Adam Craig Pocock

According to our database, Adam Craig Pocock authored at least 17 papers between 2009 and 2023.

Collaborative distances:

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

2023

Integrated Reproducibility with Self-describing Machine Learning Models.

Joseph Wonsil, Jack Sullivan, Margo I. Seltzer, Adam Craig Pocock

Proceedings of the 2023 ACM Conference on Reproducibility and Replicability, 2023

2021

Tribuo: Machine Learning with Provenance in Java.

Adam Craig Pocock

CoRR, 2021

Vate: Runtime Adaptable Probabilistic Programming for Java.

Daniel Goodman, Adam Craig Pocock, Jason Peck, Guy L. Steele Jr.

Proceedings of the EuroMLSys@EuroSys 2021, 2021

2020

Correction to: Efficient feature selection using shrinkage estimators.

Konstantinos Sechidis, Laura Azzimonti, Adam Craig Pocock, Giorgio Corani, James Weatherall, Gavin Brown

Mach. Learn., 2020

Detecting and Exorcising Statistical Demons from Language Models with Anti-Models of Negative Data.

Michael L. Wick, Kate Silverstein, Jean-Baptiste Tristan, Adam Craig Pocock, Mark Johnson

CoRR, 2020

2019

Efficient feature selection using shrinkage estimators.

Konstantinos Sechidis, Laura Azzimonti, Adam Craig Pocock, Giorgio Corani, James Weatherall, Gavin Brown

Mach. Learn., 2019

2016

Minimally-Constrained Multilingual Embeddings via Artificial Code-Switching.

Michael L. Wick, Pallika Kanani, Adam Craig Pocock

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

A scalable implementation of information theoretic feature selection for high dimensional data.

Anthony Kleerekoper, Michael Pappas, Adam Craig Pocock, Gavin Brown, Mikel Luján

Proceedings of the 2015 IEEE International Conference on Big Data (IEEE BigData 2015), Santa Clara, CA, USA, October 29, 2015

2014

Augur: Data-Parallel Probabilistic Modeling.

Jean-Baptiste Tristan, Daniel Huang, Joseph Tassarotti, Adam Craig Pocock, Stephen J. Green, Guy L. Steele Jr.

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

Beyond Fano's inequality: bounds on the optimal F-score, BER, and cost-sensitive risk and their implications.

Ming-Jie Zhao, Narayanan Unny Edakunni, Adam Craig Pocock, Gavin Brown

J. Mach. Learn. Res., 2013

Augur: a Modeling Language for Data-Parallel Probabilistic Inference.

Jean-Baptiste Tristan, Daniel Huang, Joseph Tassarotti, Adam Craig Pocock, Stephen J. Green, Guy L. Steele Jr.

CoRR, 2013

2012

Feature selection via joint likelihood.

Adam Craig Pocock

PhD thesis, 2012

Informative Priors for Markov Blanket Discovery.

Adam Craig Pocock, Mikel Luján, Gavin Brown

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection.

Gavin Brown, Adam Craig Pocock, Ming-Jie Zhao, Mikel Luján

J. Mach. Learn. Res., 2012

2010

Online Non-stationary Boosting.

Adam Craig Pocock, Paraskevas Yiapanis, Jeremy Singer, Mikel Luján, Gavin Brown

Proceedings of the Multiple Classifier Systems, 9th International Workshop, 2010

Toward a more accurate understanding of the limits of the TLS execution paradigm.

Nikolas Ioannou, Jeremy Singer, Salman Khan, Polychronis Xekalakis, Paraskevas Yiapanis, Adam Craig Pocock, Gavin Brown, Mikel Luján, Ian Watson, Marcelo Cintra

Proceedings of the 2010 IEEE International Symposium on Workload Characterization, 2010

2009

Fundamental Nano-Patterns to Characterize and Classify Java Methods.

Jeremy Singer, Gavin Brown, Mikel Luján, Adam Craig Pocock, Paraskevas Yiapanis

Proceedings of the Ninth Workshop on Language Descriptions Tools and Applications, 2009