Denver Dash Publications

PhD Thesis

• Caveats for Causal Reasoning with Equilibrium Models,
  Denver Dash
  Intelligent Systems Program, University of Pittsburgh, (2003).
  [pdf] [bib]

Book Chapters

• Bayesian Biosurveillance,
  Gregory F. Cooper, Denver H. Dash, John D. Levander, Weng-Keen Wong, William R. Hogan and Michael M. Wagner
  Handbook of Biosurveillance, (2006).
  [bib]

Peer-Reviewed Conferences & Journals

• Learning Why Things Change: The Difference-Based Causality Learner,
  Mark Voortman, Denver Dash and Marek Druzdzel
  Proceedings of the Twenty-Sixth Annual Conference on Uncertainty in Artificial Intelligence (UAI), (2010).
  [pdf] [bib]
• Learning Causal Models that Make Correct Manipulation Predictions,
  Mark Voortman, Denver Dash and Marek J. Druzdzel
  Journal of Machine Learning Research Workshop and Conference Proceedings. Causality: Objectives and Assessment (2010).
  [pdf] [bib]
• A Note on the Correctness of the Causal Ordering Algorithm,
  Denver Dash and Marek J. Druzdzel
  Artificial Intelligence (2008).
  [pdf] [bib]
• Efficient Inference in Persistent Dynamic Bayesian Networks,
  Tomas Singliar and Denver Dash
  Proceedings of the Twenty-Fourth Annual Conference on Uncertainty in Artificial Intelligence (UAI), (2008).
  [pdf] [bib]
• COD: Online Temporal Clustering for Outbreak Detection,
  Tomas Singliar and Denver Dash
  Proceedings of the Twenty-First Conference on Artificial Intelligence (AAAI), (2007).
  [pdf] [bib]
• When Gossip is Good: Distributed Probabilistic Inference for Detection of Slow Network Intrusions,
  Denver Dash, Branislav Kveton, John Mark Agosta, Eve Schooler, Jaideep Chandreshekar, Abraham Bachrach and Alex Newman
  Proceedings of the Twentieth Conference on Artificial Intelligence (AAAI), (2006).
  [pdf] [bib]
• Restructuring Dynamic Causal Systems in Equilibrium,
  Denver Dash
  Robert G. Cowell and Zoubin Ghahramani (Eds.) Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (AIStats), (2005).
  [pdf] [bib]
• Automatic Excursion Detection in Manufacturing: Preliminary Results,
  Branislav Kveton and Denver Dash
  Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS), (2005).
  [pdf] [bib]
• Bayesian Biosurveillance Using Multiple Data Streams,
  Weng-Keen Wong, Gregory F. Cooper, Dash, John D. Levander, J. Dowling, William Hogan and Michael M. Wagner
  Morbidity and Mortality Weekly Report: Supplement (2005).
  [pdf] [bib]
• Bayesian Biosurveillance of Disease Outbreaks,
  Gregory Cooper, Denver Dash, John Levander, Weng-Keen Wong, William Hogan and Michael Wagner
  Proceedings of the 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI), (2004).
  [pdf] [bib]
• Model Averaging for Prediction with Discrete Bayesian Networks,
  Denver Dash and Gregory F. Cooper
  Journal of Machine Learning Research (2004).
  [pdf] [bib]
• Bayesian Biosurveillance Using Multiple Data Streams,
  Gregory Cooper, Weng-Keen Wong, Denver Dash, John Levander, William Hogan and Michael Wagner
  Proceedings of the Third Annual Syndromic Surveillance Conference, (2004).
  [pdf] [bib]
• Exploiting Parametric Test Dependencies for Selective Avoidance of Sort Tests
  John Mark Agosta, Denver Dash, Christian Shelton and Krishna Arvind
  Intel Design and Test Technology Conference (DTTC), (2004).
  [bib]
• Model Averaging with Discrete Bayesian Classifiers,
  Denver Dash and Gregory F. Cooper
  Bishop, C. M. and Frey, B. J. (Eds.) Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics (AIStats), (2003).
  [pdf] [bib]
• A Robust Independence Test for Constraint-Based Learning of Causal Structure,
  Denver Dash and Marek Druzdzel
  Proceedings of the Nineteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI), (2003).
  [pdf] [bib]
• Evaluation of Bayesian Networks Used for Diagnostics,
  K. Wojtek Przytula, Denver Dash and Don Thompson
  Proceedings of the IEEE Aerospace Conference, (2003).
  [pdf] [bib]
• Exact Model Averaging with Naive Bayesian Classifiers,
  Denver Dash and Gregory F. Cooper
  Sammut, Claude and Hoffmann, Achim (Eds.) Proceedings of the Nineteenth International Conference on Machine Learning (ICML) (2002).
  [pdf] [bib]
• A Method for Evaluating Elicitation Schemes for Probabilistic Models,
  Haiqin Wang, Denver Dash and Marek J. Druzdzel
  IEEE Transaction on Systems, Man and Cybernetics Part B: Cybernetics (2002).
  [pdf] [bib]
• Caveats for Causal Reasoning with Equilibrium Models,
  Denver Dash and Marek J. Druzdzel
  Benferhat, Salem and Besnard, Philippe (Eds.) Proceedings of the Sixth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), (2001).
  [pdf] [bib]
• A Method for Evaluating Elicitation Schemes for Probabilities,
  Haiqin Wang and Denver Dash
  Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS) (2001).
  [pdf] [bib]
• A Hybrid Anytime Algorithm for the Construction of Causal Models From Sparse Data,
  Denver Dash and Marek Druzdzel
  Proceedings of the Fifteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI), (1999).
  [pdf] [bib]
• A Sheer-Induced Instability in Freely Suspended Smectic-A Liquid Crystal Films,
  Denver Dash and Xaio-Lun Wu
  Physical Review Letters (1999).
  [bib]

