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ORCIDRicardo Vilalta
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- affiliation: University of Houston, USA
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2020 – today
- 2024
[j17]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)- [j16]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): 15044 (2024) - 2023
- [i9]Zhenyu Dai, Ben Moews, Ricardo Vilalta, Romeel Dave:
Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter. CoRR abs/2303.14090 (2023) - [i8]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 abs/2312.10040 (2023) - 2022
- [j15]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: 787421 (2022) - 2021
- [c44]Mikhail M. Meskhi, Adriano Rivolli, Rafael G. Mantovani, Ricardo Vilalta:
Learning Abstract Task Representations. MetaDL@AAAI 2021: 127-137 - [i7]Mikhail M. Meskhi, Adriano Rivolli, Rafael G. Mantovani, Ricardo Vilalta:
Learning Abstract Task Representations. CoRR abs/2101.07852 (2021) - [i6]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:
Applications and Techniques for Fast Machine Learning in Science. CoRR abs/2110.13041 (2021) - 2020
- [c43]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:
Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients. SSCI 2020: 3115-3124 - [i5]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:
Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients. CoRR abs/2010.05941 (2020)
2010 – 2019
- 2019
- [c42]Farinaz Pisheh, Ricardo Vilalta:
Filter-Based Information-Theoretic Feature Selection. ICAAI 2019: 207-211 - [i4]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 abs/1911.02479 (2019) - 2018
- [i3]Behrang Mehrparvar, Ricardo Vilalta:
Conceptual Domain Adaptation Using Deep Learning. CoRR abs/1808.05355 (2018) - [i2]Ricardo Vilalta, Kinjal Dhar Gupta, Dainis Boumber, Mikhail M. Meskhi:
A General Approach to Domain Adaptation with Applications in Astronomy. CoRR abs/1812.08839 (2018) - [i1]Ricardo Vilalta:
Transfer Learning in Astronomy: A New Machine-Learning Paradigm. CoRR abs/1812.10403 (2018) - 2017
- [c41]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: 1-7 - [c40]Ricardo Vilalta, Emille E. O. Ishida, Róbert Beck, R. Sutrisno, Rafael S. de Souza, Ashish Mahabal:
Photometric redshift estimation: An active learning approach. SSCI 2017: 1-8 - [r6]Ricardo Vilalta, Christophe G. Giraud-Carrier, Pavel Brazdil, Carlos Soares:
Inductive Transfer. Encyclopedia of Machine Learning and Data Mining 2017: 666-671 - [r5]Pavel Brazdil, Ricardo Vilalta, Christophe G. Giraud-Carrier, Carlos Soares:
Metalearning. Encyclopedia of Machine Learning and Data Mining 2017: 818-823 - 2016
- [j14]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: 44-52 (2016) - [c39]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: 247-252 - [c38]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: 1-8 - 2015
- [j13]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: 21-32 (2015) - [c37]Malcolm Dcosta, Dvijesh J. Shastri, Ricardo Vilalta, Judee K. Burgoon, Ioannis T. Pavlidis:
Perinasal indicators of deceptive behavior. FG 2015: 1-8 - [c36]Ricardo Vilalta, Kinjal Dhar Gupta, Ashish Mahabal:
Star Classification Under Data Variability: An Emerging Challenge in Astroinformatics. ECML/PKDD (3) 2015: 241-244 - 2014
- [c35]Roberto Valerio, Ricardo Vilalta:
A Data Complexity Approach to Kernel Selection for Support Vector Machines. AAAI 2014: 3138-3139 - [c34]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: 24:1-24:10 - [c33]Ricardo Vilalta, Kinjal Dhar Gupta, Lucas Macri:
Domain Adaptation under Data Misalignment: An Application to Cepheid Variable Star Classification. ICPR 2014: 3660-3665 - 2013
