Fundamental Nano-Patterns to Characterize and Classify Java Methods. - Jeremy Singer; Gavin Brown 001; Mikel Luján; Adam Craig Pocock; Paraskevas Yiapanis (LDTA, 2009)

Toward a more accurate understanding of the limits of the TLS execution paradigm. - Nikolas Ioannou; Jeremy Singer; Salman Khan 002; Polychronis Xekalakis; Paraskevas Yiapanis; Adam Craig Pocock; Gavin Brown 001; Mikel Luján; Ian Watson; Marcelo Cintra (IISWC, 2010)

Online Non-stationary Boosting. - Adam Craig Pocock; Paraskevas Yiapanis; Jeremy Singer; Mikel Luján; Gavin Brown 001 (MCS, 2010)

Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection. - Gavin Brown 001; Adam Craig Pocock; Ming-Jie Zhao; Mikel Luján (J. Mach. Learn. Res., 2012)

Informative Priors for Markov Blanket Discovery. - Adam Craig Pocock; Mikel Luján; Gavin Brown 001 (AISTATS, 2012)

Feature selection via joint likelihood. - Adam Craig Pocock (2012)

Augur: a Modeling Language for Data-Parallel Probabilistic Inference. - Jean-Baptiste Tristan; Daniel Huang 001; Joseph Tassarotti; Adam Craig Pocock; Stephen J. Green; Guy L. Steele Jr. (CoRR, 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 001 (J. Mach. Learn. Res., 2013)

Augur: Data-Parallel Probabilistic Modeling. - Jean-Baptiste Tristan; Daniel Huang 001; Joseph Tassarotti; Adam Craig Pocock; Stephen J. Green; Guy L. Steele Jr. (NIPS, 2014)

A scalable implementation of information theoretic feature selection for high dimensional data. - Anthony Kleerekoper; Michael Pappas; Adam Craig Pocock; Gavin Brown 001; Mikel Luján (IEEE BigData, 2015)

Minimally-Constrained Multilingual Embeddings via Artificial Code-Switching. - Michael L. Wick; Pallika Kanani; Adam Craig Pocock (AAAI, 2016)

Efficient feature selection using shrinkage estimators. - Konstantinos Sechidis; Laura Azzimonti; Adam Craig Pocock; Giorgio Corani; James Weatherall; Gavin Brown 001 (Mach. Learn., 2019)

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)

Correction to: Efficient feature selection using shrinkage estimators. - Konstantinos Sechidis; Laura Azzimonti; Adam Craig Pocock; Giorgio Corani; James Weatherall; Gavin Brown 001 (Mach. Learn., 2020)

Vate: Runtime Adaptable Probabilistic Programming for Java. - Daniel Goodman; Adam Craig Pocock; Jason Peck; Guy L. Steele Jr. (EuroMLSys@EuroSys, 2021)

Tribuo: Machine Learning with Provenance in Java. - Adam Craig Pocock (CoRR, 2021)

Integrated Reproducibility with Self-describing Machine Learning Models. - Joseph Wonsil; Jack Sullivan; Margo I. Seltzer; Adam Craig Pocock (ACM-REP, 2023)

Computational Experiment Comprehension using Provenance Summarization. - Nichole Boufford; Joseph Wonsil; Adam Craig Pocock; Jack Sullivan; Margo I. Seltzer; Thomas Pasquier (ACM-REP, 2024)