List of project papers for STAT 539

Diversity priors
• Near-Optimal MAP Inference for Determinantal Point Processes, J. Gillenwater, A. Kulesza, and B. Taskar. Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, December 2012. (most of these papers can be found on Ben Taskar's home page)
• Learning Determinantal Point Processes, A. Kulesza, and B. Taskar. Conference on Uncertainty in Artificial Intelligence (UAI), Barcelona, Spain, July 2011.
• k-DPPs: Fixed-Size Determinantal Point Processes, A. Kulesza, and B. Taskar. International Conference on Machine Learning (ICML), Bellevue, WA, June 2011.
• Structured Determinantal Point Processes, A. Kulesza, and B. Taskar. Neural Information Processing Systems Conference (NIPS), Vancouver, BC, December 2010. (Warning: complex experiments!)
• Resources
  • Determinantal Point Processes for Machine Learning, A. Kulesza and B. Taskar. Foundations and Trends in Machine Learning: Vol. 5, No 2-3, December 2012. (arXiv version) an extended paper containing the three above papers plus a lot of background.
  • Overview of DPPs slides
• Large-Margin Determinantal Point Processes Boqing Gong, Wei-lun Chao, Kristen Grauman, Fei Sha
• Chris Priors for Diversity in Generative Latent Variable Models Zou, JY, Adams RP. Advances in Neural Information Processing Systems 25. 2012.
• Submodular point processes Rishabh Iyer and Jeff Bilmes

  Network models and community detection

• Roy Spectral clustering and the high-dimensional stochastic blockmodel Karl Rohe, Sourav Chatterjee, and Bin Yu, Annals of Statistiscs, Volume 39, Number 4 (2011), 1878-1915. (You are not responsible for the proof details in this paper, only for understanding the results and the assumptions.)
• Justin Latent Multi-group Membership Graph Model by M. Kim, J. Leskovec. International Conference on Machine Learning (ICML), 2012.
• Alex Learning Social Infectivity in Sparse Low-rank Networks Using Multi-dimensional Hawkes Processes by Ke Zhou, Le Song, Hongyuan Zha, 2013.
  This is a follow-up on Inferring Networks of Diffusion and Influence by M. Gomez-Rodriguez, J. Leskovec, A. Krause. ACM Transactions on Knowledge Discovery from Data (TKDD), 2012.

  Models for rank data and learning to rank

• Alec The asymptotics of ranking algorithms John C. Duchi, Lester Mackey, and Michael I. Jordan, Ann. Statist. Volume 41, Number 5 (2013), 2292-2323.
• Non-parametric Modeling of Partially Ranked Data G. Lebanon and Y. Mao. Journal of Machine Learning Research 9(Oct):2401-2429, 2008.
• Wesley Bayesian inference for Plackett-Luce ranking models John Guiver and Edward Snelson, ICML 2009
• Corinne An exponential family model over infinite rankings by Marina Meila and Le Bao, Journal of Machine Learning Research, 10:3481--3518, 2010.
• Luca Recursive Inversion Models for Permutations Christopher Meek, Marina Meila NIPS 2014: 631-639
• Background papers on models for rank data
  • Fligner, M.A., and J.S. Verducci. "Distance Based Ranking Models." Journal of the Royal Statistical Society, B 48, no. 3 (1986): 359--369. (original Generalized Mallows paper)
  • Fligner, Michael A., and Joseph S. Verducci. "Multistage Ranking Models." Journal of the American Statistical Association 83, no. 403 (1988): 892- 901 (Generalized Mallows - some more results)
  • a bibliography
  • Csiszar, V. "Markov Bases of Conditional Independence Models for Permutations." Kybernetika 45, no. 2 (2009).??
  • Huang, Jonathan, and Carlos Guestrin. "Uncovering the Riffled Independence structure of rankings." Electronic Journal of Statistics 6 (2012): 1999-230. (precursor to RIM models -- note there are 2 more follow-up papers by the same authors on this topic)
  • (Huang, Jonathan, Carlos Guestrin, and Leonidas Guibas. "Fourier Theoretic Probabilistic Inference Over Permutations." Journal of Machine Learning Research 10 (2009): 997-1070. This is not quite background, but gives some perspective on the previous paper.)
  • Hunter, David. "MM Algorithms for Generalized Bradley-Terry Models." The Annals of Statistics384-406 32, no. 1 (2004): 384-406. (relates to the Plackett-Luce paper)
  • Technical Report no. 515 Consensus Ranking Under the Exponential Model Marina Meila, Kapil Phadnis, Arthur Patterson and Jeff Bilmes April 2007 (Generalized Mallows)
• J. He, H. Tong, Q. Mei, and B. Szymanski. Gender: A generic diversified ranking algorithm. In Neural Information Processing Systems (NIPS), pages 1151-1159, 2012 (See also H. Tong, J. He, Z. Wen, and C.Y. Lin. Diversified Ranking on Large Graphs: an Optimization Viewpoint. KDD 2011.)
• Learning to Rank with Nonsmooth Cost Functions Christopher J .C. Burges, Robert Ragno and Quoc Viet Le. See also From RankNet to LambdaRank to LambdaMART: An OverviewChristopher J.C. Burges. (A project on this topic should include some material from the second paper as well).
• The asymptotics of ranking algorithms John Duchi, Lester Mackey, Michael Jordan The Annals of Statistics 2013, Vol. 41, No. 5, 2292–2323. See also conference paper and slides here. (You are not responsible for the proof details in this paper, only for understanding the results and the assumptions.)