The cover tree data structure speeds up exact nearest neighbor queries over arbitrary metric
spaces. This paper makes cover trees even faster. In particular, we provide (1) a simpler …

Purpose Sampling an action according to the probability that the action is believed to be the
optimal one is sometimes called Thompson sampling. Methods Although mostly applied to …

This paper describes a new information-theoretic policy evaluation technique for
reinforcement learning. This technique converts any compression or density model into a …

This paper investigates the use of reinforcement learning for the path planning of an
autonomous triangular marine platform in unknown environments under various …

The purpose of this book is to collect the fundamental results for decision making under
uncertainty in one place. In particular, the aim is to give a unified account of algorithms and …

The cover tree (CT) and its improved version are hierarchical data structures that simplified
navigating nets while maintaining good runtime guarantees. They can perform nearest …

A data set may be partitioned according to relative differ ences indicated by a cover tree. A
cover tree may be generated for a data set. Items in the data set may be stored at the same …

Discovering personal identifying information (PII) in unstructured data is an important pre-
processing step in enabling privacy preserving machine learning as well as compliance with …

Hashing has been attracting much attention in computer vision recently, since it can provide
efficient similarity comparison in massive multimedia databases with fast query speed and …

In this work we consider a new family of algorithms for sequential prediction, Hierarchical
Partitioning Forecasters (HPFs). Our goal is to provide appealing theoretical-regret …