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NIPS
2008
9 years 5 months ago
An interior-point stochastic approximation method and an L1-regularized delta rule
The stochastic approximation method is behind the solution to many important, actively-studied problems in machine learning. Despite its farreaching application, there is almost n...
Peter Carbonetto, Mark Schmidt, Nando de Freitas
NIPS
2008
9 years 5 months ago
Online Optimization in X-Armed Bandits
Sébastien Bubeck, Rémi Munos, Gilles...
NIPS
2008
9 years 5 months ago
Mortal Multi-Armed Bandits
We formulate and study a new variant of the k-armed bandit problem, motivated by e-commerce applications. In our model, arms have (stochastic) lifetime after which they expire. In...
Deepayan Chakrabarti, Ravi Kumar, Filip Radlinski,...
NIPS
2008
9 years 5 months ago
Local Gaussian Process Regression for Real Time Online Model Learning
Learning in real-time applications, e.g., online approximation of the inverse dynamics model for model-based robot control, requires fast online regression techniques. Inspired by...
Duy Nguyen-Tuong, Matthias Seeger, Jan Peters
NIPS
2008
9 years 5 months ago
Scalable Algorithms for String Kernels with Inexact Matching
We present a new family of linear time algorithms based on sufficient statistics for string comparison with mismatches under the string kernels framework. Our algorithms improve t...
Pavel P. Kuksa, Pai-Hsi Huang, Vladimir Pavlovic
NIPS
2008
9 years 5 months ago
The Recurrent Temporal Restricted Boltzmann Machine
The Temporal Restricted Boltzmann Machine (TRBM) is a probabilistic model for sequences that is able to successfully model (i.e., generate nice-looking samples of) several very hi...
Ilya Sutskever, Geoffrey E. Hinton, Graham W. Tayl...
NIPS
2008
9 years 5 months ago
Kernel Change-point Analysis
We introduce a kernel-based method for change-point analysis within a sequence of temporal observations. Change-point analysis of an unlabelled sample of observations consists in,...
Zaïd Harchaoui, Francis Bach, Eric Moulines
NIPS
2008
9 years 5 months ago
Designing neurophysiology experiments to optimally constrain receptive field models along parametric submanifolds
Sequential optimal design methods hold great promise for improving the efficiency of neurophysiology experiments. However, previous methods for optimal experimental design have in...
Jeremy Lewi, Robert J. Butera, David M. Schneider,...
NIPS
2008
9 years 5 months ago
Simple Local Models for Complex Dynamical Systems
We present a novel mathematical formalism for the idea of a "local model" of an uncontrolled dynamical system, a model that makes only certain predictions in only certai...
Erik Talvitie, Satinder Singh
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