Ricardo Vilalta

Publication Activity (10 Years)

Years Active: 1997-2024
Publications (10 Years): 20

Top Topics

Failure Prediction

Unsupervised Clustering

Domain Adaptation

Top Venues

Frontiers Big Data

Artif. Intell. Medicine

Publications

Kishansingh Rajput, Malachi Schram, Willem Blokland, Yasir Alanazi, Pradeep Ramuhalli, Alexander Zhukov, Charles Peters, Ricardo Vilalta
Robust errant beam prognostics with conditional modeling for particle accelerators. Mach. Learn. Sci. Technol. 5 (1) (2024)
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
Corrigendum: Applications and techniques for fast machine learning in science. Frontiers Big Data 6 (2024)
Zhenyu Dai, Ben Moews, Ricardo Vilalta, Romeel Dave
Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter. CoRR (2023)
Kishansingh Rajput, Malachi Schram, Willem Blokland, Yasir Alanazi, Pradeep Ramuhalli, Alexander Zhukov, Charles Peters, Ricardo Vilalta
Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators. CoRR (2023)
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
Applications and Techniques for Fast Machine Learning in Science. Frontiers Big Data 5 (2022)
Mikhail M. Meskhi, Adriano Rivolli, Rafael G. Mantovani, Ricardo Vilalta
Learning Abstract Task Representations. MetaDL@AAAI (2021)
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, Tomas E. Muller 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 Krupa, Pan Li, Sandeep Madireddy, Ethan Marx, Patrick McCormack, Andres Meza, Jovan Mitrevski, Mohammed Attia Mohammed, Farouk Mokhtar, Eric Moreno, Srishti Nagu, Rohin Narayan, Noah Palladino, Zhiqiang Que, Sang Eon Park, Subramanian Ramamoorthy, Dylan Rankin, Simon Rothman, Ashish Sharma, Sioni Summers, Pietro Vischia, Jean-Roch Vlimant, Olivia Weng
Applications and Techniques for Fast Machine Learning in Science. CoRR (2021)
Mikhail M. Meskhi, Adriano Rivolli, Rafael G. Mantovani, Ricardo Vilalta
Learning Abstract Task Representations. CoRR (2021)
Noble Kennamer, Emille E. O. Ishida, Santiago Gonzalez-Gaitan, Rafael S. de Souza, Alexander T. Ihler, Kara Ponder, Ricardo Vilalta, Anais Möller, David O. Jones, Mi Dai, Alberto Krone-Martins, Bruno Quint, Sreevarsha Sreejith, Alex I. Malz, Lluís Galbany
Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients. CoRR (2020)
Noble Kennamer, Emille E. O. Ishida, Santiago Gonzalez-Gaitan, Rafael S. de Souza, Alexander T. Ihler, Kara Ponder, Ricardo Vilalta, Anais Möller, David O. Jones, Mi Dai, Alberto Krone-Martins, Bruno Quint, Sreevarsha Sreejith, Alex I. Malz, Lluís Galbany
Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients. SSCI (2020)
Farinaz Pisheh, Ricardo Vilalta
Filter-Based Information-Theoretic Feature Selection. ICAAI (2019)
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
Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era. CoRR (2019)
Ricardo Vilalta, Kinjal Dhar Gupta, Dainis Boumber, Mikhail M. Meskhi
A General Approach to Domain Adaptation with Applications in Astronomy. CoRR (2018)
Ricardo Vilalta
Transfer Learning in Astronomy: A New Machine-Learning Paradigm. CoRR (2018)
Behrang Mehrparvar, Ricardo Vilalta
Conceptual Domain Adaptation Using Deep Learning. CoRR (2018)
Carlos A. Rincon C., Jehan-François Pâris, Ricardo Vilalta, Albert M. K. Cheng, Darrell D. E. Long
Disk failure prediction in heterogeneous environments. SPECTS (2017)
Ricardo Vilalta, Emille E. O. Ishida, R. Beck, R. Sutrisno, Rafael S. de Souza, Ashish Mahabal
Photometric redshift estimation: An active learning approach. SSCI (2017)
Giulia Toti, Ricardo Vilalta, Peggy Lindner, Barry Lefer, Charles Macias, Daniel Price
Analysis of correlation between pediatric asthma exacerbation and exposure to pollutant mixtures with association rule mining. Artif. Intell. Medicine 74 (2016)
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
Exploring the spectroscopic diversity of type Ia supernovae with Deep Learning and Unsupervised Clustering. Astroinformatics (2016)
