| A hybrid anytime algorithm for the constructiion of causal models from sparse data D Dash, MJ Druzdzel arXiv preprint arXiv:1301.6689, 2013 | 150 | 2013 |
| Bayesian biosurveillance of disease outbreaks GF Cooper, D Dash, J Levander, WK Wong, W Hogan, M Wagner arXiv preprint arXiv:1207.4122, 2012 | 120 | 2012 |
| Model averaging for prediction with discrete Bayesian networks D Dash, GF Cooper Journal of Machine Learning Research 5 (Sep), 1177-1203, 2004 | 104 | 2004 |
| When gossip is good: Distributed probabilistic inference for detection of slow network intrusions D Dash, B Kveton, JM Agosta, E Schooler, J Chandrashekar, A Bachrach, ... Proceedings of the national conference on Artificial Intelligence 21 (2), 1115, 2006 | 94 | 2006 |
| Robust Independence Testing for Constraint-Based Learning of Causal Structure. D Dash, MJ Druzdzel UAI 3, 167-174, 2003 | 85 | 2003 |
| Non-Boolean associative architectures based on nano-oscillators SP Levitan, Y Fang, DH Dash, T Shibata, DE Nikonov, GI Bourianoff 2012 13th International Workshop on Cellular Nanoscale Networks and their …, 2012 | 73 | 2012 |
| Scenecad: Predicting object alignments and layouts in rgb-d scans A Avetisyan, T Khanova, C Choy, D Dash, A Dai, M Nießner Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 62 | 2020 |
| Restructuring dynamic causal systems in equilibrium D Dash International Workshop on Artificial Intelligence and Statistics, 81-88, 2005 | 59 | 2005 |
| Exact model averaging with naive Bayesian classifiers D Dash, GF Cooper ICML, 91-98, 2002 | 55 | 2002 |
| A distributed host-based worm detection system SG Cheetancheri, JM Agosta, DH Dash, KN Levitt, J Rowe, EM Schooler Proceedings of the 2006 SIGCOMM workshop on Large-scale attack defense, 107-113, 2006 | 50 | 2006 |
| Caveats for causal reasoning with equilibrium models D Dash, M Druzdzel European Conference on Symbolic and Quantitative Approaches to Reasoning and …, 2001 | 46 | 2001 |
| A method for evaluating elicitation schemes for probabilistic models H Wang, D Dash, MJ Druzdzel IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 32 …, 2002 | 44 | 2002 |
| Learning why things change: the difference-based causality learner M Voortman, D Dash, MJ Druzdzel arXiv preprint arXiv:1203.3525, 2012 | 42 | 2012 |
| Evaluation of Bayesian network models for decision support KW Przytula, D Dash US Patent 7,650,272, 2010 | 41 | 2010 |
| Evaluation of Bayesian networks used for diagnostics KW Przytula, D Dash, D Thompson Proc IEEE Aerospace Conf 60, 1-12, 2003 | 41 | 2003 |
| Machine learning algorithms for event detection. D Margineantu, WK Wong, D Dash Machine Learning 79 (3), 2010 | 26 | 2010 |
| Systems and methods for mapping MC Falconer, D Choudhury, R Arefi, DJ Dahle, DH Dash, VS Somayazulu, ... US Patent 10,026,001, 2018 | 24 | 2018 |
| Estimation of colchicine in tubers of Gloriosa superba L. originated from different agroclimatic zones of Odisha, India UC Basak, D Dash, AK Mahapatra International Journal of Pharmacognosy and Phytochemical Research 4 (3), 157-161, 2012 | 24 | 2012 |
| Use of multiple data streams to conduct Bayesian biologic surveillance WK Wong, G Cooper, D Dash, J Levander, J Dowling, W Hogan, ... Morbidity and Mortality Weekly Report 54 (suppl), 63-69, 2005 | 23 | 2005 |
| A note on the correctness of the causal ordering algorithm D Dash, MJ Druzdzel Artificial Intelligence 172 (15), 1800-1808, 2008 | 22 | 2008 |
