large-Scale Computing Systems. Workshop on Self-Healing, Adaptive, and Self-Managed Systems as part of the 16th Annual ACM International Conference on Supercomputing.

2001

Vilalta R., Brodie M., Oblinger D., Rish I. (2001).
A Unified Framework For Evaluation Metrics In Classification Using Decision Trees. Proceedings of the 12th European Conference on Machine Learning (ECML01), Freiburg, Germany.

Vilalta R., Ma S., Hellerstein J. (2001).
Rule Induction of Computer Events. Proceedings of the 12th IFIP/IEEE International Workshop on Distributed Systems: Operations & Management (DSOM01), Nancy, France.

Vilalta R., Drissi Y. (2001).
Research Directions in Meta-Learning. Proceedings of the International Conference on Artificial Intelligence, (ICAI01) Las Vegas, Nevada. Ed. H. R. Arabnia.

2000

Vilalta R., Oblinger D. (2000).
A Quantification Of Distance-Bias Between Evaluation Metrics In Classification. Proceedings of the 17th International Conference on Machine Learning (ICML00). Stanford University, Stanford, CA., pp.1087-1094.

Vilalta R., Rish I., Oblinger D. (2000).
What Works Well Where in Inductive Learning? Workshop during the 17th International Conference on Machine Learning (ICML00). Stanford University, Stanford, CA., pp.1087-1094.

Vilalta R., Apte C., Weiss S. (2000).
Operational Data Analysis: Improved Predictions Using Multi-Computer Pattern Detection. Proceedings of the International Workshop on Distributed Systems: Operations & Management (DSOM00), Austin, Texas.

1999

1997

Vilalta R. (1999).
Understanding Accuracy Performance Through Concept Characterization And Algorithm Analysis. Workshop on Recent Advances in Meta-Learning and Future Work, 16th International Conference on Machine Learning, Bled Slovenia. Edited by Christophe Giraud-Carrier and Bernhard Pfahringer. pp 3-9.

1996

Vilalta R., Rendell L. (1997).
Integrating Feature Construction with Multiple Classifiers in Decision Tree Induction. Proceedings of the 14th International Conference on Machine Learning (ICML97). Vanderbilt University, Nashville, TN. Morgan Kaufman Publishers, San Francisco, CA. pp 394-402.

Vilalta R., Rendell L. (1997).
Global Data Analysis and the Fragmentation Problem in Decision Tree Induction. Proceedings of the 9th European Conference on Machine Learning (ECML97). Prague, Czsech Republic. LNAI, Vol. XXX, pp. 312-326, Springer-Verlag.

Perez E., Vilalta R., Rendell L. (1996).
On the Importance of Change of Representation in Induction (Invited talk). Presented at the Workshop of Inductive Learning for the 1996 Canadian Conference on Artificial Intelligence.