Kinjal Dhar Gupta, Renuka Pampana, Ricardo Vilalta, Emille E. O. Ishida, Rafael S. de Souza
Automated supernova Ia classification using adaptive learning techniques. SSCI (2016)
Malcolm Dcosta, Dvijesh J. Shastri, Ricardo Vilalta, Judee K. Burgoon, Ioannis T. Pavlidis
Perinasal indicators of deceptive behavior. FG (2015)
Rafael S. de Souza, Ewan Cameron, Madhura Killedar, Joseph M. Hilbe, Ricardo Vilalta, Umberto Maio, Veronica Biffi, Benedetta Ciardi, Jamie D. Riggs
The overlooked potential of Generalized Linear Models in astronomy, I: Binomial regression. Astron. Comput. 12 (2015)
Ricardo Vilalta, Kinjal Dhar Gupta, Ashish Mahabal
Star Classification Under Data Variability: An Emerging Challenge in Astroinformatics. ECML/PKDD (3) (2015)
Roberto Valerio, Ricardo Vilalta
A Data Complexity Approach to Kernel Selection for Support Vector Machines. AAAI (2014)
Ricardo Vilalta, Kinjal Dhar Gupta, Lucas Macri
Domain Adaptation under Data Misalignment: An Application to Cepheid Variable Star Classification. ICPR (2014)
Weidong Shi, Yuanfeng Wen, Ziyi Liu, Xi Zhao, Dainis Boumber, Ricardo Vilalta, Lei Xu
Fault resilient physical neural networks on a single chip. CASES (2014)
Francisco Ocegueda-Hernandez, Ricardo Vilalta
An Empirical Study of the Suitability of Class Decomposition for Linear Models: When Does It Work Well? SDM (2013)
Ricardo Vilalta, Kinjal Dhar Gupta, Lucas Macri
A machine learning approach to Cepheid variable star classification using data alignment and maximum likelihood. Astron. Comput. 2 (2013)
Ricardo Vilalta, Luis Real
Modeling repetitive patterns: A bridge between pattern theory and data mining. GrC (2012)
Wei Ding, Tomasz F. Stepinski, Yang Mu, Lourenço P. C. Bandeira, Ricardo Vilalta, Youxi Wu, Zhenyu Lu, Tianyu Cao, Xindong Wu
Subkilometer crater discovery with boosting and transfer learning. ACM Trans. Intell. Syst. Technol. 2 (4) (2011)
Predrag T. Tosic, Ricardo Vilalta
A unified framework for reinforcement learning, co-learning and meta-learning how to coordinate in collaborative multi-agent systems. ICCS (1) (2010)
Predrag T. Tosic, Ricardo Vilalta
Learning and Meta-Learning for Coordination of Autonomous Unmanned Vehicles - A Preliminary Analysis. ECAI (2010)
Wei Ding, Tomasz F. Stepinski, Lourenço P. C. Bandeira, Ricardo Vilalta, Youxi Wu, Zhenyu Lu, Tianyu Cao
Automatic detection of craters in planetary images: an embedded framework using feature selection and boosting. CIKM (2010)
Ricardo Vilalta, Francisco Ocegueda-Hernandez, C. Bagaria
A Conceptual Study of Model Selection in Classification - Multiple Local Models vs One Global Model. ICAART (1) (2010)
Soumya Ghosh, Tomasz F. Stepinski, Ricardo Vilalta
Automatic Annotation of Planetary Surfaces With Geomorphic Labels. IEEE Trans. Geosci. Remote. Sens. 48 (1-1) (2010)
Rachsuda Jiamthapthaksin, Christoph F. Eick, Ricardo Vilalta
A Framework for Multi-Objective Clustering and Its Application to Co-Location Mining. ADMA (2009)
Jaspal Subhlok, Olin G. Johnson, Venkat Subramaniam, Ricardo Vilalta, Chang Yun
Tablet PC video based hybrid coursework in computer science: report from a pilot project. SIGCSE (2007)
Ricardo Vilalta, Tomasz F. Stepinski, Murali-Krishna Achari
An efficient approach to external cluster assessment with an application to martian topography. Data Min. Knowl. Discov. 14 (1) (2007)
Tomasz F. Stepinski, Ricardo Vilalta, Soumya Ghosh
Machine Learning Tools for Automatic Mapping of Martian Landforms. IEEE Intell. Syst. 22 (6) (2007)
Chaofan Sun, Ricardo Vilalta
Data Selection Using SASH Trees for Support Vector Machines. MLDM (2007)
Tomasz F. Stepinski, Soumya Ghosh, Ricardo Vilalta
Machine Learning for Automatic Mapping of Planetary Surfaces. AAAI (2007)
Christoph F. Eick, Alain Rouhana, Abraham Bagherjeiran, Ricardo Vilalta
Using clustering to learn distance functions for supervised similarity assessment. Eng. Appl. Artif. Intell. 19 (4) (2006)
