Assignments

• Assignment #1 (solution)
  Out Jan 7, 2019 due Jan 14, 2019
• Assignment #2 (solution)
  Out Jan 14, 2019 due Jan 23, 2019
• Assignment #3 (solution)
  Out Jan 23, 2019 due Jan 30, 2019
• Assignment #4 (solution)
  Out Feb 18, 2019 due Feb 27, 2019
• Assignment #5
  Out March 6, 2019 due March 13, 2019

Final Project

The final project is worth 30% of your final grade and can be done in teams of 2 or 3. Students should be putting in a substantial amount of effort into the project (about 4-5 weeks worth of work). The project should involve graphical models and will require a major programming effort, in the programming language of choice by the student team.

The project should simulate a research project. This means the project should have a research question or hypothesis that the project investigates. The team must also conduct experiments to test the hypothesis. I consider a project to be worth a B or C grade if you implement an existing algorithm from a paper. To get an A, you must extend the existing work in some "interesting" way that can be reasonably done in 3-4 weeks. The best case scenario is for your class project to become a conference or workshop paper.

The deliverables for the project are as follows:

1. Before the project begins, you will submit a 1 page proposal describing your project.
2. There will be a checkpoint 2 weeks into the project to make sure that you are making progress.
3. A written report. The report should follow the structure of a research paper with the following sections: introduction, related work, methodology, results, discussion, and conclusion. Please use Latex or Word.
4. Any code you used for the project. Please zip and submit to TEACH under the "project" submission.
5. A 15 min oral presentation during exam week.

Projects are due March 20, 2019 midnight. Please slide the writeup under my door (or email the pdf) and submit the code via the TEACH handin.

Project Proposal

Your project proposal should be about 1 page and should have the following:

1. The names and user IDs of the members of your group.
2. 1-2 paragraphs describing what your project is about. In particular, describe what your main objective is and what experiments you will run to demonstrate it.
3. A few sentences describing your deliverables for the midway checkpoint (roughly 2 weeks after your project starts)

Ideas for your final project

If you would like to look for papers related to probabilistic graphical models, take a look at the following conferences and journals:

Conferences: Uncertainty in AI (UAI), International Conference of Machine Learning (ICML), Neural Information Processing Systems (NeurIPS, previously called NIPS), AAAI conference on Artificial Intelligence (AAAI), International Joint Conference on AI (IJCAI), AI and Statistics (AISTATS)

Journals: Journal of Machine Learning Research (JMLR), IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Journal of Artificial Intelligence Research (JAIR)

• Fast learning of mixtures of tree models.
  Learning with Mixtures of Trees by Marina Meila and Michael I. Jordan
• Explore extensions to the work on model averaging of Naive Bayes models.
  References:
  Exact model averaging with naive Bayesian classifiers by Denver Dash and Gregory F. Cooper
  Model averaging for Naive Bayes models. by Ga Wu and Scott Sanner
• Do a "bakeoff" project. A bakeoff project is one that does an empirical comparison between multiple methods related to graphical models. For instance, you could do a bakeoff between different recent approximate inference techniques (note that I said recent here). To get an A, you would need to do a thorough job with the empirical comparison.
• Related to the bakeoff project, you could compare deep learning methods versus probabilistic graphical model methods for some task.
• If you are in both the probaiblistic graphical models and the deep learning class, you can investigate the following papers that fall at the intersection of probabilistic graphical models and deep learning:
  • Auto-encoding variational Bayes by D. P. Kingma and M. Welling
  • Bayesian Generative Adversarial Networks by Y. Saatchi and A. G. Wilson
  • FLOW-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models by A. Grover, M. Dhar and S. Ermon
  • Sum-Product Networks: A New Deep Architecture by H. Poon and P. Domingos
  • More generally, topics related to Bayesian deep learning and variational inference for deep learning
• Reproducibility investigation: Take a recent paper on probabilistic graphical models that has source code available and see if you can reproduce the results. You would also need to propose a new set of experiments to investigate some aspect of the approach that was not in the original paper.
• Explore recent approaches to Marginal MAP calculation. Note that you will need to read about Marginal MAP in your textbook.
  • Maximum Model Counting by D. Fremont, M. N. Rabe, S. A. Seshia.
  • Solving Marginal MAP problems with NP Oracles and XOR Constraints by Y. Xue, Z. Li, S. Ermon, C. P. Gomes and B. Selman.
  • Variational algorithms for marginal MAP by Q. Liu and A. T. Ihler.
• Reinforcement learning: use a probabilistic graphical model to learn a model of the environment (e.g. "Learning and Querying Fast Generatie Models for Reinforcement Learning" by Buesing, Weer, Racaniere, Eslami, Rezende, Reichert, Viola, Besse, Gregor, Hassabis and Wiestra")

Resources

Bayesian network packages

• Bayes Net Toolbox for Matlab
• Bayesian Network tools in Java
• SMILE and GENIE

Data Repositories

• UCI Data Repository
• Multi-Label Datasets

Project Grading

1. Project Checkpoint (5%) (due March 1, 2019 in class)
The project milestone involves handing in a 1 page report describing the milestones met along with any problems encountered and solutions for solving them. You will be handing in any code you've written so far in a zip file and using the handin system for ENGR.

2. Written Report (65%) (due March 20 at 5:00 pm PST)

The written report should be 4-8 pages using the standard templates for AAAI or ICML. The 8 pages is a max limit but references do not count as part of the 8 pages. Your written report should be like a research paper in which you empirically compare your approach against other competitive approaches and discuss the pros and cons. Your written report should have the following sections: Introduction, Related Work, Methodology, Results, Discussion, Conclusion, Bibliography.

When grading your written report, I will be evaluating it as a research paper. This means I will be evaluating it according to the following criteria:

• Novelty (5%): How original is this work? Are you simply re-implementing a paper or are you trying out something that is different or unexplored?
• Motivation/Relevance to the course (5%): How is this project related to Probabilistic Graphical Models? What is the reason for investigating this topic?
• Effort (20%): How much effort went into this project?
• Methodology (15%): What is the research hypothesis of your project? How well do you investigate it?
• Results (15%) : Do the experimental results adequately test this hypothesis?
• Conclusions (5%): What conclusions can be drawn from these experiments? Do the experiments support the hypothesis?

You may submit the pdf for your written report through the TEACH handin system or slide a harcopy under my office door. Please submit your source code through TEACH or provide a Github link.

3. Oral Presentation (30%) (sign up on doodle link below)

The oral presentation will be 15 mins with 5 mins for questions. Every team must sign up for a project presentation during the finals week (March 20-23). Sign up for a 30 min slot on the Doodle link here. When signing up, please put down all the names of your group members. All presentations are in KEC 2057 unless stated otherwise. All group members must be at the presentation unless I have permitted certain absences.

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