PPT-Advancing Heuristics for Search over Graphical Models

William Lam Final Defense March 16 2017 Committee Rina Dechter Alexander Ihler Sameer Singh Collaborators Rina Dechter Kalev Kask Javier Larrosa Alexander Ihler Thesis Contributions. This lecture topic. Read Chapter 3.5-3.7. Next lecture topic. Read Chapter 4.1-4.2. (Please read lecture topic material . before. and . after each lecture on that topic). You will be expected to know. Idea: give the algorithm “hints” about the desirability of different states . Use an . evaluation function. . to rank nodes and select the most promising one for expansion. Greedy best-first search. Initialize. . the . frontier . using the . starting state. While the frontier is not empty. Choose a frontier node to expand according to . search strategy . and take it off the frontier. If the node contains the . Graphical Model Inference. View observed data and unobserved properties as . random variables. Graphical Models: compact graph-based encoding of probability distributions (high dimensional, with complex dependencies). Presenter: . Mati. Bot. Course: Advance Seminar in Algorithms (Prof. . Yefim. . Dinitz. ). Human-Competitiveness Definition. Evolving Hyper-Heuristics using Genetic . Programming . Rush-Hour (bronze . Initialize. . the . frontier . using the . starting state. While the frontier is not empty. Choose a frontier node to expand according to . search strategy . and take it off the frontier. If the node contains the . Tamara L Berg. CSE 595 Words & Pictures. Announcements. HW3 . online tonight. Start thinking about project ideas . Project . proposals in class Oct 30 . . Come to office hours . Oct. 23-25 . to discuss . Automated Reasoning with Graphical models. Rina. Dechter. Bren school of ICS. University of California, Irvine. ICS 90 . November 2016. Agenda. My work in AI. How did I get to AI?. 2. ICS-90, 2016. Knowledge representation and Reasoning. Generalized covariance matrices and their inverses. Menglong Li. Ph.d. of Industrial Engineering. Dec 1. st. 2016. Outline. Recap: Gaussian graphical model. Extend to general graphical model. Model setting. Qi Lou, Rina . Dechter. , Alexander . Ihler. Feb. 8, 2017. 1. Guideline. Anytime bounds for the partition function of a graphical model.. Estimate (bound) the partition function as a heuristic search problem on AND/OR search trees. Kenny Denmark. Jason Isenhower. Ross Roessler. Background. A* uses heuristics for efficient searching. Initially, heuristics provided by "expert". Challenge is to have program create heuristics. Comparison of Strategies for Scalable Causal Discovery of Latent Variable Models from Mixed Data Vineet Raghu , Joseph D. Ramsey, Alison Morris, Dimitrios V. Manatakis, Peter Spirtes, Panos K. Chrysanthis, Clark Glymour, and Panayiotis V. Benos Asst. . Prof.. . Dr.. Ahmet ÜNVEREN. 20. 20. -20. 21. . FALL. . Dr. . Ünveren. 1. Practical Issues . The lecturer. Asst. . Prof.. . Dr.. Ahmet ÜNVEREN . E-mail: . ahmet. .unveren@emu.edu.tr. Part 1: Overview and Applications . Outline. Motivation for Probabilistic Graphical Models. Applications of Probabilistic Graphical Models. Graphical Model Representation. Probabilistic Modeling. 1. when trying to solve a real-world problem using mathematics, it is common to define a mathematical model of the world, e.g..