Global Data Analysis and the Fragmentation Problem in Decision Tree Induction. - Ricardo Vilalta; Gunnar Blix; Larry A. Rendell (ECML, 1997)

Integrating Feature Construction with Multiple Classifiers in Decision Tree Induction. - Ricardo Vilalta; Larry A. Rendell (ICML, 1997)

On the Development of Inductive Learning Algorithms: Generating Flexible and Adaptable Concept Representations - Ricardo Vilalta (1998)

Operational Data Analysis: Improved Predictions Using Multi-computer Pattern Detection. - Ricardo Vilalta; Chidanand Apté; Sholom M. Weiss (DSOM, 2000)

A Quantification of Distance Bias Between Evaluation Metrics In Classification. - Ricardo Vilalta; Daniel Oblinger (ICML, 2000)

Rule Induction of Computer Events. - Ricardo Vilalta; Sheng Ma; Joseph L. Hellerstein (DSOM, 2001)

A Unified Framework for Evaluation Metrics in Classification Using Decision Trees. - Ricardo Vilalta; Mark Brodie; Daniel Oblinger; Irina Rish (ECML, 2001)

Predicting Rare Events In Temporal Domains. - Ricardo Vilalta; Sheng Ma (ICDM, 2002)

A Characterization of Difficult Problems in Classification. - Ricardo Vilalta; Youssef Drissi (ICMLA, 2002)

A Classification Approach for Prediction of Target Events in Temporal Sequences. - Carlotta Domeniconi; Chang-Shing Perng; Ricardo Vilalta; Sheng Ma (PKDD, 2002)

A Perspective View and Survey of Meta-Learning. - Ricardo Vilalta; Youssef Drissi (Artif. Intell. Rev., 2002)

Predictive algorithms in the management of computer systems. - Ricardo Vilalta; Chidanand Apté; Joseph L. Hellerstein; Sheng Ma; Sholom M. Weiss (IBM Syst. J., 2002)

A hierarchical approach to classification for systems with complex low-level interactions. - Ricardo Vilalta; Murali-Krishna Achari (ISIC, 2003)

A Decomposition of Classes via Clustering to Explain and Improve Naive Bayes. - Ricardo Vilalta; Irina Rish (ECML, 2003)

Class Decomposition via Clustering: A New Framework for Low-Variance Classifiers. - Ricardo Vilalta; Murali-Krishna Achari; Christoph F. Eick (ICDM, 2003)

Critical event prediction for proactive management in large-scale computer clusters. - Ramendra K. Sahoo; Adam J. Oliner; Irina Rish; Manish Gupta 002; José E. Moreira; Sheng Ma; Ricardo Vilalta; Anand Sivasubramaniam (KDD, 2003)

Evaluation Metrics in Classification: A Quantification of Distance-Bias. - Ricardo Vilalta; Daniel Oblinger (Comput. Intell., 2003)

Piece-Wise Model Fitting Using Local Data Patterns. - Ricardo Vilalta; Murali-Krishna Achari; Christoph F. Eick (ECAI, 2004)

Using Representative-Based Clustering for Nearest Neighbor Dataset Editing. - Christoph F. Eick; Nidal M. Zeidat; Ricardo Vilalta (ICDM, 2004)

A Quantification of Cluster Novelty with an Application to Martian Topography. - Ricardo Vilalta; Tomasz F. Stepinski; Murali-Krishna Achari; Francisco Ocegueda-Hernandez (PKDD, 2004)

Using Meta-Learning to Support Data Mining. - Ricardo Vilalta; Christophe G. Giraud-Carrier; Pavel Brazdil; Carlos Soares (Int. J. Comput. Sci. Appl., 2004)

Introduction to the Special Issue on Meta-Learning. - Christophe G. Giraud-Carrier; Ricardo Vilalta; Pavel Brazdil (Mach. Learn., 2004)

Meta-Learning. - Ricardo Vilalta; Christophe G. Giraud-Carrier; Pavel Brazdil (The Data Mining and Knowledge Discovery Handbook, 2005)

