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- David M. Blei, Peter I. Frazier
- ICML
- 2010

We develop the distance dependent Chinese restaurant process, a flexible class of distributions over partitions that allows for dependencies between the elements. This class can be used to model many kinds of dependencies between data in infinite clustering models, including dependencies arising from time, space, and network connectivity. We examine the… (More)

- Peter I. Frazier, Warren B. Powell, Savas Dayanik
- INFORMS Journal on Computing
- 2009

We consider a Bayesian ranking and selection problem with independent normal rewards and a correlated multivariate normal belief on the mean values of these rewards. Because this formulation of the ranking and selection problem models dependence between alternatives' mean values, algorithms may utilize this dependence to perform efficiently even when the… (More)

- Rolf Waeber, Peter I. Frazier, Shane G. Henderson
- SIAM J. Control and Optimization
- 2013

Bisection search is the most efficient algorithm for locating a unique point X * ∈ [0, 1] when we are able to query an oracle only about whether X * lies to the left or right of a point x of our choosing. We study a noisy version of this classic problem, where the oracle's response is correct only with probability p. The probabilistic bisection algorithm… (More)

- Peter I. Frazier, Warren B. Powell, Savas Dayanik
- SIAM J. Control and Optimization
- 2008

In a sequential Bayesian ranking and selection problem with independent normal populations and common known variance, we study a previously introduced measurement policy which we refer to as the knowledge-gradient policy. This policy myopically maximizes the expected increment in the value of information in each time period, where the value is measured… (More)

- Peter I. Frazier, Angela J. Yu
- NIPS
- 2007

Most models of decision-making in neuroscience assume an infinite horizon, which yields an optimal solution that integrates evidence up to a fixed decision threshold; however, under most experimental as well as naturalistic behavioral settings, the decision has to be made before some finite deadline, which is often experienced as a stochastic quantity,… (More)

- Bruno Jedynak, Peter I. Frazier, Raphael Sznitman
- J. Applied Probability
- 2012

We consider the problem of 20 questions with noisy answers, in which we seek to find a target by repeatedly choosing a set, asking an oracle whether the target lies in this set, and obtaining an answer corrupted by noise. Starting with a prior distribution on the target's location, we seek to minimize the expected entropy of the posterior distribution. We… (More)

We consider information collection problems, in which we must decide how much and of what type of information to collect. We focus our interest on sequential Bayesian information collection problems. In making such decisions we trade the benefit of information (the ability to make better decisions in the future) against its cost (money, time, or opportunity… (More)

- Samuel Gershman, Peter I. Frazier, David M. Blei
- IEEE Transactions on Pattern Analysis and Machine…
- 2015

Latent feature models are widely used to decompose data into a small number of components. Bayesian nonparametric variants of these models, which use the Indian buffet process (IBP) as a prior over latent features, allow the number of features to be determined from the data. We present a generalization of the IBP, the <italic>distance dependent Indian… (More)

This paper considers the task of finding a target location by making a limited number of sequential observations. Each observation results from evaluating an imperfect classifier of a chosen cost and accuracy on an interval of chosen length and position. Within a Bayesian framework, we study the problem of minimizing an objective that combines the entropy… (More)

- Ilya O. Ryzhov, Warren B. Powell, Peter I. Frazier
- Operations Research
- 2012

We derive a one-period look-ahead policy for finite-and infinite-horizon online optimal learning problems with Gaussian rewards. Our approach is able to handle the case where our prior beliefs about the rewards are correlated, which is not handled by traditional multi-armed bandit methods. Experiments show that our KG policy performs competitively against… (More)