Ricardo Vilalta

According to our database, Ricardo Vilalta authored at least 76 papers between 1997 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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., March, 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, Maurizio Pierini, Thea Aarrestad, Steffen Bähr, Jürgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomás E. Müller-Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Dongning Guo, Kyle J. Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belinavon Krosigk, Shen Wang, Thomas K. Warburton

Frontiers Big Data, 2024

2023

Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter.

Zhenyu Dai, Ben Moews, Ricardo Vilalta, Romeel Dave

CoRR, 2023

2022

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, Maurizio Pierini, Thea Aarrestad, Steffen Bähr, Jürgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomás E. Müller-Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Dongning Guo, Kyle J. Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belinavon Krosigk, Shen Wang, Thomas K. Warburton

Frontiers Big Data, 2022

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, Maurizio Pierini, Thea Aarrestad, Steffen Bähr, Jürgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomás E. Müller-Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Kyle J. Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belinavon Krosigk, Thomas K. Warburton, Maria Acosta Flechas, Anthony Aportela, Thomas Calvet, Leonardo Cristella, Daniel Diaz, Caterina Doglioni, Maria Domenica Galati, Elham E Khoda, Farah Fahim, Davide Giri, Benjamin Hawks, Duc Hoang, Burt Holzman, Shih-Chieh Hsu, Sergo Jindariani, Iris Johnson, Raghav Kansal, Ryan Kastner, Erik Katsavounidis, Jeffrey D. Krupa, Pan Li, Sandeep Madireddy, Ethan Marx, Patrick McCormack, Andres Meza, Jovan Mitrevski, Mohammed Attia Mohammed, Farouk Mokhtar, Eric A. Moreno, Srishti Nagu, Rohin Narayan, Noah Palladino, Zhiqiang Que, Sang Eon Park, Subramanian Ramamoorthy, Dylan S. Rankin, Simon Rothman, Ashish Sharma, Sioni Summers, Pietro Vischia, Jean-Roch Vlimant, Olivia Weng

CoRR, 2021

Learning Abstract Task Representations.

Mikhail M. Meskhi, Adriano Rivolli, Rafael G. Mantovani, Ricardo Vilalta

Proceedings of the AAAI Workshop on Meta-Learning and MetaDL Challenge, 2021

2020

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, Sreevarsha Sreejith, Alex I. Malz, Lluís Galbany

Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, 2020

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, João Caldeira, Douglas A. Caldwell, Joleen K. Carlberg, Yen-Chi Chen, Chuanfei Dong, Eric D. Feigelson, V. Zach Golkhou, Vinay Kashyap, T. S. Li, Thomas Loredo, Luisa Lucie-Smith, Kaisey S. Mandel, J. R. Martínez-Galarza, Adam A. Miller, Priyamvada Natarajan, Michelle Ntampaka, Andy Ptak, David Rapetti, Lior Shamir, Aneta Siemiginowska, Brigitta M. Sipocz, Arfon M. Smith, Nhan Tran, Ricardo Vilalta, Lucianne M. Walkowicz, John ZuHone

CoRR, 2019

Filter-Based Information-Theoretic Feature Selection.

Farinaz Pisheh, Ricardo Vilalta

Proceedings of the 3rd International Conference on Advances in Artificial Intelligence, 2019

2018

Transfer Learning in Astronomy: A New Machine-Learning Paradigm.

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

Conceptual Domain Adaptation Using Deep Learning.

Behrang Mehrparvar, Ricardo Vilalta

CoRR, 2018

2017

Inductive Transfer.

Ricardo Vilalta, Christophe G. Giraud-Carrier, Pavel Brazdil, Carlos Soares

Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Metalearning.

Pavel Brazdil, Ricardo Vilalta, Christophe G. Giraud-Carrier, Carlos Soares

Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Photometric redshift estimation: An active learning approach.