Workshops

• Toward a New Representation for Causality in Dynamic Systems,
  Denver Dash and Mark Voortman
  Workshop on Philosophy and Machine Learning, In conjunction with the 25th annual conference on Neural Information Processing Systems (NIPS), (2011).
  [pdf] [bib]
• Building a Real-Time System for High-Level Person Recognition,
  Denver Dash and Long Quoc Tran
  Ann Nicholson (Eds.) Proceedings of the Eighth UAI Bayesian Modeling Applications Workshop (UAI-AW), (2011).
  [pdf] [bib]
• Low-Dimensional Embedding of Large-Scale Infinite-Dimensional Function Spaces with Application to the Human Brain Connectome,
  Saeed Amizadeh, Mei Chen, Denver Dash, Milos Hauskrecht and Walter Schneider
  Workshop on Lowrank Methods for Large-scale Machine Learning, In conjunction with the 24th annual conference on Neural Information Processing Systems (NIPS), (2010).
  [pdf] [bib]
• Learning Dynamic Causal Models with Latent Confounding Processes,
  Mark Voortman and Denver Dash
  Workshop on Learning and Planning from Batch Time Series Dataa, In conjunction with the 24th annual conference on Neural Information Processing Systems (NIPS), (2010).
  [pdf] [bib]
• Relational Learning for Collective Classification of Entities in Images,
  Anton Chechetka, Denver Dash and Matthai Philipose
  AAAI-2010 Workshop on Statistical Relational AI (StarAI), (2010).
  [pdf] [bib]
• Efficient Causal Discovery and Abstraction in Perception Streams,
  Saeed Amizadeh and Denver Dash
  Workshop on Bounded-rational analyses of human cognition: {Bayesian} models, approximate inference, and the brain, in conjunction with the 23rd annual conference on Neural Information Processing Systems (NIPS), (2009).
  [pdf] [bib]
• Difference-Based Causal Models: Bridging the Gap Between Granger Causality and DCMs,
  Mark Voortman, Denver Dash, Marek Druzdzel, Dean Pomerleau and Gus Sudre
  Workshop on Connectivity Inference in Neuroimaging (CINI), in conjunction with the 23rd annual conference on Neural Information Processing Systems (NIPS), (2009).
  [pdf] [bib]
• Learning Causal Models That Make Correct Manipulation Predictions with Time Series Data,
  Mark Voortman, Denver Dash and Marek Druzdzel
  Workshop on Causality, in conjunction with the 22nd annual conference on Neural Information Processing Systems (NIPS), (2008).
  [pdf] [bib]
• A Distributed Host-based Worm Detection System,
  Senthilkumar G. Cheetancheri, John Mark Agosta, Denver H. Dash, Karl N. Levitt, Jeff Rowe and Eve Schooler
  Proceedings of the ACM SIGCOMM Workshop on Large Scale Attack Defense (LSAD), (2006).
  [pdf] [bib]
• Detecting Weak Network Anomalies with Bayesian Models
  Denver Dash, John Mark Agosta, Jaideep Chandrashekar and Eve Schooler
  Workshop on Machine Learning Algorithms for Surveillance and Event Detection, In conjunction with the Twenty-Third International Conference on Machine Learning (ICML), (2006).
  [bib]
• Learning Robust Generative Models for Distributed Anomaly Detection
  Denver Dash, John Mark Agosta, Abraham Bachrach, Branislav Kveton, Alex Newman and Eve Schooler
  Workshop on Intelligence Beyond the Desktop, In conjunction with the Nineteenth annual conference on Neural Information Processing Systems (NIPS), (2005).
  [bib]
• Distributed Inference to Detect a Network Attack,
  John Mark Agosta, Abraham Bachrach, Denver Dash, Branislav Kveton, Alex Newman and Eve Schooler
  Adaptive and Resilient Computing Security Workshop (ARCS), (2005).
  [pdf] [bib]
• Population-Wide Anomaly Detection,
  Weng-Keen Wong, Greg Cooper, Denver Dash, John Levander, William Hogan and Mike Wagner
  The Workshop on Data Mining Methods for Anomaly Detection, In conjunction with the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2005).
  [pdf] [bib]
• Empirical Investigation of Manipulation-Equilibration Commutability in Causal Models
  Denver Dash
  The Workshop on Causality and Causal Discovery, In conjunction with the Seventeenth Canadian Conference on Artificial Intelligence, (2004).
  [bib]
• An Inconsistency Between Causal Discovery and Causal Reasoning,
  Denver Dash and Marek J. Druzdzel
  The Workshop on Conditional Independence Structures and Graphical Models and the Workshop on Causal Interpretation of Graphical Models, (1999).
  [pdf] [bib]
• Problems Related to Causal Reasoning in Equilibrium Models
  Denver Dash and Marek J. Druzdzel
  Proceedings of the Conference on Theoretical Informatics: Methods of Analysis of Incomplete and Distributed Information, (1999).
  [bib]

Unpublished

• Modeling DNA Splice Regions by Learning Bayesian Networks,
  Denver Dash and Vanathi Gopalakrishnan
  Intelligent Systems Program, University of Pittsburgh, (2001).
  [pdf] [bib]