Adaptive Clustering: Obtaining Better Clusters Using Feedback and Past Experience. - Abraham Bagherjeiran; Christoph F. Eick; Chun-Sheng Chen; Ricardo Vilalta (ICDM, 2005)

Content-Based Image Retrieval through a Multi-Agent Meta-Learning Framework. - Abraham Bagherjeiran; Ricardo Vilalta; Christoph F. Eick (ICTAI, 2005)

Testing Theories in Particle Physics Using Maximum Likelihood and Adaptive Bin Allocation. - Bruce Knuteson; Ricardo Vilalta (PKDD, 2005)

Digital topography models for Martian surfaces. - Tomasz F. Stepinski; Ricardo Vilalta (IEEE Geosci. Remote. Sens. Lett., 2005)

Identifying and Characterizing Class Clusters to Explain Learning Performance. - Ricardo Vilalta (AAAI Spring Symposium: What Went Wrong and Why: Lessons from AI Research and Applications, 2006)

Automatic Recognition of Landforms on Mars Using Terrain Segmentation and Classification. - Tomasz F. Stepinski; Soumya Ghosh; Ricardo Vilalta (Discovery Science, 2006)

Using clustering to learn distance functions for supervised similarity assessment. - Christoph F. Eick; Alain Rouhana; Abraham Bagherjeiran; Ricardo Vilalta (Eng. Appl. Artif. Intell., 2006)

Machine Learning for Automatic Mapping of Planetary Surfaces. - Tomasz F. Stepinski; Soumya Ghosh; Ricardo Vilalta (AAAI, 2007)

Data Selection Using SASH Trees for Support Vector Machines. - Chaofan Sun; Ricardo Vilalta (MLDM, 2007)

Tablet PC video based hybrid coursework in computer science: report from a pilot project. - Jaspal Subhlok; Olin G. Johnson; Venkat Subramaniam; Ricardo Vilalta; Chang Yun (SIGCSE, 2007)

An efficient approach to external cluster assessment with an application to martian topography. - Ricardo Vilalta; Tomasz F. Stepinski; Murali-Krishna Achari (Data Min. Knowl. Discov., 2007)

Machine Learning Tools for Automatic Mapping of Martian Landforms. - Tomasz F. Stepinski; Ricardo Vilalta; Soumya Ghosh (IEEE Intell. Syst., 2007)

Metalearning - Applications to Data Mining. - Pavel Brazdil; Christophe G. Giraud-Carrier; Carlos Soares; Ricardo Vilalta (Springer, 2009)

A Framework for Multi-Objective Clustering and Its Application to Co-Location Mining. - Rachsuda Jiamthapthaksin; Christoph F. Eick; Ricardo Vilalta (ADMA, 2009)

Meta-Learning. - Christophe G. Giraud-Carrier; Pavel Brazdil; Carlos Soares; Ricardo Vilalta (Encyclopedia of Data Warehousing and Mining, 2009)

Cluster Validation. - Ricardo Vilalta; Tomasz F. Stepinski (Encyclopedia of Data Warehousing and Mining, 2009)

Automatic detection of craters in planetary images: an embedded framework using feature selection and boosting. - Wei Ding 003; Tomasz F. Stepinski; Lourenço P. C. Bandeira; Ricardo Vilalta; Youxi Wu; Zhenyu Lu; Tianyu Cao 001 (CIKM, 2010)

Learning and Meta-Learning for Coordination of Autonomous Unmanned Vehicles - A Preliminary Analysis. - Predrag T. Tosic; Ricardo Vilalta (ECAI, 2010)

A Conceptual Study of Model Selection in Classification - Multiple Local Models vs One Global Model. - Ricardo Vilalta; Francisco Ocegueda-Hernandez; C. Bagaria (ICAART (1), 2010)

A unified framework for reinforcement learning, co-learning and meta-learning how to coordinate in collaborative multi-agent systems. - Predrag T. Tosic; Ricardo Vilalta (ICCS, 2010)

