The structure of bayes networks for visual recognition. - John Mark Agosta (UAI, 1988)

Model-Based Influence Diagrams for Machine Vision. - Tod S. Levitt; John Mark Agosta; Thomas O. Binford (UAI, 1989)

"Conditional Inter-Causally Independent" Node Distributions, a Property of "Noisy-OR" Models. - John Mark Agosta (UAI, 1991)

Constraining Influence Diagram Structure by Generative Planning: An Application to the Optimization of Oil Spill Response. - John Mark Agosta (UAI, 1996)

When Gossip is Good: Distributed Probabilistic Inference for Detection of Slow Network Intrusions. - Denver Dash; Branislav Kveton; John Mark Agosta; Eve M. Schooler; Jaideep Chandrashekar; Abraham Bachrach; Alex Newman (AAAI, 2006)

A distributed host-based worm detection system. - Senthilkumar G. Cheetancheri; John Mark Agosta; Denver Dash; Karl N. Levitt; Jeff Rowe; Eve M. Schooler (LSAD@SIGCOMM, 2006)

Optimal Cost, Collaborative, and Distributed Response to Zero-Day Worms - A Control Theoretic Approach. - Senthilkumar G. Cheetancheri; John Mark Agosta; Karl N. Levitt; Shyhtsun Felix Wu; Jeff Rowe (RAID, 2008)

Workshop summary: Seventh annual workshop on Bayes applications. - John Mark Agosta; Russell G. Almond; Dennis M. Buede; Marek J. Druzdzel; Judy Goldsmith; Silja Renooij (ICML, 2009)

What is disputed on the web? - Rob Ennals; Dan Byler; John Mark Agosta; Barbara Rosario (WICOW, 2010)

Highlighting disputed claims on the web. - Rob Ennals; Beth Trushkowsky; John Mark Agosta (WWW, 2010)

Automated Refinement of Bayes Networks' Parameters based on Test Ordering Constraints. - Omar Zia Khan; Pascal Poupart; John Mark Agosta (NIPS, 2011)

Mixture models of endhost network traffic. - John Mark Agosta; Jaideep Chandrashekar; Mark Crovella; Nina Taft; Daniel Ting (INFOCOM, 2013)

Iterative Model Refinement of Recommender MDPs Based on Expert Feedback. - Omar Zia Khan; Pascal Poupart; John Mark Agosta (ECML/PKDD (1), 2013)

A Lightweight Inference Method for Image Classification. - John Mark Agosta; Preeti J. Pillai (UAI Application Workshops, 2013)

The structure of Bayes nets for vision recognition - John Mark Agosta (CoRR, 2013)

Scalable Data Analytics Using R: Single Machines to Hadoop Spark Clusters. - John Mark Agosta; Debraj GuhaThakurta; Robert Horton; Mario Inchiosa; Srini Kumar; Mengyue Zhao (KDD, 2016)

Interpreting Dynamic Causal Model Policies. - John Mark Agosta; Robert Horton; Maryam Tavakoli Hosseinabadi (PKDD/ECML Workshops (2), 2023)

Hierarchical Bayesian Regression for Multi-Location Sales Transaction Forecasting. - John Mark Agosta; Mario Inchiosa (CoRR, 2023)

Redeeming Data Science by Decision Modelling. - John Mark Agosta; Robert Horton (CoRR, 2023)

Runtime Variation in Big Data Analytics. - Yiwen Zhu; Rathijit Sen; Robert Horton; John Mark Agosta (Proc. ACM Manag. Data, 2023)