Upper bounds on the Bayes-optimal procedure for ranking &amp;amp; selection with independent normal priors

Xie, Jun; Frazier, Peter I.

doi:10.1109/wsc.2013.6721479

Cited by 2 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast with the previous problem, computation in this problem scales exponentially in k due to "curse of dimensionality", because we can no longer decompose equation ( 5) into multiple tractable sub-problems (Powell 2007). Instead, we consider a Lagrangian relaxation, following developments Hu et al (2014), Xie and Frazier (2013b), that provides a computationally tractable upper bound on the value of equation ( 5), and which motivates an index-based heuristic policy below in Section 3.1.…”

Section: Mathematical Model With a Constraint On Items Forwardedmentioning

confidence: 99%

A Markov Decision Process Analysis of the Cold Start Problem in Bayesian Information Filtering

Zhao,

Frazier

2014

Preprint

Self Cite

View full text Add to dashboard Cite

We consider the information filtering problem, in which we face a stream of items, and must decide which ones to forward to a user to maximize the number of relevant items shown, minus a penalty for each irrelevant item shown. Forwarding decisions are made separately in a personalized way for each user. We focus on the cold-start setting for this problem, in which we have limited historical data on the user's preferences, and must rely on feedback from forwarded articles to learn which the fraction of items relevant to the user in each of several item categories. Performing well in this setting requires trading exploration vs. exploitation, forwarding items that are likely to be irrelevant, to allow learning that will improve later performance. In a Bayesian setting, and using Markov decision processes, we show how the Bayes-optimal forwarding algorithm can be computed efficiently when the user will examine each forwarded article, and how an upper bound on the Bayes-optimal procedure and a heuristic index policy can be obtained for the setting when the user will examine only a limited number of forwarded items. We present results from simulation experiments using parameters estimated using historical data from arXiv.org.

show abstract

Section: Mathematical Model With a Constraint On Items Forwardedmentioning

confidence: 99%

A Markov Decision Process Analysis of the Cold Start Problem in Bayesian Information Filtering

Zhao,

Frazier

2014

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our use of a Lagrangian relaxation to study the Bayesian formulation of the MCS problem is also similar to Xie and Frazier (2013a), which used a Lagrangian relaxation to bound the value of the Bayes-optimal procedure for the ranking and selection problem.…”

Section: Introductionmentioning

confidence: 99%

Parallel Bayesian policies for finite-horizon multiple comparisons with a known standard

Hu¹,

Frazier²,

Xie³

2014

Proceedings of the Winter Simulation Conference 2014

Self Cite

View full text Add to dashboard Cite

We consider the problem of multiple comparisons with a known standard, in which we wish to allocate simulation effort efficiently across a finite number of simulated systems, to determine which systems have mean performance exceeding a known threshold. We suppose that parallel computing resources are available, and that we are given a fixed simulation budget. We consider this problem in a Bayesian setting, and formulate it as a stochastic dynamic program. For simplicity, we focus on Bernoulli sampling, with a linear loss function. Using links to restless multi-armed bandits, we provide a computationally tractable upper bound on the value of the Bayes-optimal policy, and an index policy motivated by these upper bounds.

show abstract

Upper bounds on the Bayes-optimal procedure for ranking & selection with independent normal priors

Cited by 2 publications

References 23 publications

A Markov Decision Process Analysis of the Cold Start Problem in Bayesian Information Filtering

A Markov Decision Process Analysis of the Cold Start Problem in Bayesian Information Filtering

Parallel Bayesian policies for finite-horizon multiple comparisons with a known standard

Contact Info

Product

Resources

About