Rina Dechter

About

Rina Dechter is a Distinguished Professor of Computer Science at UC Irvine's Donald Bren School of Information & Computer Sciences. Her research centers on the computational aspects of automated reasoning and knowledge representation, including search, constraint processing, and probabilistic reasoning. Her primary aim is to devise efficient methods through the understanding and exploitation of tractable reasoning tasks. She is an author of the book 'Constraint Processing' published by Morgan Kaufmann in 2003, and 'Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms' published by Morgan and Claypool in 2013. Dechter has authored over 150 research papers and has served on the editorial boards of several prominent journals, including Artificial Intelligence, the Constraint Journal, the Journal of Artificial Intelligence Research, and the Journal of Machine Learning (JMLR). She has received numerous awards, including the Presidential Young Investigator Award in 1991, the 2007 Association of Constraint Programming (ACP) research excellence award, and is a Fellow of the ACM since 2013. She has also been Co-Editor-in-Chief of Artificial Intelligence since 2011.

Research topics

• Computer Science
• Artificial Intelligence
• Theoretical computer science
• Mathematics
• Algorithm
• Data Mining
• Mathematical optimization
• Combinatorics

Selected publications

• Search Algorithms for m Best Solutions for Graphical Models

  Proceedings of the AAAI Conference on Artificial Intelligence · 2021 · 27 citations

  1st authorCorresponding

  The paper focuses on finding the m best solutions to combinatorial optimization problems using Best-First or Branchand- Bound search. Specifically, we present m-A*, extending the well-known A* to the m-best task, and prove that all its desirable properties, including soundness, completeness and optimal efficiency, are maintained. Since Best-First algorithms have memory problems, we also extend the memoryefficient Depth-First Branch-and-Bound to the m-best task. We extend both algorithms to optim…

  PublisherDOI

• AND/OR Search Spaces for Graphical Models

  Synthesis lectures on artificial intelligence and machine learning · 2019-01-01 · 18 citations

  book-chapter1st authorCorresponding
  PublisherDOI

• Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms, Second Edition

  Synthesis lectures on artificial intelligence and machine learning · 2019-02-14 · 6 citations

  article1st authorCorresponding
  PublisherDOI

• Submodel Decomposition Bounds for Influence Diagrams

  Proceedings of the AAAI Conference on Artificial Intelligence · 2021 · 4 citations

  Senior authorCorresponding

  Influence diagrams (IDs) are graphical models for representing and reasoning with sequential decision-making problems under uncertainty. Limited memory influence diagrams (LIMIDs) model a decision-maker (DM) who forgets the history in the course of making a sequence of decisions. The standard inference task in IDs and LIMIDs is to compute the maximum expected utility (MEU), which is one of the most challenging tasks in graphical models. We present a model decomposition framework in both IDs and…

  PublisherOA PDFDOI

• Deep Bucket Elimination

  2021 · 4 citations

  Senior authorCorresponding

  Bucket Elimination (BE) is a universal inference scheme that can solve most tasks over probabilistic and deterministic graphical models exactly. However, it often requires exponentially high levels of memory (in the induced-width) preventing its execution. In the spirit of exploiting Deep Learning for inference tasks, in this paper, we will use neural networks to approximate BE. The resulting Deep Bucket Elimination (DBE) algorithm is developed for computing the partition function. We provide a…

  PublisherOA PDFDOI

Recent grants

• RI: Small: Heuristic Search Algorithms for Probabilistic Graphical Models

  NSF · $500k · 2015–2019

• Strategies for High Performance Graph-Based Reasoning

  NSF · $450k · 2004–2008

• RI: High Performance Algorithms for Probabilistic and Deterministic Graphical Models

  NSF · $450k · 2007–2012

Frequent coauthors

• Kalev Kask

  University of California, Irvine

  64 shared

• Robert Mateescu

  Western Digital (United States)

  57 shared

• Vibhav Gogate

  The University of Texas at Dallas

  52 shared

• Radu Marinescu

  49 shared

• Lars Otten

  University of California, Irvine

  40 shared

• Alexander Ihler

  University of California, Irvine

  33 shared

• Judea Pearl

  University of California, Los Angeles

  23 shared

• Bozhena Bidyuk

  19 shared

Education

• Ph.D., Computer Science

  Stanford University

  1980

• M.S., Computer Science

  Stanford University

  1976

• B.S., Mathematics

  University of California, Los Angeles

  1974

Awards & honors

• Presidential Young Investigator Award (1991)
• Fellow of the American Association of Artificial Intelligenc…
• Radcliffe Fellowship (2005-2006)
• 2007 Association of Constraint Programming (ACP) Research Ex…
• Fellow of the ACM (2013)

Similar researchers at University of California, Irvine

• Pierre Baldih-121
• Gene Tsudikh-82
• Padhraic Smythh-78