Papers
The Interplay of Optimization and Machine Learning Research
Kristin P. Bennett, Emilio Parrado-Hernández
The Iterated Sigma Point Kalman Filter with Applications to Long Range Stereo
G. Sibley, G. Sukhatme, L. Matthies
The Neurodynamics of Belief Propagation on Binary Markov Random Fields
Thomas Ott, Ruedi Stoop
Theory and Dynamics of Perceptual Bistability
Paul R. Schrater, Rashmi Sundareswara
The Robustness-Performance Tradeoff in Markov Decision Processes
Huan Xu, Shie Mannor
Tighter PAC-Bayes Bounds
Amiran Ambroladze, Emilio Parrado-hernández, John S. Shawe-taylor
Toward Attribute Efficient Learning of Decision Lists and Parities
Adam R. Klivans, Rocco A. Servedio
Towards a general independent subspace analysis
Fabian J. Theis
Training Conditional Random Fields for Maximum Labelwise Accuracy
Samuel S. Gross, Olga Russakovsky, Chuong B. Do et al.
Uncertainty, phase and oscillatory hippocampal recall
Máté Lengyel, Peter Dayan
Unified Inference for Variational Bayesian Linear Gaussian State-Space Models
David Barber, Silvia Chiappa
Unified Inverse Depth Parametrization for Monocular SLAM
J. Montiel, J. Civera, A. Davison
Universal Kernels
Charles A. Micchelli, Yuesheng Xu, Haizhang Zhang
Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing
Yuanhao Chen, Long Zhu, Alan L. Yuille
Unsupervised Regression with Applications to Nonlinear System Identification
Ali Rahimi, Ben Recht
Using Combinatorial Optimization within Max-Product Belief Propagation
Daniel Tarlow, Gal Elidan, Daphne Koller et al.
Using Machine Learning to Guide Architecture Simulation
Greg Hamerly, Erez Perelman, Jeremy Lau et al.
Walk-Sums and Belief Propagation in Gaussian Graphical Models
Dmitry M. Malioutov, Jason K. Johnson, Alan S. Willsky
Worst-Case Analysis of Selective Sampling for Linear Classification
Nicoló Cesa-Bianchi, Claudio Gentile, Luca Zaniboni
A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior
Hal Daumé III, Daniel Marcu
A Classification Framework for Anomaly Detection
Ingo Steinwart, Don Hush, Clint Scovel
Active Coevolutionary Learning of Deterministic Finite Automata
Josh Bongard, Hod Lipson
Active Learning For Outdoor Obstacle Detection
Cristian Dima, Martial Hebert
Active Learning to Recognize Multiple Types of Plankton
Tong Luo, Kurt Kramer, Dmitry B. Goldgof et al.