Ricardo Vilalta, Emille E. O. Ishida, Róbert Beck, R. Sutrisno, Rafael S. de Souza, Ashish Mahabal

Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

Disk failure prediction in heterogeneous environments.

Carlos A. Rincon C., Jehan-François Pâris, Ricardo Vilalta, Albert M. K. Cheng, Darrell D. E. Long

Proceedings of the International Symposium on Performance Evaluation of Computer and Telecommunication Systems, 2017

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

Automated supernova Ia classification using adaptive learning techniques.

Kinjal Dhar Gupta, Renuka Pampana, Ricardo Vilalta, Emille E. O. Ishida, Rafael S. de Souza

Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence, 2016

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

Proceedings of the Astroinformatics 2016, Sorrento, Italy, October 19-25, 2016, 2016

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

Star Classification Under Data Variability: An Emerging Challenge in Astroinformatics.

Ricardo Vilalta, Kinjal Dhar Gupta, Ashish Mahabal

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Perinasal indicators of deceptive behavior.

Malcolm Dcosta, Dvijesh J. Shastri, Ricardo Vilalta, Judee K. Burgoon, Ioannis T. Pavlidis

Proceedings of the 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, 2015

2014

Domain Adaptation under Data Misalignment: An Application to Cepheid Variable Star Classification.

Ricardo Vilalta, Kinjal Dhar Gupta, Lucas Macri

Proceedings of the 22nd International Conference on Pattern Recognition, 2014

Fault resilient physical neural networks on a single chip.

Weidong Shi, Yuanfeng Wen, Ziyi Liu, Xi Zhao, Dainis Boumber, Ricardo Vilalta, Lei Xu

Proceedings of the 2014 International Conference on Compilers, 2014

A Data Complexity Approach to Kernel Selection for Support Vector Machines.

Roberto Valerio, Ricardo Vilalta

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

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

An Empirical Study of the Suitability of Class Decomposition for Linear Models: When Does It Work Well?

Francisco Ocegueda-Hernandez, Ricardo Vilalta

Proceedings of the 13th SIAM International Conference on Data Mining, 2013

2012

Modeling repetitive patterns: A bridge between pattern theory and data mining.

Ricardo Vilalta, Luis Real

Proceedings of the 2012 IEEE International Conference on Granular Computing, 2012

2011

Subkilometer crater discovery with boosting and transfer learning.

Wei Ding, Tomasz F. Stepinski, Yang Mu, Lourenço P. C. Bandeira, Ricardo Vilalta, Youxi Wu, Zhenyu Lu, Tianyu Cao, Xindong Wu

ACM Trans. Intell. Syst. Technol., 2011

2010

Inductive Transfer.

Ricardo Vilalta, Christophe G. Giraud-Carrier, Pavel Brazdil, Carlos Soares

Proceedings of the Encyclopedia of Machine Learning, 2010

Metalearning.

Pavel Brazdil, Ricardo Vilalta, Christophe G. Giraud-Carrier, Carlos Soares

Proceedings of the Encyclopedia of Machine Learning, 2010

Meta-Learning - Concepts and Techniques.

Ricardo Vilalta, Christophe G. Giraud-Carrier, Pavel Brazdil

Proceedings of the Data Mining and Knowledge Discovery Handbook, 2nd ed., 2010

Automatic Annotation of Planetary Surfaces With Geomorphic Labels.

Soumya Ghosh, Tomasz F. Stepinski, Ricardo Vilalta

IEEE Trans. Geosci. Remote. Sens., 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

Proceedings of the International Conference on Computational Science, 2010

A Conceptual Study of Model Selection in Classification - Multiple Local Models vs One Global Model.

Ricardo Vilalta, Francisco Ocegueda-Hernandez, C. Bagaria

Proceedings of the ICAART 2010 - Proceedings of the International Conference on Agents and Artificial Intelligence, Volume 1, 2010

Learning and Meta-Learning for Coordination of Autonomous Unmanned Vehicles - A Preliminary Analysis.

