Papers
Efficient Structure Learning of Markov Networks using L_1-Regularization
Su-in Lee, Varun Ganapathi, Daphne Koller
Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension
Manfred K. Warmuth, Dima Kuzmin
Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees
Konrad Rieck, Pavel Laskov, Sören Sonnenburg
A Nonparametric Approach to Bottom-Up Visual Saliency
Wolf Kienzle, Felix A. Wichmann, Matthias O. Franz et al.
Correcting Sample Selection Bias by Unlabeled Data
Jiayuan Huang, Arthur Gretton, Karsten Borgwardt et al.
Learning to parse images of articulated bodies
Deva Ramanan
Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds
Benjamin I. Rubinstein, Peter L. Bartlett, J. H. Rubinstein
Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields
Chi-hoon Lee, Shaojun Wang, Feng Jiao et al.
Manifold Denoising
Matthias Hein, Markus Maier
Support Vector Machines on a Budget
Ofer Dekel, Yoram Singer
Chained Boosting
Christian R. Shelton, Wesley Huie, Kin F. Kan
Image Retrieval and Classification Using Local Distance Functions
Andrea Frome, Yoram Singer, Jitendra Malik
Mutagenetic tree Fisher kernel improves prediction of HIV drug resistance from viral genotype
Tobias Sing, Niko Beerenwinkel
A Humanlike Predictor of Facial Attractiveness
Amit Kagian, Gideon Dror, Tommer Leyvand et al.
Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing
Yuanhao Chen, Long Zhu, Alan L. Yuille
The Neurodynamics of Belief Propagation on Binary Markov Random Fields
Thomas Ott, Ruedi Stoop
Recursive ICA
Honghao Shan, Lingyun Zhang, Garrison W. Cottrell
An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments
Michael I. Mandel, Daniel P. Ellis, Tony Jebara
Parameter Expanded Variational Bayesian Methods
Tommi S. Jaakkola, Yuan Qi
An Application of Reinforcement Learning to Aerobatic Helicopter Flight
Pieter Abbeel, Adam Coates, Morgan Quigley et al.
Context Effects in Category Learning: An Investigation of Four Probabilistic Models
Michael Mozer, Michael Shettel, Michael P. Holmes
Approximate inference using planar graph decomposition
Amir Globerson, Tommi S. Jaakkola
A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems
Yoshinobu Kawahara, Takehisa Yairi, Kazuo Machida
Causal inference in sensorimotor integration
Konrad P. Körding, Joshua B. Tenenbaum
Learning to be Bayesian without Supervision
Martin Raphan, Eero P. Simoncelli