Papers
A Simulation-Based Algorithm for Ergodic Control of Markov Chains Conditioned on Rare Events
Shalabh Bhatnagar, Vivek S. Borkar, Madhukar Akarapu
Incremental Support Vector Learning: Analysis, Implementation and Applications
Pavel Laskov, Christian Gehl, Stefan Krüger et al.
Linear Programming Relaxations and Belief Propagation -- An Empirical Study
Chen Yanover, Talya Meltzer, Yair Weiss
Streamwise Feature Selection
Jing Zhou, Dean P. Foster, Robert A. Stine et al.
Estimating the “Wrong” Graphical Model: Benefits in the Computation-Limited Setting
Martin J. Wainwright
Collaborative Multiagent Reinforcement Learning by Payoff Propagation
Jelle R. Kok, Nikos Vlassis
Learning Factor Graphs in Polynomial Time and Sample Complexity
Pieter Abbeel, Daphne Koller, Andrew Y. Ng
Considering Cost Asymmetry in Learning Classifiers
Francis R. Bach, David Heckerman, Eric Horvitz
Large Scale Transductive SVMs
Ronan Collobert, Fabian Sinz, Jason Weston et al.
Active Learning with Feedback on Features and Instances
Hema Raghavan, Omid Madani, Rosie Jones
Structured Prediction, Dual Extragradient and Bregman Projections
Ben Taskar, Simon Lacoste-Julien, Michael I. Jordan
Kernel-Based Learning of Hierarchical Multilabel Classification Models
Juho Rousu, Craig Saunders, Sandor Szedmak et al.
Efficient Learning of Label Ranking by Soft Projections onto Polyhedra
Shai Shalev-Shwartz, Yoram Singer
Large Scale Multiple Kernel Learning
Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer et al.
Building Support Vector Machines with Reduced Classifier Complexity
S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCoste
Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems
Luca Zanni, Thomas Serafini, Gaetano Zanghirati
Maximum-Gain Working Set Selection for SVMs
Tobias Glasmachers, Christian Igel
Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problems
Tijl De Bie, Nello Cristianini
Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming
Matthias Heiler, Christoph Schnörr
Bayesian Network Learning with Parameter Constraints
Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat Rao
Linear Programs for Hypotheses Selection in Probabilistic Inference Models
Anders Bergkvist, Peter Damaschke, Marcel Lüthi
Ensemble Pruning Via Semi-definite Programming
Yi Zhang, Samuel Burer, W. Nick Street
Second Order Cone Programming Approaches for Handling Missing and Uncertain Data
Pannagadatta K. Shivaswamy, Chiranjib Bhattacharyya, Alexander J. Smola
The Interplay of Optimization and Machine Learning Research
Kristin P. Bennett, Emilio Parrado-Hernández