Ricardo Vilalta

82 papers A* 9A 5B 4C 2Misc 3Journal 27Unranked 22

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Corrigendum: Applications and techniques for fast machine learning in science. Frontiers Big Data 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
2024	—	`conf`	Meta-learning Loss Functions of Parametric Partial Differential Equations Using Physics-Informed Neural Networks. DS (1) Michail Koumpanakis, Ricardo Vilalta
2024	J	`jnl`	Meta-learning Loss Functions of Parametric Partial Differential Equations Using Physics-Informed Neural Networks. CoRR Michail Koumpanakis, Ricardo Vilalta
2024	J	`jnl`	Robust errant beam prognostics with conditional modeling for particle accelerators. Mach. Learn. Sci. Technol. Kishansingh Rajput, Malachi Schram, Willem Blokland, Yasir Alanazi, Pradeep Ramuhalli, Alexander Zhukov, Charles Peters, Ricardo Vilalta
2023	J	`jnl`	Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter. CoRR Zhenyu Dai, Ben Moews, Ricardo Vilalta, Romeel Dave
2023	J	`jnl`	Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators. CoRR Kishansingh Rajput, Malachi Schram, Willem Blokland, Yasir Alanazi, Pradeep Ramuhalli, Alexander Zhukov, Charles Peters, Ricardo Vilalta
2022	J	`jnl`	Applications and Techniques for Fast Machine Learning in Science. Frontiers Big Data 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
2021	J	`jnl`	Applications and Techniques for Fast Machine Learning in Science. CoRR 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
2021	—	`conf`	Learning Abstract Task Representations. MetaDL@AAAI Mikhail M. Meskhi, Adriano Rivolli, Rafael G. Mantovani, Ricardo Vilalta
2021	J	`jnl`	Learning Abstract Task Representations. CoRR Mikhail M. Meskhi, Adriano Rivolli, Rafael G. Mantovani, Ricardo Vilalta
2020	—	`conf`	Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients. SSCI 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
2020	J	`jnl`	Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients. CoRR 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
2019	J	`jnl`	Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era. CoRR 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 L. 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
2019	—	`conf`	Filter-Based Information-Theoretic Feature Selection. ICAAI Farinaz Pisheh, Ricardo Vilalta
2018	J	`jnl`	A General Approach to Domain Adaptation with Applications in Astronomy. CoRR Ricardo Vilalta, Kinjal Dhar Gupta, Dainis Boumber, Mikhail M. Meskhi
2018	J	`jnl`	Conceptual Domain Adaptation Using Deep Learning. CoRR Behrang Mehrparvar, Ricardo Vilalta
2018	J	`jnl`	Transfer Learning in Astronomy: A New Machine-Learning Paradigm. CoRR Ricardo Vilalta
2017	—	`conf`	Disk failure prediction in heterogeneous environments. SPECTS Carlos A. Rincon C., Jehan-François Pâris, Ricardo Vilalta, Albert M. K. Cheng, Darrell D. E. Long
2017	—	`ch.`	Inductive Transfer. Encyclopedia of Machine Learning and Data Mining Ricardo Vilalta, Christophe G. Giraud-Carrier, Pavel Brazdil, Carlos Soares
2017	—	`ch.`	Metalearning. Encyclopedia of Machine Learning and Data Mining Pavel Brazdil, Ricardo Vilalta, Christophe G. Giraud-Carrier, Carlos Soares
2017	—	`conf`	Photometric redshift estimation: An active learning approach. SSCI Ricardo Vilalta, Emille E. O. Ishida, Róbert Beck, R. Sutrisno, Rafael S. de Souza, Ashish Mahabal