Predrag T. Tosic, Ricardo Vilalta

Proceedings of the ECAI 2010, 2010

Automatic detection of craters in planetary images: an embedded framework using feature selection and boosting.

Wei Ding, Tomasz F. Stepinski, Lourenço P. C. Bandeira, Ricardo Vilalta, Youxi Wu, Zhenyu Lu, Tianyu Cao

Proceedings of the 19th ACM Conference on Information and Knowledge Management, 2010

2009

Cluster Validation.

Ricardo Vilalta, Tomasz F. Stepinski

Proceedings of the Encyclopedia of Data Warehousing and Mining, Second Edition (4 Volumes), 2009

Meta-Learning.

Christophe G. Giraud-Carrier, Pavel Brazdil, Carlos Soares, Ricardo Vilalta

Proceedings of the Encyclopedia of Data Warehousing and Mining, Second Edition (4 Volumes), 2009

A Framework for Multi-Objective Clustering and Its Application to Co-Location Mining.

Rachsuda Jiamthapthaksin, Christoph F. Eick, Ricardo Vilalta

Proceedings of the Advanced Data Mining and Applications, 5th International Conference, 2009

Metalearning - Applications to Data Mining.

Pavel Brazdil, Christophe G. Giraud-Carrier, Carlos Soares, Ricardo Vilalta

Cognitive Technologies, Springer, ISBN: 978-3-540-73262-4, 2009

2007

Machine Learning Tools for Automatic Mapping of Martian Landforms.

Tomasz F. Stepinski, Ricardo Vilalta, Soumya Ghosh

IEEE Intell. Syst., 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

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

Proceedings of the 38th SIGCSE Technical Symposium on Computer Science Education, 2007

Data Selection Using SASH Trees for Support Vector Machines.

Chaofan Sun, Ricardo Vilalta

Proceedings of the Machine Learning and Data Mining in Pattern Recognition, 2007

Machine Learning for Automatic Mapping of Planetary Surfaces.

Tomasz F. Stepinski, Soumya Ghosh, Ricardo Vilalta

Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 2007

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

Automatic Recognition of Landforms on Mars Using Terrain Segmentation and Classification.

Tomasz F. Stepinski, Soumya Ghosh, Ricardo Vilalta

Proceedings of the Discovery Science, 9th International Conference, 2006

Identifying and Characterizing Class Clusters to Explain Learning Performance.

Ricardo Vilalta

Proceedings of the What Went Wrong and Why: Lessons from AI Research and Applications, 2006

2005

Digital topography models for Martian surfaces.

Tomasz F. Stepinski, Ricardo Vilalta

IEEE Geosci. Remote. Sens. Lett., 2005

Testing Theories in Particle Physics Using Maximum Likelihood and Adaptive Bin Allocation.

Bruce Knuteson, Ricardo Vilalta

Proceedings of the Knowledge Discovery in Databases: PKDD 2005, 2005

Content-Based Image Retrieval through a Multi-Agent Meta-Learning Framework.

Abraham Bagherjeiran, Ricardo Vilalta, Christoph F. Eick

Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2005), 2005

Adaptive Clustering: Obtaining Better Clusters Using Feedback and Past Experience.

Abraham Bagherjeiran, Christoph F. Eick, Chun-Sheng Chen, Ricardo Vilalta

Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 2005

Meta-Learning.

Ricardo Vilalta, Christophe G. Giraud-Carrier, Pavel Brazdil

Proceedings of the Data Mining and Knowledge Discovery Handbook., 2005

2004

Introduction to the Special Issue on Meta-Learning.

Christophe G. Giraud-Carrier, Ricardo Vilalta, Pavel Brazdil

Mach. Learn., 2004

Using Meta-Learning to Support Data Mining.

Ricardo Vilalta, Christophe G. Giraud-Carrier, Pavel Brazdil, Carlos Soares

Int. J. Comput. Sci. Appl., 2004

A Quantification of Cluster Novelty with an Application to Martian Topography.

