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ML
2002
ACM
154views Machine Learning» more  ML 2002»
14 years 9 months ago
Technical Update: Least-Squares Temporal Difference Learning
TD() is a popular family of algorithms for approximate policy evaluation in large MDPs. TD() works by incrementally updating the value function after each observed transition. It h...
Justin A. Boyan
ICDM
2005
IEEE
185views Data Mining» more  ICDM 2005»
15 years 3 months ago
Adaptive Product Normalization: Using Online Learning for Record Linkage in Comparison Shopping
The problem of record linkage focuses on determining whether two object descriptions refer to the same underlying entity. Addressing this problem effectively has many practical ap...
Mikhail Bilenko, Sugato Basu, Mehran Sahami
NIPS
2007
14 years 11 months ago
A Game-Theoretic Approach to Apprenticeship Learning
We study the problem of an apprentice learning to behave in an environment with an unknown reward function by observing the behavior of an expert. We follow on the work of Abbeel ...
Umar Syed, Robert E. Schapire
ICRA
2009
IEEE
227views Robotics» more  ICRA 2009»
15 years 4 months ago
Adaptive autonomous control using online value iteration with gaussian processes
— In this paper, we present a novel approach to controlling a robotic system online from scratch based on the reinforcement learning principle. In contrast to other approaches, o...
Axel Rottmann, Wolfram Burgard
SIGIR
2010
ACM
15 years 1 months ago
Learning more powerful test statistics for click-based retrieval evaluation
Interleaving experiments are an attractive methodology for evaluating retrieval functions through implicit feedback. Designed as a blind and unbiased test for eliciting a preferen...
Yisong Yue, Yue Gao, Olivier Chapelle, Ya Zhang, T...