Automatic Annotation of Planetary Surfaces With Geomorphic Labels. - Soumya Ghosh; Tomasz F. Stepinski; Ricardo Vilalta (IEEE Trans. Geosci. Remote. Sens., 2010)

Meta-Learning - Concepts and Techniques. - Ricardo Vilalta; Christophe G. Giraud-Carrier; Pavel Brazdil (Data Mining and Knowledge Discovery Handbook, 2010)

Metalearning. - Pavel Brazdil; Ricardo Vilalta; Christophe G. Giraud-Carrier; Carlos Soares (Encyclopedia of Machine Learning, 2010)

Inductive Transfer. - Ricardo Vilalta; Christophe G. Giraud-Carrier; Pavel Brazdil; Carlos Soares (Encyclopedia of Machine Learning, 2010)

Subkilometer crater discovery with boosting and transfer learning. - Wei Ding 003; Tomasz F. Stepinski; Yang Mu; Lourenço P. C. Bandeira; Ricardo Vilalta; Youxi Wu; Zhenyu Lu; Tianyu Cao 001; Xindong Wu 001 (ACM Trans. Intell. Syst. Technol., 2011)

Modeling repetitive patterns: A bridge between pattern theory and data mining. - Ricardo Vilalta; Luis Real (GrC, 2012)

An Empirical Study of the Suitability of Class Decomposition for Linear Models: When Does It Work Well? - Francisco Ocegueda-Hernandez; Ricardo Vilalta (SDM, 2013)

A machine learning approach to Cepheid variable star classification using data alignment and maximum likelihood. - Ricardo Vilalta; Kinjal Dhar Gupta; Lucas Macri (Astron. Comput., 2013)

A Data Complexity Approach to Kernel Selection for Support Vector Machines. - Roberto Valerio; Ricardo Vilalta (AAAI, 2014)

Fault resilient physical neural networks on a single chip. - Weidong Shi; Yuanfeng Wen; Ziyi Liu 002; Xi Zhao 001; Dainis Boumber; Ricardo Vilalta; Lei Xu 012 (CASES, 2014)

Domain Adaptation under Data Misalignment: An Application to Cepheid Variable Star Classification. - Ricardo Vilalta; Kinjal Dhar Gupta; Lucas Macri (ICPR, 2014)

Perinasal indicators of deceptive behavior. - Malcolm Dcosta; Dvijesh J. Shastri; Ricardo Vilalta; Judee K. Burgoon; Ioannis T. Pavlidis (FG, 2015)

Star Classification Under Data Variability: An Emerging Challenge in Astroinformatics. - Ricardo Vilalta; Kinjal Dhar Gupta; Ashish Mahabal (ECML/PKDD (3), 2015)

The overlooked potential of Generalized Linear Models in astronomy, I: Binomial regression. - Rafael S. de Souza; Ewan Cameron; Madhura Killedar; Joseph M. Hilbe; Ricardo Vilalta; Umberto Maio; Veronica Biffi; Benedetta Ciardi; Jamie D. Riggs (Astron. Comput., 2015)

Exploring the spectroscopic diversity of type Ia supernovae with Deep Learning and Unsupervised Clustering. - Emille E. O. Ishida; Michele Sasdelli; Ricardo Vilalta; Michel Aguena; Vinicius C. Busti; Hugo Camacho; Arlindo M. M. Trindade; Fabian Gieseke; Rafael S. de Souza; Yabebal T. Fantaye; Paolo A. Mazzali (Astroinformatics, 2016)

Automated supernova Ia classification using adaptive learning techniques. - Kinjal Dhar Gupta; Renuka Pampana; Ricardo Vilalta; Emille E. O. Ishida; Rafael S. de Souza (SSCI, 2016)

Analysis of correlation between pediatric asthma exacerbation and exposure to pollutant mixtures with association rule mining. - Giulia Toti; Ricardo Vilalta; Peggy Lindner; Barry Lefer; Charles Macias; Daniel Price (Artif. Intell. Medicine, 2016)

