Publications

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Deshpande A, Guestrin C, Madden S.  2005.  Using probabilistic models for data management in acquisitional environments. Proc. CIDR. :317-328.
Deshpande A, Guestrin C, Madden SR, Hellerstein JM, Hong W.  2004.  Model-driven data acquisition in sensor networks. Proceedings of the Thirtieth international conference on Very large data bases - Volume 30. :588-599.
Deshpande A, Ives Z, Raman V.  2007.  Adaptive query processing: why, how, when, what next? Proceedings of the 33rd international conference on Very large data bases. :1426-1427.
Deshpande A, Sarawagi S.  2007.  Probabilistic graphical models and their role in databases. Proceedings of the 33rd international conference on Very large data bases. :1435-1436.
Deshpande A, Hellerstein JM.  2002.  Decoupled query optimization for federated database systems. 18th International Conference on Data Engineering, 2002. Proceedings. :716-727.
Deshpande A, Hicks MW.  2005.  Toward on-line schema evolution for non-stop systems. 11th High Performance Transaction Systems Workshop.
Deshpande A.  2010.  Increasing representational power and scaling reasoning in probabilistic databases. Proceedings of the 13th International Conference on Database Theory. :1-1.
Deshpande A, Madden S.  2006.  MauveDB: supporting model-based user views in database systems. Proceedings of the 2006 ACM SIGMOD international conference on Management of data. :73-84.
Deshpande A, Huang R, Raman V, Riggs T, Song D, Subramanian L.  1999.  A Study of the Structure of the Web. University of California, Berkeley.
Deshpande A, Hellerstein J.  2002.  On using correlation-based synopses during query optimization. Computer Science Division (EECS), University of California Berkeley.
Deshpande A, Guestrin C, Madden S, Hellerstein J, Hong W.  2005.  Model-based approximate querying in sensor networks. The VLDB Journal. 14(4):417-443.
Deshpande A.  2004.  Handling estimation errors in database query processing.
Deshpande A, Guestrin C, Hong W, Madden S.  2005.  Exploiting Correlated Attributes in Acquisitional Query Processing. 21st International Conference on Data Engineering, 2005. ICDE 2005. Proceedings. :143-154.
Deshpande A, Nath S, Gibbons PB, Seshan S.  2003.  IRIS: Internet-scale Resource-Intensive Sensor Services. Intel Research, UC Berkeley, Carnegie Mellon University.
Desimone R, Wilkins DE, Bienkowski M, desJardins M.  1993.  SOCAP: lessons learned in automating military operations planning. Proceedings of the 6th international conference on Industrial and engineering applications of artificial intelligence and expert systems. :384-393.
desJardins M, Bulka B, Carr R, Jordan E, Rheingans P.  2007.  Heuristic Search and Information Visualization Methods for School Redistricting. AI Magazine. 28(3):59-59.
desJardins M.  1995.  How to succeed in graduate school: a guide for students and advisors: part II of II. Crossroads. 1(3):1-6.
desJardins M, Littman M.  2010.  Broadening student enthusiasm for computer science with a great insights course. Proceedings of the 41st ACM technical symposium on Computer science education. :157-161.
desJardins M.  1993.  Workshop summary: AAAI-92 Workshop on Constraining Learning with Prior Knowledge. ACM SIGART Bulletin. 4(3):11-13.
desJardins M.  2001.  A day in the life of … Marie DesJardins. Crossroads. 8(2):8-9.
desJardins M, Bulka B, Carr R, Hunt A, Rathod P, Rheingans P.  2006.  Heuristic search and information visualization methods for school redistricting. Proceedings of the 18th conference on Innovative applications of artificial intelligence - Volume 2. :1774-1781.
desJardins M.  1994.  Knowledge acquisition techniques for a military planning system. Tools with Artificial Intelligence, 1994. Proceedings., Sixth International Conference on. :454-460.
desJardins M, Wolverton M.  1999.  Coordinating a Distributed Planning System. AI Magazine. 20(4):45-45.
desJardins M.  1994.  How to succeed in graduate school: a guide for students and advisors: part I of II. Crossroads. 1(2):3-9.
desJardins M, Rathod P, Getoor L.  2008.  LEARNING STRUCTURED BAYESIAN NETWORKS: COMBINING ABSTRACTION HIERARCHIES AND TREE‐STRUCTURED CONDITIONAL PROBABILITY TABLES. Computational Intelligence. 24(1):1-22.

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