[Added 8 May 2008]

• Follow-up to Dan's KDD paper on Mixture of Multinomials involving convergence diagnostics. (This one is for Dan.)
• Co-clustering paper explaining a common framework underpinning LDA and the Author-Topic models.
• LDA with credible intervals or at least a multi-sample technique for estimating P(w|z)
• Label switching analysis on LDA
• Length modeling
• Suverying Gibbs samplers (possibly for MM or for LDA):
  • collapsed v. non-collapsed
  • block v. non-block sampler
  • random scan
  • non-collapsed: continuous variables – diagnose using traditional convergence diagnostics
• Keyword identification for documents – build on your work for Data Mining group project involving LDA topics
• Sampling N_d (# of tokens in doc. d) == choosing feature selector in the inference loop
  • choosing dimensionality / cut-off for a fixed feature selector
• Comparison of Bayesian model selection and non-parametric prior

• Re-read E & D. Specifically look for values we can use for candidate, batch, and other parameter settings
  • Re-run all experiments using best values

• Redoing cost model in R
• Code to compute cost (derived columns)
• QBC: fix sampling, code review/Vote entropy
• “Human Waits” Active learning (human only labels 1)
  • Batch but based on previous annotations
• We want the submitted job to run the revision that was current at submission (could pass revision number, or could set aside binaries)
• Write out XML file without results at beginning. Dump results that you have on sigkill before terminating
• Wrap ant script in shell script/python so that it can trap sigkill
• Fast Maxent uses prior

• Phase in cutoffs

• Thesis!!!!
• Prepare slides for Bayesian reading group
• MI for POS-Tagging
• Add MI to LID pipeline

• BYU Graduate Research Fellowship Award: http://www.byu.edu/gradstudies/index.php?action=resources.fellowship&fellowshipid=57
  • Application due around January 2008

• LDA and Co-clustering
• Mutual-Information feature selector for POS-Tagging
• Incorporate Mutual-Information feature selector into LID.

• Research work done at BBN on speech N-best lists from Schwartz, Nguyen, and Austin
• Research work done by Robert Moore on estimating probability distributions using n-best lists

Brainstorming List