- [j12]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: 46-53 (2013) - [c32]Francisco Ocegueda-Hernandez, Ricardo Vilalta:
An Empirical Study of the Suitability of Class Decomposition for Linear Models: When Does It Work Well? SDM 2013: 432-440 - 2012
- [c31]Ricardo Vilalta, Luis Real:
Modeling repetitive patterns: A bridge between pattern theory and data mining. GrC 2012: 493-498 - 2011
- [j11]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): 39:1-39:22 (2011) - 2010
- [j10]Soumya Ghosh, Tomasz F. Stepinski, Ricardo Vilalta:
Automatic Annotation of Planetary Surfaces With Geomorphic Labels. IEEE Trans. Geosci. Remote. Sens. 48(1-1): 175-185 (2010) - [c30]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: 749-758 - [c29]Predrag T. Tosic, Ricardo Vilalta:
Learning and Meta-Learning for Coordination of Autonomous Unmanned Vehicles - A Preliminary Analysis. ECAI 2010: 163-168 - [c28]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: 113-118 - [c27]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 2010: 2217-2226 - [p2]Ricardo Vilalta, Christophe G. Giraud-Carrier, Pavel Brazdil:
Meta-Learning - Concepts and Techniques. Data Mining and Knowledge Discovery Handbook 2010: 717-731 - [r4]Ricardo Vilalta, Christophe G. Giraud-Carrier, Pavel Brazdil, Carlos Soares:
Inductive Transfer. Encyclopedia of Machine Learning 2010: 545-548 - [r3]Pavel Brazdil, Ricardo Vilalta, Christophe G. Giraud-Carrier, Carlos Soares:
Metalearning. Encyclopedia of Machine Learning 2010: 662-666
2000 – 2009
- 2009
- [b2]Pavel Brazdil, Christophe G. Giraud-Carrier, Carlos Soares, Ricardo Vilalta:
Metalearning - Applications to Data Mining. Cognitive Technologies, Springer 2009, ISBN 978-3-540-73262-4, pp. I-X, 1-176 [contents] - [c26]Rachsuda Jiamthapthaksin, Christoph F. Eick, Ricardo Vilalta:
A Framework for Multi-Objective Clustering and Its Application to Co-Location Mining. ADMA 2009: 188-199 - [r2]Ricardo Vilalta, Tomasz F. Stepinski:
Cluster Validation. Encyclopedia of Data Warehousing and Mining 2009: 231-236 - [r1]Christophe G. Giraud-Carrier, Pavel Brazdil, Carlos Soares, Ricardo Vilalta:
Meta-Learning. Encyclopedia of Data Warehousing and Mining 2009: 1207-1215 - 2007
- [j9]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): 1-23 (2007) - [j8]Tomasz F. Stepinski, Ricardo Vilalta, Soumya Ghosh:
Machine Learning Tools for Automatic Mapping of Martian Landforms. IEEE Intell. Syst. 22(6): 100-106 (2007) - [c25]Tomasz F. Stepinski, Soumya Ghosh, Ricardo Vilalta:
Machine Learning for Automatic Mapping of Planetary Surfaces. AAAI 2007: 1807-1812 - [c24]Chaofan Sun, Ricardo Vilalta:
Data Selection Using SASH Trees for Support Vector Machines. MLDM 2007: 286-295 - [c23]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: 74-78 - 2006
- [j7]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): 395-401 (2006) - [c22]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: 19-25 - [c21]Tomasz F. Stepinski, Soumya Ghosh, Ricardo Vilalta:
Automatic Recognition of Landforms on Mars Using Terrain Segmentation and Classification. Discovery Science 2006: 255-266 - 2005
- [j6]Tomasz F. Stepinski, Ricardo Vilalta:
Digital topography models for Martian surfaces. IEEE Geosci. Remote. Sens. Lett. 2(3): 260-264 (2005) - [c20]Abraham Bagherjeiran, Christoph F. Eick, Chun-Sheng Chen, Ricardo Vilalta:
Adaptive Clustering: Obtaining Better Clusters Using Feedback and Past Experience. ICDM 2005: 565-568 - [c19]Abraham Bagherjeiran, Ricardo Vilalta, Christoph F. Eick:
Content-Based Image Retrieval through a Multi-Agent Meta-Learning Framework. ICTAI 2005: 24-28 - [c18]Christoph F. Eick, Alain Rouhana, Abraham Bagherjeiran, Ricardo Vilalta:
