AISTATS*07 Poster Session 1

Thursday March 22

Learning Markov Structure by Maximum Entropy Relaxation
- Jason Johnson, Venkat Chandrasekaran, and Alan Willsky
Hierarchical Beta Processes and the Indian Buffet Process
- Romain Thibaux and Michael I. Jordan
Generalized Non-metric Multidimensional Scaling
- Sameer Agarwal, Josh Wills, Lawrence Cayton, Gert Lanckriet, Serge Belongie, and David Kriegman
Information Retrieval by Inferring Implicit Queries from Eye Movements
- David R. Hardoon, John Shawe-Taylor, Antti Ajanki, Kai Puolamaki, and Samuel Kaski
Approximate Counting of Graphical Models Via MCMC
- Jose M Pena
Learning Multilevel Distributed Representations for High Dimensional Sequences
- Ilya Sutskever and Geoffrey Hinton
Learning for Larger Datasets with the Gaussian Process Latent Variable Model
- Neil Lawrence
Fast Low-Rank Semidefinite Programming for Embedding and Clustering
- Brian Kulis, Arun Surendran, and John C. Platt
Multi-object tracking with representations of the symmetric group
- Risi Kondor, Andrew Howard, and Tony Jebara
Minimum Volume Embedding
- Blake Shaw and Tony Jebara
Exact Bayesian structure learning from uncertain interventions
- Daniel Eaton and Kevin Murphy
Efficient large margin semisupervised learning
- Junhui Wang
Using two-stage conditional word frequency models to model word burstiness and motivating TF-IDF
- Peter Sunehag
AClass: A simple, online, parallelizable algorithm for probabilistic classification
- Vikash Mansinghka, Daniel Roy, Ryan Rifkin, and Josh Tenenbaum
Dissimilarity in Graph-Based Semi-Supervised Classification
- Andrew Goldberg, Xiaojin Zhu, and Steve Wright
The Kernel Path in Kernelized LASSO
- Gang Wang, Dit-Yan Yeung, and Frederick Lochovsky
Policy-Gradients for PSRs and POMDPs
- Douglas Aberdeen, Olivier Buffet, and Owen Thomas
The Rademacher Complexity of Co-Regularized Kernel Classes
- David Rosenberg and Peter Bartlett