2016	J	`jnl`	Analysis of correlation between pediatric asthma exacerbation and exposure to pollutant mixtures with association rule mining. Artif. Intell. Medicine Giulia Toti, Ricardo Vilalta, Peggy Lindner, Barry Lefer, Charles Macias, Daniel Price
2016	—	`conf`	Automated supernova Ia classification using adaptive learning techniques. SSCI Kinjal Dhar Gupta, Renuka Pampana, Ricardo Vilalta, Emille E. O. Ishida, Rafael S. de Souza
2016	—	`conf`	Exploring the spectroscopic diversity of type Ia supernovae with Deep Learning and Unsupervised Clustering. Astroinformatics 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
2015	B	`conf`	Perinasal indicators of deceptive behavior. FG Malcolm Dcosta, Dvijesh J. Shastri, Ricardo Vilalta, Judee K. Burgoon, Ioannis T. Pavlidis
2015	—	`conf`	Star Classification Under Data Variability: An Emerging Challenge in Astroinformatics. ECML/PKDD (3) Ricardo Vilalta, Kinjal Dhar Gupta, Ashish Mahabal
2015	J	`jnl`	The overlooked potential of Generalized Linear Models in astronomy, I: Binomial regression. Astron. Comput. Rafael S. de Souza, Ewan Cameron, Madhura Killedar, Joseph M. Hilbe, Ricardo Vilalta, Umberto Maio, Veronica Biffi, Benedetta Ciardi, Jamie D. Riggs
2014	A*	`conf`	A Data Complexity Approach to Kernel Selection for Support Vector Machines. AAAI Roberto Valerio, Ricardo Vilalta
2014	B	`conf`	Domain Adaptation under Data Misalignment: An Application to Cepheid Variable Star Classification. ICPR Ricardo Vilalta, Kinjal Dhar Gupta, Lucas Macri
2014	Misc	`conf`	Fault resilient physical neural networks on a single chip. CASES Weidong Shi, Yuanfeng Wen, Ziyi Liu, Xi Zhao, Dainis Boumber, Ricardo Vilalta, Lei Xu
2013	J	`jnl`	A machine learning approach to Cepheid variable star classification using data alignment and maximum likelihood. Astron. Comput. Ricardo Vilalta, Kinjal Dhar Gupta, Lucas Macri
2013	A	`conf`	An Empirical Study of the Suitability of Class Decomposition for Linear Models: When Does It Work Well? SDM Francisco Ocegueda-Hernandez, Ricardo Vilalta
2012	Misc	`conf`	Modeling repetitive patterns: A bridge between pattern theory and data mining. GrC Ricardo Vilalta, Luis Real
2011	J	`jnl`	Subkilometer crater discovery with boosting and transfer learning. ACM Trans. Intell. Syst. Technol. Wei Ding, Tomasz F. Stepinski, Yang Mu, Lourenço P. C. Bandeira, Ricardo Vilalta, Youxi Wu, Zhenyu Lu, Tianyu Cao, Xindong Wu
2010	—	`conf`	A Conceptual Study of Model Selection in Classification - Multiple Local Models vs One Global Model. ICAART (1) Ricardo Vilalta, Francisco Ocegueda-Hernandez, C. Bagaria
2010	Misc	`conf`	A unified framework for reinforcement learning, co-learning and meta-learning how to coordinate in collaborative multi-agent systems. ICCS Predrag T. Tosic, Ricardo Vilalta
2010	J	`jnl`	Automatic Annotation of Planetary Surfaces With Geomorphic Labels. IEEE Trans. Geosci. Remote. Sens. Soumya Ghosh, Tomasz F. Stepinski, Ricardo Vilalta
2010	A	`conf`	Automatic detection of craters in planetary images: an embedded framework using feature selection and boosting. CIKM Wei Ding, Tomasz F. Stepinski, Lourenço P. C. Bandeira, Ricardo Vilalta, Youxi Wu, Zhenyu Lu, Tianyu Cao
