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» On Learning Boolean Functions
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ICML
2000
IEEE
16 years 7 months ago
On-line Learning for Humanoid Robot Systems
Humanoid robots are high-dimensional movement systems for which analytical system identification and control methods are insufficient due to unknown nonlinearities in the system s...
Gaurav Tevatia, Jörg Conradt, Sethu Vijayakum...
KDD
2008
ACM
172views Data Mining» more  KDD 2008»
16 years 7 months ago
Structured metric learning for high dimensional problems
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
Jason V. Davis, Inderjit S. Dhillon
ECML
2004
Springer
16 years 10 hour ago
Batch Reinforcement Learning with State Importance
Abstract. We investigate the problem of using function approximation in reinforcement learning where the agent’s policy is represented as a classifier mapping states to actions....
Lihong Li, Vadim Bulitko, Russell Greiner
STOC
1993
ACM
117views Algorithms» more  STOC 1993»
15 years 10 months ago
Efficient noise-tolerant learning from statistical queries
In this paper, we study the problem of learning in the presence of classification noise in the probabilistic learning model of Valiant and its variants. In order to identify the cl...
Michael J. Kearns
STOC
2007
ACM
136views Algorithms» more  STOC 2007»
16 years 7 months ago
Exponential separations for one-way quantum communication complexity, with applications to cryptography
We give an exponential separation between one-way quantum and classical communication protocols for two partial Boolean functions, both of which are variants of the Boolean Hidden...
Dmitry Gavinsky, Julia Kempe, Iordanis Kerenidis, ...