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» Machine-Learning Applications of Algorithmic Randomness
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ICML
2010
IEEE
14 years 7 months ago
Implicit Online Learning
Online learning algorithms have recently risen to prominence due to their strong theoretical guarantees and an increasing number of practical applications for large-scale data ana...
Brian Kulis, Peter L. Bartlett
ICML
2009
IEEE
15 years 10 months ago
Learning structurally consistent undirected probabilistic graphical models
In many real-world domains, undirected graphical models such as Markov random fields provide a more natural representation of the dependency structure than directed graphical mode...
Sushmita Roy, Terran Lane, Margaret Werner-Washbur...
94
Voted
ICML
2010
IEEE
14 years 10 months ago
Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate
Restricted Boltzmann Machines (RBMs) are a type of probability model over the Boolean cube {-1, 1}n that have recently received much attention. We establish the intractability of ...
Philip M. Long, Rocco A. Servedio
103
Voted
ALT
2002
Springer
15 years 6 months ago
Optimally-Smooth Adaptive Boosting and Application to Agnostic Learning
We describe a new boosting algorithm that is the first such algorithm to be both smooth and adaptive. These two features make possible performance improvements for many learning ...
Dmitry Gavinsky
66
Voted
ICML
2009
IEEE
15 years 10 months ago
Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery
We develop a cyclical blockwise coordinate descent algorithm for the multi-task Lasso that efficiently solves problems with thousands of features and tasks. The main result shows ...
Han Liu, Mark Palatucci, Jian Zhang