We propose a new approach to reinforcement learning which combines least squares function approximation with policy iteration. Our method is model-free and completely off policy. ...
It has been observed that traditional decision trees produce poor probability estimates. In many applications, however, a probability estimation tree (PET) with accurate probabilit...
Probabilistic latent semantic indexing (PLSI) represents documents of a collection as mixture proportions of latent topics, which are learned from the collection by an expectation...
Alexander Hinneburg, Hans-Henning Gabriel, Andr&eg...
We propose a privacy-preserving protocol for computing aggregation queries over the join of private tables. In this problem, several parties wish to share aggregated information ov...
We address the problem of learning topic hierarchies from data. The model selection problem in this domain is daunting—which of the large collection of possible trees to use? We...
David M. Blei, Thomas L. Griffiths, Michael I. Jor...