CPSC 532 - Topics in AI:
Probabilistic graphical models, knowledge graphs and statistical-relational AI

Here is a tentative schedule. Each week, 2 students will jointly present the paper. All papers are freely available for us, but you might need to use a UBC VPN.

• Jan 13 - Graphical Models foundations: Probability, Independence, graphical models Slides: lect1.pdf lect2.pdf
• Jan 12 - Assignment 0 due
• Jan 15 - Belief networks and other graphical models (cont). lect3.pdf
• Jan 20 Discussion papers (AI and probability):
  • Zoubin Ghahramani. Probabilistic machine learning and artificial intelligence. Nature, 521:452-459, 2015, doi doi:10.1038/nature14541.
• Jan 22 Probabilistic Inference: Exact. Slides: lect7.pdf lect4.pdf
• Jan 27 - Markov Chains, and stochastic simulation. Slides: lect5.pdf Discussion paper (probabilistic inference):
  • Tian Sang, Paul Beame, and Henry A. Kautz. Performing Bayesian inference by weighted model counting. In AAAI, 2005.
• Jan 29 - Stochastic simulation (cont). Learning Probabilsitc Models. Slides: lect6.pdf ML.pdf.
• Feb 3 Discussion paper (causality)
  • Judea Pearl and Elias Bareinboim: External Validity: From Do-Calculus to Transportability Across Populations. Statistical Science 2014, Vol. 29, No. 4.
• Feb 5 knowledge graphs, maxrix factorization for recommended systems. slides: Poole_talk.pdf lect4.pdf. Wikidata. AIPython (see Chapter 13).
• Feb 10 Discussion paper (knowledge graphs)
  • Seyed Mehran Kazemi and David Poole, SimplE Embedding for Link Prediction in Knowledge Graphs, in Proc. Thirty-second Conference on Neural Information Processing Systems (NeurIPS 2018).
• Feb 12. Learning knowledge graph overview. FB15k relations (and translations of some of test set): Sentences2.txt. Slides
• Feb 12 - see Wiki for February Assignment/Project.
• Feb 24 Discussion paper (everyone reads and 2 people act as discussion leaders):
  • Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings by Hongyu Ren, Weihua Hu, Jure Leskovec (ICLR 2020)
• Mar 3. See Piazza.
• Mar 5: Discussion paper (everyone reads and 2 people act as discussion leaders):
  • Modeling Relational Data with Graph Convolutional Networks by Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling ESWC 2018.
• March 5 - see Wiki for March Assignment/Project. Choose papers.
• Mar 10: Discussion paper (everyone reads and 2 people act as discussion leaders):
  • End-to-End Differentiable Proving by Tim Rocktaschel, Sebastian Riedel (NIPS 2017)
• Mar 17 - recruiting talk
• Mar 19 - relational probabilistic models. Slides: lect3.pdf
• Mar 24 - (exact) lifted inference
• March 26
  • Mathias Niepert and Guy Van den Broeck, lifted MCMC 2021.
• March 31
  • Incremental Knowledge Base Construction Using DeepDive Sen Wu, Ce Zhang, Christopher De Sa, Jaeho Shin, Feiran Wang, and C. Re. VLDB. 2015
• April 9
  • Learning Systems of Concepts with an Infinite Relational Model Charles Clark, Joshua B Tenenbaum, Thomas L Griffiths, Takeshi Yamada, Naonori Ueda.

Last updated: 2021-03-22 David Poole