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» On Learning Mixtures of Heavy-Tailed Distributions
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ICML
2007
IEEE
14 years 9 months ago
Multi-task reinforcement learning: a hierarchical Bayesian approach
We consider the problem of multi-task reinforcement learning, where the agent needs to solve a sequence of Markov Decision Processes (MDPs) chosen randomly from a fixed but unknow...
Aaron Wilson, Alan Fern, Soumya Ray, Prasad Tadepa...
CDC
2009
IEEE
149views Control Systems» more  CDC 2009»
13 years 9 months ago
Context-dependent multi-class classification with unknown observation and class distributions with applications to bioinformatic
We consider the multi-class classification problem, based on vector observation sequences, where the conditional (given class observations) probability distributions for each class...
Alex S. Baras, John S. Baras
JMLR
2006
137views more  JMLR 2006»
13 years 8 months ago
Bounds for Linear Multi-Task Learning
Abstract. We give dimension-free and data-dependent bounds for linear multi-task learning where a common linear operator is chosen to preprocess data for a vector of task speci...c...
Andreas Maurer
IDA
2009
Springer
14 years 3 months ago
Blind Spectral-GMM Estimation for Underdetermined Instantaneous Audio Source Separation
Abstract. The underdetermined blind audio source separation problem is often addressed in the time-frequency domain by assuming that each time-frequency point is an independently d...
Simon Arberet, Alexey Ozerov, Rémi Gribonva...
IJCV
2002
133views more  IJCV 2002»
13 years 8 months ago
Probabilistic Tracking with Exemplars in a Metric Space
Abstract. A new, exemplar-based, probabilistic paradigm for visual tracking is presented. Probabilistic mechanisms are attractive because they handle fusion of information, especia...
Kentaro Toyama, Andrew Blake