New England Machine Learning Day 2013

1. Priors for Diversity in Generative Latent Variable Models by James Zou and Ryan Adams.

2. Generalized Random Utility Models by Hossein Azari, David C. Parkes, and Lirong Xia.

3. Approximate Inference in Collective Graphical Models by Daniel Sheldon, Tao Sun, Akshat Kumar, and Thomas G. Dietterich.

4. Discovering Structure in Spiking Networks by Scott Linderman and Ryan Adams.

5. Poisson Statistics and the Future of Internet Marketing by Delaram Motamedvaziri, Mohammad Hossein Rohban, Venkatesh Saligrama, and David Castanon.

6. Copy or Coincidence? A Model for Detecting Social Influence and Duplication Events by Lisa Friedland, David Jensen, and Michael Lavine.

7. An Impossibility Result for High Dimensional Supervised Learning by M. H. Rohban, P. Ishwar, B. Orten, W. C. Karl, and V. Saligrama.

8. Localizing 3D Cuboids in Single-view Images by Jianxiong Xiao, Bryan C. Russell, and Antonio Torralba.

10. Accelerating Inference: Towards a Full Language, Compiler and Hardware Stack by Lyric Labs – Analog Devices.

11. Efficient Nearest-Neighbor Search in the Probability Simplex by Kriste Krstovski, David A. Smith, Hanna M. Wallach, Andrew McGregor, and Michael J. Kurtz.

12. Image Caption Generation by Rebecca Mason.

13. The Gesture Recognition Toolkit by Nicholas Gillian and Joseph Paradiso.

14. The incidental parameter problem in network analysis for neural spiking data by Dahlia Nadkarni and Matthew Harrison.

15. Knowledge Mining Blood Pressure Data with Dynamic Bayesian Network Modeling by Alex Waldin, Kalyan Veeramachaneni, and Una-May O’Reilly.

16. The network you keep: Graphlet-Based discrimination of persons of interest by Saber Shokat Fadaee, Javed A. Aslam, Nikos Passas, and Ravi Sundaram.

17. Probabilistic reasoning about human edits in information integration by Michael Wick, Ari Kobren, and Andrew McCallum.

18. Spectral Discovery of Clinical Autism Phenotypes with Subspace Regularization by Finale Doshi-Velez, Deniz Oktay, Ben Mayne, and Isaac Kohane.

19. Predicting Age Distribution—A Generative Bayesian Model by Huseyin Oktay, Aykut Firat, and David Jensen.

20. An Improved Message-Passing Algorithm Incorporating Certainty Information by Nate Derbinsky, José Bento Ayres Pereira, Veit Elser, and Jonathan S. Yedidia.

21. A New Geometric Approach to Latent Topic Modeling and Discovery by Weicong Ding, Mohammad H. Rohban, Prakash Ishwar, and Venkatesh Saligrama.

22. Coco-Q: Learning in Stochastic Games with Side Payments by Eric Sodomka, Elizabeth Hilliard, Amy Greenwald, and Michael Littman.

23. Modeling Clinical Prognosis by Learning Interpretable Representations from Massive Health Data by Rohit Joshi and Peter Szolovits.

24. An Efficient Atomic Norm Minimization Approach to Identification of Low Order Models by Burak Yilmaz, Constantino Lagoa, and Mario Sznaier.

25. Agglomerative Clustering of Bagged Data Using Joint Distributions by David Arbour, James Atwood, Ahmed El-Kishky, and David Jensen.

26. Hankel Based Maximum Margin Classifiers: A Connection Between Machine Learning and Wiener Systems Identification by Fei Xiong, Yongfang Cheng, Octavia Camps, Mario Sznaier, and
Constantino Lagoa.

27. Fitting Large-Scale GLMs with Implicit Updates by Panos Toulis, Jason Rennie, and Edo Airoldi.

28. Automatic delineation of radiosensitive structures in CT images using statistical appearance models and level sets by Karl D. Fritscher and Gregory Sharp.

29. Topic-Partitioned Multinetwork Embeddings by Peter Krafft, Juston Moore, Bruce Desmarais, and Hanna Wallach.

30. Evaluating Crowdsourcing Participants in the Absence of Ground-Truth by Ramanathan Subramanian, Romer Rosales, Glenn Fung, and Jennifer Dy.

31. Nonparametric Mixture of Gaussian Processes with Constraints by James C. Ross.

32. Sparse Signal Processing with Linear and Non-Linear Observations: A Unified Shannon Theoretic Approach by Cem Aksoylar, George Atia, and Venkatesh Saligrama.

33. More Efficient Dual Decomposition for Corpus Wide Inference by Alexandre Passos, David Belanger, Sebastian Riedel, and Andrew McCallum.

34. Learning with Irregularly Sampled Time Series Data by Steve Cheng-Xian Li and Benjamin M. Marlin.

35. Batch-iFDD for Representation Expansion in Large MDPs by Alborz Geramifard, Tom Walsh, Nicholas Roy, and Jonathan How.

36. Leveraging Hierarchical Structure in Diagnostic Codes for Predicting Incident Heart Failure by Anima Singh and John Guttag.

37. Layered Model for Video Analysis by Deqing Sun, Jonas Wulff, Erik B. Sudderth, Hanspeter Pfister, and Michael J. Black.

38. FlexGP: a Divide and Conquer Approach to Machine Learning on the Cloud by Kalyan Veeramachaneni, Owen Derby, Dylan Sherry, and Una-May O’Reilly.

39. Density Estimation and Anomaly Detection Using the Relevance Vector Machine by Jose Lopez.

40. Reasoning about Independence in Probabilistic Models of Relational Data by Marc Maier, Katerina Marazopoulou, and David Jensen.

41. On a Particle-Stabilized Wang-Landau Algorithm by Luke Bornn, Pierre Jacob, Arnaud Doucet, and Pierre Del Moral.

42. Posterior Consistency for the Number of Components in a Finite Mixture by Jeffrey W. Miller and Matthew T. Harrison.