2010	—	`ch.`	Inductive Transfer. Encyclopedia of Machine Learning Ricardo Vilalta, Christophe G. Giraud-Carrier, Pavel Brazdil, Carlos Soares
2010	A	`conf`	Learning and Meta-Learning for Coordination of Autonomous Unmanned Vehicles - A Preliminary Analysis. ECAI Predrag T. Tosic, Ricardo Vilalta
2010	—	`ch.`	Meta-Learning - Concepts and Techniques. Data Mining and Knowledge Discovery Handbook Ricardo Vilalta, Christophe G. Giraud-Carrier, Pavel Brazdil
2010	—	`ch.`	Metalearning. Encyclopedia of Machine Learning Pavel Brazdil, Ricardo Vilalta, Christophe G. Giraud-Carrier, Carlos Soares
2009	C	`conf`	A Framework for Multi-Objective Clustering and Its Application to Co-Location Mining. ADMA Rachsuda Jiamthapthaksin, Christoph F. Eick, Ricardo Vilalta
2009	—	`ch.`	Cluster Validation. Encyclopedia of Data Warehousing and Mining Ricardo Vilalta, Tomasz F. Stepinski
2009	—	`ch.`	Meta-Learning. Encyclopedia of Data Warehousing and Mining Christophe G. Giraud-Carrier, Pavel Brazdil, Carlos Soares, Ricardo Vilalta
2009	—	`book`	Metalearning - Applications to Data Mining. Pavel Brazdil, Christophe G. Giraud-Carrier, Carlos Soares, Ricardo Vilalta
2007	J	`jnl`	An efficient approach to external cluster assessment with an application to martian topography. Data Min. Knowl. Discov. Ricardo Vilalta, Tomasz F. Stepinski, Murali-Krishna Achari
2007	—	`conf`	Data Selection Using SASH Trees for Support Vector Machines. MLDM Chaofan Sun, Ricardo Vilalta
2007	J	`jnl`	Machine Learning Tools for Automatic Mapping of Martian Landforms. IEEE Intell. Syst. Tomasz F. Stepinski, Ricardo Vilalta, Soumya Ghosh
2007	A*	`conf`	Machine Learning for Automatic Mapping of Planetary Surfaces. AAAI Tomasz F. Stepinski, Soumya Ghosh, Ricardo Vilalta
2007	A	`conf`	Tablet PC video based hybrid coursework in computer science: report from a pilot project. SIGCSE Jaspal Subhlok, Olin G. Johnson, Venkat Subramaniam, Ricardo Vilalta, Chang Yun
2006	B	`conf`	Automatic Recognition of Landforms on Mars Using Terrain Segmentation and Classification. Discovery Science Tomasz F. Stepinski, Soumya Ghosh, Ricardo Vilalta
2006	—	`conf`	Identifying and Characterizing Class Clusters to Explain Learning Performance. AAAI Spring Symposium: What Went Wrong and Why: Lessons from AI Research and Applications Ricardo Vilalta
2006	J	`jnl`	Using clustering to learn distance functions for supervised similarity assessment. Eng. Appl. Artif. Intell. Christoph F. Eick, Alain Rouhana, Abraham Bagherjeiran, Ricardo Vilalta
2005	A*	`conf`	Adaptive Clustering: Obtaining Better Clusters Using Feedback and Past Experience. ICDM Abraham Bagherjeiran, Christoph F. Eick, Chun-Sheng Chen, Ricardo Vilalta
2005	B	`conf`	Content-Based Image Retrieval through a Multi-Agent Meta-Learning Framework. ICTAI Abraham Bagherjeiran, Ricardo Vilalta, Christoph F. Eick
2005	J	`jnl`	Digital topography models for Martian surfaces. IEEE Geosci. Remote. Sens. Lett. Tomasz F. Stepinski, Ricardo Vilalta
2005	—	`ch.`	Meta-Learning. The Data Mining and Knowledge Discovery Handbook Ricardo Vilalta, Christophe G. Giraud-Carrier, Pavel Brazdil
2005	—	`conf`	Testing Theories in Particle Physics Using Maximum Likelihood and Adaptive Bin Allocation. PKDD Bruce Knuteson, Ricardo Vilalta
