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An Optimal Policy for Target Localization with Application to Electron Microscopy
"... This paper considers the task of finding a target location by making a limited number of sequential observation. 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 prob ..."
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This paper considers the task of finding a target location by making a limited number of sequential observation. 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 of the posterior distribution with the cost of the questions asked. In this problem, we show that the onestep lookahead policy is Bayesoptimal for any arbitrary time horizon. Moreover, this onestep lookahead policy is easy to compute and implement. We then use this policy in the context of localizing mitochondria in electron microscope images, and experimentally show that significant speed ups in acquisition can be gained, while maintaining near equal image quality at target locations, when compared to current policies. Proceedings of the 30 th
Research Statement
"... Summary I am interested in (1) the design of intelligent agents and systems, primarily guided by machine learning; (2) modeling and understanding collective dynamics that result from intelligent individual behavior; and (3) using this understanding to inform the design of venues where people and aut ..."
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Summary I am interested in (1) the design of intelligent agents and systems, primarily guided by machine learning; (2) modeling and understanding collective dynamics that result from intelligent individual behavior; and (3) using this understanding to inform the design of venues where people and automated agents come together to interact. A central focus of my research is on understanding how information flows through systems, how it can be best used by intelligent agents, and how its presence, absence, or the form in which it is available impacts decisions at the individual and systemic levels. My work can be categorized into four broad themes. 1: Collective intelligence I am interested in both modeling and understanding the dynamics of collective intelligence, and in designing algorithms that allow us to use the power of collective wisdom to make better decisions. I have been working on the foundations of a rigorous theory of how information grows in novel social media like Wikipedia and the blogosphere, and on information aggregation and dissemination in prediction markets. In recent work, we have documented some remarkable regularities in the life cycles of average Wikipedia pages and blog posts [26, 27]. They exhibit a concave rise to an editing / commenting peak, followed by decay at a 1/t rate over time. We have proposed a simple model of information creation that matches the data
BISECTION SEARCH WITH NOISY RESPONSES
"... Abstract. 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 cor ..."
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Abstract. 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 (PBA) introduced in Horstein (1963) can be used to locate X ∗ in this setting. While the method works extremely well in practice, very little is known about its theoretical properties. In this paper, we provide several key findings about the PBA, which lead to the main conclusion that the expected absolute residuals of successive search results, i.e., E[X ∗ − Xn], converge to 0 at a geometric rate.
SEQUENTIAL SCREENING: A BAYESIAN DYNAMIC PROGRAMMING ANALYSIS OF OPTIMAL GROUPSPLITTING
"... Sequential screening is the problem of allocating simulation effort to identify those input factors that have an important effect on a simulation’s output. In this problem, sophisticated algorithms can be substantially more efficient than simulating one factor at a time. We consider this problem in ..."
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Sequential screening is the problem of allocating simulation effort to identify those input factors that have an important effect on a simulation’s output. In this problem, sophisticated algorithms can be substantially more efficient than simulating one factor at a time. We consider this problem in a Bayesian framework, in which each factor is important independently and with a known probability. We use dynamic programming to compute the Bayesoptimal method for splitting factors among groups within a sequential bifurcation procedure (Bettonvil & Kleijnen 1997). We assume importance can be tested without error. Numerical experiments suggest that existing groupsplitting rules are optimal, or close to optimal, when factors have homogeneous importance probability, but that substantial gains are possible when factors have heterogeneous probability of importance. 1