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COLT
2010
Springer
14 years 7 months ago
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
We present a new family of subgradient methods that dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradie...
John Duchi, Elad Hazan, Yoram Singer
ECML
2007
Springer
15 years 3 months ago
Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling
In this paper we investigate the relation between transfer learning in reinforcement learning with function approximation and supervised learning with concept drift. We present a n...
Jan Ramon, Kurt Driessens, Tom Croonenborghs
ICALP
1998
Springer
15 years 1 months ago
A Neuroidal Architecture for Cognitive Computation
An architecture is described for designing systems that acquire and manipulate large amounts of unsystematized, or so-called commonsense, knowledge. Its aim is to exploit to the fu...
Leslie G. Valiant
ICML
2005
IEEE
15 years 10 months ago
New d-separation identification results for learning continuous latent variable models
Learning the structure of graphical models is an important task, but one of considerable difficulty when latent variables are involved. Because conditional independences using hid...
Ricardo Silva, Richard Scheines
NIPS
2008
14 years 11 months ago
Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text
In this paper, we address the question of what kind of knowledge is generally transferable from unlabeled text. We suggest and analyze the semantic correlation of words as a gener...
Yi Zhang 0010, Jeff Schneider, Artur Dubrawski