Using Clustering to Learn Distance Functions for Supervised Similarity Assessment. MLDM 2005: 120-131 - [c17]Bruce Knuteson, Ricardo Vilalta:
Testing Theories in Particle Physics Using Maximum Likelihood and Adaptive Bin Allocation. PKDD 2005: 552-560 - [p1]Ricardo Vilalta, Christophe G. Giraud-Carrier, Pavel Brazdil:
Meta-Learning. The Data Mining and Knowledge Discovery Handbook 2005: 731-748 - 2004
- [j5]Ricardo Vilalta, Christophe G. Giraud-Carrier, Pavel Brazdil, Carlos Soares:
Using Meta-Learning to Support Data Mining. Int. J. Comput. Sci. Appl. 1(1): 31-45 (2004) - [j4]Christophe G. Giraud-Carrier, Ricardo Vilalta, Pavel Brazdil:
Introduction to the Special Issue on Meta-Learning. Mach. Learn. 54(3): 187-193 (2004) - [c16]Ricardo Vilalta, Murali-Krishna Achari, Christoph F. Eick:
Piece-Wise Model Fitting Using Local Data Patterns. ECAI 2004: 559-563 - [c15]Christoph F. Eick, Nidal M. Zeidat, Ricardo Vilalta:
Using Representative-Based Clustering for Nearest Neighbor Dataset Editing. ICDM 2004: 375-378 - [c14]Ricardo Vilalta, Tomasz F. Stepinski, Murali-Krishna Achari, Francisco Ocegueda-Hernandez:
A Quantification of Cluster Novelty with an Application to Martian Topography. PKDD 2004: 434-445 - 2003
- [j3]Ricardo Vilalta, Daniel Oblinger:
Evaluation Metrics in Classification: A Quantification of Distance-Bias. Comput. Intell. 19(3): 264-283 (2003) - [c13]Ricardo Vilalta, Murali-Krishna Achari:
A hierarchical approach to classification for systems with complex low-level interactions. ISIC 2003: 110-115 - [c12]Ricardo Vilalta, Irina Rish:
A Decomposition of Classes via Clustering to Explain and Improve Naive Bayes. ECML 2003: 444-455 - [c11]Ricardo Vilalta, Murali-Krishna Achari, Christoph F. Eick:
Class Decomposition via Clustering: A New Framework for Low-Variance Classifiers. ICDM 2003: 673-676 - [c10]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: 426-435 - 2002
- [j2]Ricardo Vilalta, Youssef Drissi:
A Perspective View and Survey of Meta-Learning. Artif. Intell. Rev. 18(2): 77-95 (2002) - [j1]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): 461-474 (2002) - [c9]Ricardo Vilalta, Sheng Ma:
Predicting Rare Events In Temporal Domains. ICDM 2002: 474-481 - [c8]Ricardo Vilalta, Youssef Drissi:
A Characterization of Difficult Problems in Classification. ICMLA 2002: 133-138 - [c7]Carlotta Domeniconi, Chang-Shing Perng, Ricardo Vilalta, Sheng Ma:
A Classification Approach for Prediction of Target Events in Temporal Sequences. PKDD 2002: 125-137 - 2001
- [c6]Ricardo Vilalta, Sheng Ma, Joseph L. Hellerstein:
Rule Induction of Computer Events. DSOM 2001: 75-86 - [c5]Ricardo Vilalta, Mark Brodie, Daniel Oblinger, Irina Rish:
A Unified Framework for Evaluation Metrics in Classification Using Decision Trees. ECML 2001: 503-514 - 2000
- [c4]Ricardo Vilalta, Chidanand Apté, Sholom M. Weiss:
Operational Data Analysis: Improved Predictions Using Multi-computer Pattern Detection. DSOM 2000: 37-46 - [c3]Ricardo Vilalta, Daniel Oblinger:
A Quantification of Distance Bias Between Evaluation Metrics In Classification. ICML 2000: 1087-1094
1990 – 1999
- 1998
- [b1]Ricardo Vilalta:
On the Development of Inductive Learning Algorithms: Generating Flexible and Adaptable Concept Representations. University of Illinois Urbana-Champaign, USA, 1998 - 1997
- [c2]Ricardo Vilalta, Gunnar Blix, Larry A. Rendell:
Global Data Analysis and the Fragmentation Problem in Decision Tree Induction. ECML 1997: 312-326 - [c1]Ricardo Vilalta, Larry A. Rendell:
Integrating Feature Construction with Multiple Classifiers in Decision Tree Induction. ICML 1997: 394-402
Coauthor Index
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