2005	—	`conf`	Using Clustering to Learn Distance Functions for Supervised Similarity Assessment. MLDM Christoph F. Eick, Alain Rouhana, Abraham Bagherjeiran, Ricardo Vilalta
2004	—	`conf`	A Quantification of Cluster Novelty with an Application to Martian Topography. PKDD Ricardo Vilalta, Tomasz F. Stepinski, Murali-Krishna Achari, Francisco Ocegueda-Hernandez
2004	J	`jnl`	Introduction to the Special Issue on Meta-Learning. Mach. Learn. Christophe G. Giraud-Carrier, Ricardo Vilalta, Pavel Brazdil
2004	A	`conf`	Piece-Wise Model Fitting Using Local Data Patterns. ECAI Ricardo Vilalta, Murali-Krishna Achari, Christoph F. Eick
2004	J	`jnl`	Using Meta-Learning to Support Data Mining. Int. J. Comput. Sci. Appl. Ricardo Vilalta, Christophe G. Giraud-Carrier, Pavel Brazdil, Carlos Soares
2004	A*	`conf`	Using Representative-Based Clustering for Nearest Neighbor Dataset Editing. ICDM Christoph F. Eick, Nidal M. Zeidat, Ricardo Vilalta
2003	—	`conf`	A Decomposition of Classes via Clustering to Explain and Improve Naive Bayes. ECML Ricardo Vilalta, Irina Rish
2003	—	`conf`	A hierarchical approach to classification for systems with complex low-level interactions. ISIC Ricardo Vilalta, Murali-Krishna Achari
2003	A*	`conf`	Class Decomposition via Clustering: A New Framework for Low-Variance Classifiers. ICDM Ricardo Vilalta, Murali-Krishna Achari, Christoph F. Eick
2003	A*	`conf`	Critical event prediction for proactive management in large-scale computer clusters. KDD Ramendra K. Sahoo, Adam J. Oliner, Irina Rish, Manish Gupta, José E. Moreira, Sheng Ma, Ricardo Vilalta, Anand Sivasubramaniam
2003	J	`jnl`	Evaluation Metrics in Classification: A Quantification of Distance-Bias. Comput. Intell. Ricardo Vilalta, Daniel Oblinger
2002	C	`conf`	A Characterization of Difficult Problems in Classification. ICMLA Ricardo Vilalta, Youssef Drissi
2002	—	`conf`	A Classification Approach for Prediction of Target Events in Temporal Sequences. PKDD Carlotta Domeniconi, Chang-Shing Perng, Ricardo Vilalta, Sheng Ma
2002	J	`jnl`	A Perspective View and Survey of Meta-Learning. Artif. Intell. Rev. Ricardo Vilalta, Youssef Drissi
2002	A*	`conf`	Predicting Rare Events In Temporal Domains. ICDM Ricardo Vilalta, Sheng Ma
2002	J	`jnl`	Predictive algorithms in the management of computer systems. IBM Syst. J. Ricardo Vilalta, Chidanand Apté, Joseph L. Hellerstein, Sheng Ma, Sholom M. Weiss
2001	—	`conf`	A Unified Framework for Evaluation Metrics in Classification Using Decision Trees. ECML Ricardo Vilalta, Mark Brodie, Daniel Oblinger, Irina Rish
2001	—	`conf`	Rule Induction of Computer Events. DSOM Ricardo Vilalta, Sheng Ma, Joseph L. Hellerstein
2000	A*	`conf`	A Quantification of Distance Bias Between Evaluation Metrics In Classification. ICML Ricardo Vilalta, Daniel Oblinger
2000	—	`conf`	Operational Data Analysis: Improved Predictions Using Multi-computer Pattern Detection. DSOM Ricardo Vilalta, Chidanand Apté, Sholom M. Weiss
1998	—	`—`	On the Development of Inductive Learning Algorithms: Generating Flexible and Adaptable Concept Representations Ricardo Vilalta
1997	—	`conf`	Global Data Analysis and the Fragmentation Problem in Decision Tree Induction. ECML Ricardo Vilalta, Gunnar Blix, Larry A. Rendell
1997	A*	`conf`	Integrating Feature Construction with Multiple Classifiers in Decision Tree Induction. ICML Ricardo Vilalta, Larry A. Rendell