NIPS papers are out

For a full list see here. Some papers I want to read based on my current interests:

Random Projections for Manifold Learning
Chinmay Hegde, Michael Wakin, Richard Baraniuk

The Distribution Family of Similarity Distances
Gertjan Burghouts, Arnold Smeulders, Jan-Mark Geusebroek

Manifold Sculpting
Michael Gashler, Dan Ventura, Tony Martinez

A learning framework for nearest neighbor search
Lawrence Cayton, Sanjoy Dasgupta

Learning Bounds for Domain Adaptation
John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman

Convex Relaxations of EM
Yuhong Guo, Dale Schuurmans

A Randomized Algorithm for Large Scale Support Vector Learning
Krishnan Kumar, Chiru Bhattacharya, Ramesh Hariharan

Bundle Methods for Machine Learning
Alex Smola, S V N Vishwanathan, Quoc Le

Regularized Boost for Semi-Supervised Learning
Ke Chen, Shihai Wang

Learning the structure of manifolds using random projections
Yoav Freund, Sanjoy Dasgupta, Mayank Kabra, Nakul Verma

A complexity measure for intuitive theories
Charles Kemp, Noah Goodman, Joshua Tenenbaum