1227 search results - page 23 / 246 » Learning Rates for Q-Learning |
RECSYS
2009
ACM
15 years 4 months ago
2009
ACM
In the Netflix Prize competition many new collaborative filtering (CF) approaches emerged, which are excellent in optimizing the RMSE of the predictions. Matrix factorization (M...
NIPS
1998
14 years 11 months ago
1998
In this paper, we address two issues of long-standing interest in the reinforcement learning literature. First, what kinds of performance guarantees can be made for Q-learning aft...
64
Voted
ICML
2005
IEEE
15 years 10 months ago
2005
IEEE
We investigate the empirical applicability of several bounds (a number of which are new) on the true error rate of learned classifiers which hold whenever the examples are chosen ...
JMLR
2010
14 years 4 months ago
2010
We study the problem of active learning with convex loss functions. We prove that even under bounded noise constraints, the minimax rates for proper active learning are often no b...
92
Voted
UAI
2004
14 years 11 months ago
2004
Collaborative filtering is a useful technique for exploiting the preference patterns of a group of users to predict the utility of items for the active user. In general, the perfo...