Ricardo Vilalta
Identifying and Characterizing Class Clusters to Explain Learning Performance. AAAI Spring Symposium: What Went Wrong and Why: Lessons from AI Research and Applications (2006)
Tomasz F. Stepinski, Soumya Ghosh, Ricardo Vilalta
Automatic Recognition of Landforms on Mars Using Terrain Segmentation and Classification. Discovery Science (2006)
Abraham Bagherjeiran, Christoph F. Eick, Chun-Sheng Chen, Ricardo Vilalta
Adaptive Clustering: Obtaining Better Clusters Using Feedback and Past Experience. ICDM (2005)
Tomasz F. Stepinski, Ricardo Vilalta
Digital topography models for Martian surfaces. IEEE Geosci. Remote. Sens. Lett. 2 (3) (2005)
Bruce Knuteson, Ricardo Vilalta
Testing Theories in Particle Physics Using Maximum Likelihood and Adaptive Bin Allocation. PKDD (2005)
Abraham Bagherjeiran, Ricardo Vilalta, Christoph F. Eick
Content-Based Image Retrieval through a Multi-Agent Meta-Learning Framework. ICTAI (2005)
Christoph F. Eick, Alain Rouhana, Abraham Bagherjeiran, Ricardo Vilalta
Using Clustering to Learn Distance Functions for Supervised Similarity Assessment. MLDM (2005)
Ricardo Vilalta, Tomasz F. Stepinski, Murali-Krishna Achari, Francisco Ocegueda-Hernandez
A Quantification of Cluster Novelty with an Application to Martian Topography. PKDD (2004)
Ricardo Vilalta, Murali-Krishna Achari, Christoph F. Eick
Piece-Wise Model Fitting Using Local Data Patterns. ECAI (2004)
Christoph F. Eick, Nidal M. Zeidat, Ricardo Vilalta
Using Representative-Based Clustering for Nearest Neighbor Dataset Editing. ICDM (2004)
Christophe G. Giraud-Carrier, Ricardo Vilalta, Pavel Brazdil
Introduction to the Special Issue on Meta-Learning. Mach. Learn. 54 (3) (2004)
Ricardo Vilalta, Christophe G. Giraud-Carrier, Pavel Brazdil, Carlos Soares
Using Meta-Learning to Support Data Mining. Int. J. Comput. Sci. Appl. 1 (1) (2004)
Ricardo Vilalta, Daniel Oblinger
Evaluation Metrics in Classification: A Quantification of Distance-Bias. Comput. Intell. 19 (3) (2003)
Ramendra K. Sahoo, Adam J. Oliner, Irina Rish, Manish Gupta, José E. Moreira, Sheng Ma, Ricardo Vilalta, Anand Sivasubramaniam
Critical event prediction for proactive management in large-scale computer clusters. KDD (2003)
Ricardo Vilalta, Murali-Krishna Achari, Christoph F. Eick
Class Decomposition via Clustering: A New Framework for Low-Variance Classifiers. ICDM (2003)
Ricardo Vilalta, Irina Rish
A Decomposition of Classes via Clustering to Explain and Improve Naive Bayes. ECML (2003)
Ricardo Vilalta, Sheng Ma
Predicting Rare Events In Temporal Domains. ICDM (2002)
Ricardo Vilalta, Chidanand Apté, Joseph L. Hellerstein, Sheng Ma, Sholom M. Weiss
Predictive algorithms in the management of computer systems. IBM Syst. J. 41 (3) (2002)
Carlotta Domeniconi, Chang-Shing Perng, Ricardo Vilalta, Sheng Ma
A Classification Approach for Prediction of Target Events in Temporal Sequences. PKDD (2002)
Ricardo Vilalta, Youssef Drissi
A Perspective View and Survey of Meta-Learning. Artif. Intell. Rev. 18 (2) (2002)
Ricardo Vilalta, Youssef Drissi
A Characterization of Difficult Problems in Classification. ICMLA (2002)
Ricardo Vilalta, Sheng Ma, Joseph L. Hellerstein
Rule Induction of Computer Events. DSOM (2001)
Ricardo Vilalta, Mark Brodie, Daniel Oblinger, Irina Rish
A Unified Framework for Evaluation Metrics in Classification Using Decision Trees. ECML (2001)
Ricardo Vilalta, Chidanand Apté, Sholom M. Weiss
Operational Data Analysis: Improved Predictions Using Multi-computer Pattern Detection. DSOM (2000)
Ricardo Vilalta, Daniel Oblinger
A Quantification of Distance Bias Between Evaluation Metrics In Classification. ICML (2000)
Ricardo Vilalta, Gunnar Blix, Larry A. Rendell
Global Data Analysis and the Fragmentation Problem in Decision Tree Induction. ECML (1997)
Ricardo Vilalta, Larry A. Rendell
Integrating Feature Construction with Multiple Classifiers in Decision Tree Induction. ICML (1997)