CSCI 8610 Topics in Theoretical Computer Science (Spring 2018)

Probabilistic Networks: Randomness, Learning, and Algorithms


Instructor : Liming Cai
Office: 544 Boyd
Phone : 2-6081
Email : cai@cs.uga.edu
Lecture hours: 12:20 - 1:10M and 12:30 - 1:45 TR
Classrooms: 306 Boyd (M) and Forest Resources-1 0303 (TR)
Office Hours: 1:15-2:15 (Mondays), 9:45-10:45 (Tuesdays) or by appointment


Objectives

Prerequisites

Contents

Format and requirements

Reference books

  1. D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009
  2. J. Pearl, Causality: Models, Reasoning, and Inference, Cambridge University Press, 2009
  3. R. Durbin, S. Eddy, A. Krogh, and G. Mitchinson, Biological sequence analysis. Probabilistic models of proteins and nucleic acids, Cambridge University Press, 1998.

Some papers of interest

  1. Minsky, Music, Mind, and Meaning (1982)
  2. Balduzzi, Semantics, Representations and Grammars for Deep Learning (2015)
  3. Choi et al, Learning Latent Tree Graphical Models
  4. Mohammadi et al, Generalized Permutohedra from Probabilistic Graphical Models (2016)
  5. Mukherjee and Basu, Lower bounds over Boolean inputs for deep neural networks with ReLU gates (2017)
  6. Hamanaka et al, DeepGTTM-II: Automatic Generation of Metrical Structure based on Deep Learning Technique, (2016)
  7. Wainwright and Jordan, Graphical Models, Exponential Families, and Variational Inference, (2008)
  8. Arora et al, A Practical Algorithm for Topic Modeling with Provable Guarantees, (2012)
  9. Galas et al, Expansion of the Kullback-Leibler Divergence, and a new class of information metrics, (2017)
  10. Liang et al, Efficient Learning of Optimal Markov Network Topology with k-Tree Modeling, (2018)
  11. Liang et al, Stochastic k-Tree Grammar and Its Application in Biomolecular Structure Modeling, (2014)
  12. Collin, Probabilistic Context-Free Grammars (PCFGs), (2011)
Music as languages

Research Presentations and Names

Academic Dishonesty: