A tutorial on Bayesian optimization
PI Frazier - arXiv preprint arXiv:1807.02811, 2018 - arxiv.org
Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of …
(minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of …
Decision-theoretic distributed channel selection for opportunistic spectrum access: Strategies, challenges and solutions
Opportunistic spectrum access (OSA) has been regarded as the most promising approach to
solve the paradox between spectrum scarcity and waste. Intelligent decision making is key …
solve the paradox between spectrum scarcity and waste. Intelligent decision making is key …
Bayesian optimization
PI Frazier - Recent advances in optimization and modeling …, 2018 - pubsonline.informs.org
Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best suited for optimization over continuous domains of …
(minutes or hours) to evaluate. It is best suited for optimization over continuous domains of …
Regret analysis of stochastic and nonstochastic multi-armed bandit problems
S Bubeck, N Cesa-Bianchi - Foundations and Trends® in …, 2012 - nowpublishers.com
Multi-armed bandit problems are the most basic examples of sequential decision problems
with an exploration-exploitation trade-off. This is the balance between staying with the option …
with an exploration-exploitation trade-off. This is the balance between staying with the option …
[图书][B] Multi-armed bandit allocation indices
J Gittins, K Glazebrook, R Weber - 2011 - books.google.com
In 1989 the first edition of this book set out Gittins' pioneering index solution to the multi-
armed bandit problem and his subsequent investigation of a wide of sequential resource …
armed bandit problem and his subsequent investigation of a wide of sequential resource …
Program-adaptive mutational fuzzing
We present the design of an algorithm to maximize the number of bugs found for black-box
mutational fuzzing given a program and a seed input. The major intuition is to leverage white …
mutational fuzzing given a program and a seed input. The major intuition is to leverage white …
Bayesian optimization for materials design
PI Frazier, J Wang - Information science for materials discovery and design, 2016 - Springer
We introduce Bayesian optimization, a technique developed for optimizing time-consuming
engineering simulations and for fitting machine learning models on large datasets. Bayesian …
engineering simulations and for fitting machine learning models on large datasets. Bayesian …
[图书][B] Principles of cognitive radio
E Biglieri - 2013 - books.google.com
Widely regarded as one of the most promising emerging technologies for driving the future
development of wireless communications, cognitive radio has the potential to mitigate the …
development of wireless communications, cognitive radio has the potential to mitigate the …
Indexability of restless bandit problems and optimality of whittle index for dynamic multichannel access
In this paper, we consider a class of restless multiarmed bandit processes (RMABs) that
arises in dynamic multichannel access, user/server scheduling, and optimal activation in …
arises in dynamic multichannel access, user/server scheduling, and optimal activation in …
Dynamic TCP initial windows and congestion control schemes through reinforcement learning
Despite many years of improvements to it, TCP still suffers from an unsatisfactory
performance. For services dominated by short flows (eg, web search and e-commerce), TCP …
performance. For services dominated by short flows (eg, web search and e-commerce), TCP …