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COMPLEXITY OF STOCHASTIC BRANCH AND BOUND METHODS FOR BELIEF TREE SEARCH IN BAYESIAN REINFORCEMENT LEARNING

Publications associées (36)

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Tree in Tree: from Decision trees to Decision Graphs

Mahsa Shoaran, Bingzhao Zhu

Decision trees have been widely used as classifiers in many machine learning applications thanks to their lightweight and interpretable decision process. This paper introduces Tree in Tree decision graph (TnT), a framework that extends the conventional dec ...

Curran Associates, Inc, (NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing Systems)2021

Metric dimension of critical Galton–Watson trees and linear preferential attachment trees

Gergely Odor

The metric dimension of a graph G is the minimal size of a subset R of vertices of G that, upon reporting their graph distance from a distinguished (source) vertex v⋆, enable unique identification of the source vertex v⋆ among all possible vertices of G. I ...

2021

Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical Structures

Hakan Gökcesu, Kaan Gökcesu

We propose an online algorithm for sequential learning in the contextual multiarmed bandit setting. Our approach is to partition the context space and, then, optimally combine all of the possible mappings between the partition regions and the set of bandit ...

2019

Scaling Similarity Joins over Tree-Structured Data

Given a large collection of tree-structured objects (e.g., XML documents), the similarity join finds the pairs of objects that are similar to each other, based on a similarity threshold and a tree edit distance measure. The state-of-the-art similarity join ...

Assoc Computing Machinery2015

Distributed Rate Allocation in Inter-Session Network Coding

Pascal Frossard, Nikolaos Thomos, Eirina Bourtsoulatze

In this work, we propose a distributed rate allocation algorithm that minimizes the average decoding delay for multimedia clients in inter-session network coding systems. We consider a scenario where the users are organized in a mesh network and each user ...

Institute of Electrical and Electronics Engineers2014

Symbolic Resource Bound Inference

Viktor Kuncak, Ravichandhran Kandhadai Madhavan

We present an approach for inferring symbolic resource bounds for purely functional programs consisting of recursive functions, algebraic data types and nonlinear arithmetic operations. In our approach, the developer specifies the desired shape of the boun ...

2014

Cover Tree Bayesian Reinforcement Learning

Christos Dimitrakakis, Nikolaos Tziortziotis

This paper proposes an online tree-based Bayesian approach for reinforcement learning. For inference, we employ a generalised context tree model. This defines a distribution on multivariate Gaussian piecewise-linear models, which can be updated in closed f ...

2013

Accelerating Spatial Range Queries

Anastasia Ailamaki, Thomas Heinis, Farhan Tauheed, Alexandros Stougiannis

It is increasingly common for domain scientists to use computational tools to build and simulate spatial models of the phenomena they are studying. The spatial models they build are more and more detailed as well as dense and are consequently difficult to ...

2013

Scalable and Dynamically Balanced Shared-Everything OLTP with Physiological Partitioning

Anastasia Ailamaki, Frederick Ryan Johnson, Pinar Tözün, Ippokratis Pandis

Scaling the performance of shared-everything transaction processing systems to highly-parallel multicore hardware remains a challenge for database system designers. Recent proposals alleviate locking and logging bottlenecks in the system, leaving page latc ...

2013

A Speculation-Friendly Binary Search Tree

Vincent Gramoli

We introduce the first binary search tree algorithm designed for speculative executions. Prior to this work, tree structures were mainly designed for their pessimistic (non-speculative) accesses to have a bounded complexity. Researchers tried to evaluate t ...

Assoc Computing Machinery2012