Disk failure prediction in heterogeneous environments. - Carlos A. Rincon C.; Jehan-François Pâris; Ricardo Vilalta; Albert M. K. Cheng; Darrell D. E. Long (SPECTS, 2017)

Photometric redshift estimation: An active learning approach. - Ricardo Vilalta; Emille E. O. Ishida; Róbert Beck; R. Sutrisno; Rafael S. de Souza; Ashish Mahabal (SSCI, 2017)

Metalearning. - Pavel Brazdil; Ricardo Vilalta; Christophe G. Giraud-Carrier; Carlos Soares (Encyclopedia of Machine Learning and Data Mining, 2017)

Inductive Transfer. - Ricardo Vilalta; Christophe G. Giraud-Carrier; Pavel Brazdil; Carlos Soares (Encyclopedia of Machine Learning and Data Mining, 2017)

Conceptual Domain Adaptation Using Deep Learning. - Behrang Mehrparvar; Ricardo Vilalta (CoRR, 2018)

A General Approach to Domain Adaptation with Applications in Astronomy. - Ricardo Vilalta; Kinjal Dhar Gupta; Dainis Boumber; Mikhail M. Meskhi (CoRR, 2018)

Transfer Learning in Astronomy: A New Machine-Learning Paradigm. - Ricardo Vilalta (CoRR, 2018)

Filter-Based Information-Theoretic Feature Selection. - Farinaz Pisheh; Ricardo Vilalta (ICAAI, 2019)

Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era. - Brian Nord; Andrew J. Connolly; Jamie Kinney; Jeremy Kubica; Gautaum Narayan; Joshua E. G. Peek; Chad Schafer; Erik J. Tollerud; Camille Avestruz; Gutti Jogesh Babu; Simon Birrer; Douglas Burke; et al. (CoRR, 2019)

Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients. - Noble Kennamer; Emille E. O. Ishida; Santiago Gonzalez-Gaitan; Rafael S. de Souza; Alexander Ihler; Kara Ponder; Ricardo Vilalta; Anais Möller; David O. Jones; Mi Dai; Alberto Krone-Martins; Bruno Quint; et al. (SSCI, 2020)

Learning Abstract Task Representations. - Mikhail M. Meskhi; Adriano Rivolli; Rafael G. Mantovani; Ricardo Vilalta (MetaDL@AAAI, 2021)

Applications and Techniques for Fast Machine Learning in Science. - Allison McCarn Deiana; Nhan Tran; Joshua Agar; Michaela Blott; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Scott Hauck; Mia Liu; Mark S. Neubauer; Jennifer Ngadiuba; Seda Ogrenci Memik; et al. (CoRR, 2021)

Applications and Techniques for Fast Machine Learning in Science. - Allison McCarn Deiana; Nhan Tran; Joshua Agar; Michaela Blott; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Scott Hauck; Mia Liu; Mark S. Neubauer; Jennifer Ngadiuba; Seda Ogrenci Memik; et al. (Frontiers Big Data, 2022)

Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter. - Zhenyu Dai; Ben Moews; Ricardo Vilalta; Romeel Dave (CoRR, 2023)

Meta-learning Loss Functions of Parametric Partial Differential Equations Using Physics-Informed Neural Networks. - Michail Koumpanakis; Ricardo Vilalta (DS (1), 2024)

Corrigendum: Applications and techniques for fast machine learning in science. - Allison McCarn Deiana; Nhan Tran; Joshua Agar; Michaela Blott; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Scott Hauck; Mia Liu; Mark S. Neubauer; Jennifer Ngadiuba; Seda Ogrenci-Memik; et al. (Frontiers Big Data, 2024)

Robust errant beam prognostics with conditional modeling for particle accelerators. - Kishansingh Rajput; Malachi Schram; Willem Blokland; Yasir Alanazi; Pradeep Ramuhalli; Alexander Zhukov; Charles Peters; Ricardo Vilalta (Mach. Learn. Sci. Technol., 2024)