Ricardo Vilalta, Tomasz F. Stepinski, Murali-Krishna Achari, Francisco Ocegueda-Hernandez

Proceedings of the Knowledge Discovery in Databases: PKDD 2004, 2004

Using Representative-Based Clustering for Nearest Neighbor Dataset Editing.

Christoph F. Eick, Nidal M. Zeidat, Ricardo Vilalta

Proceedings of the 4th IEEE International Conference on Data Mining (ICDM 2004), 2004

Piece-Wise Model Fitting Using Local Data Patterns.

Ricardo Vilalta, Murali-Krishna Achari, Christoph F. Eick

Proceedings of the 16th Eureopean Conference on Artificial Intelligence, 2004

2003

Evaluation Metrics in Classification: A Quantification of Distance-Bias.

Ricardo Vilalta, Daniel Oblinger

Comput. Intell., 2003

Critical event prediction for proactive management in large-scale computer clusters.

Ramendra K. Sahoo, Adam J. Oliner, Irina Rish, Manish Gupta, José E. Moreira, Sheng Ma, Ricardo Vilalta, Anand Sivasubramaniam

Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24, 2003

Class Decomposition via Clustering: A New Framework for Low-Variance Classifiers.

Ricardo Vilalta, Murali-Krishna Achari, Christoph F. Eick

Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM 2003), 2003

A Decomposition of Classes via Clustering to Explain and Improve Naive Bayes.

Ricardo Vilalta, Irina Rish

Proceedings of the Machine Learning: ECML 2003, 2003

A hierarchical approach to classification for systems with complex low-level interactions.

Ricardo Vilalta, Murali-Krishna Achari

Proceedings of the International Symposium on Intelligent Control, 2003

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 Perspective View and Survey of Meta-Learning.

Ricardo Vilalta, Youssef Drissi

Artif. Intell. Rev., 2002

A Classification Approach for Prediction of Target Events in Temporal Sequences.

Carlotta Domeniconi, Chang-Shing Perng, Ricardo Vilalta, Sheng Ma

Proceedings of the Principles of Data Mining and Knowledge Discovery, 2002

A Characterization of Difficult Problems in Classification.

Ricardo Vilalta, Youssef Drissi

Proceedings of the 2002 International Conference on Machine Learning and Applications, 2002

Predicting Rare Events In Temporal Domains.

Ricardo Vilalta, Sheng Ma

Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002), 2002

2001

A Unified Framework for Evaluation Metrics in Classification Using Decision Trees.

Ricardo Vilalta, Mark Brodie, Daniel Oblinger, Irina Rish

Proceedings of the Machine Learning: EMCL 2001, 2001

Rule Induction of Computer Events.

Ricardo Vilalta, Sheng Ma, Joseph L. Hellerstein

Proceedings of the Operations & Management, 2001

2000

A Quantification of Distance Bias Between Evaluation Metrics In Classification.

Ricardo Vilalta, Daniel Oblinger

Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29, 2000

Operational Data Analysis: Improved Predictions Using Multi-computer Pattern Detection.

Ricardo Vilalta, Chidanand Apté, Sholom M. Weiss

Proceedings of the Services Management in Intelligent Networks, 2000

1998

On the Development of Inductive Learning Algorithms: Generating Flexible and Adaptable Concept Representations

Ricardo Vilalta

PhD thesis, 1998

1997

Integrating Feature Construction with Multiple Classifiers in Decision Tree Induction.

Ricardo Vilalta, Larry A. Rendell

Proceedings of the Fourteenth International Conference on Machine Learning (ICML 1997), 1997

Global Data Analysis and the Fragmentation Problem in Decision Tree Induction.

Ricardo Vilalta, Gunnar Blix, Larry A. Rendell

Proceedings of the Machine Learning: ECML-97, 1997