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We introduce a natural variant of the (metric uncapac-itated)-median problem that we call the online median problem. Whereas the-median problem involves optimizing the simultaneous placement of facilities, the on-line median problem imposes the following additional constraints: the facilities are placed one at a time; a facility cannot be moved once it is(More)
We describe an efficient algorithm for protein backbone structure determination from solution Nuclear Magnetic Resonance (NMR) data. A key feature of our algorithm is that it finds the conformation and orientation of secondary structure elements as well as the global fold in polynomial time. This is the first polynomial-time algorithm for de novo(More)
We give randomized constant-factor approximation algorithms for the-median problem and an intimately related clustering problem. The input to each of these problems is a metric space with Ò weighted points and an integer , ¼ Ò. For any such input, let Ê denote the ratio between the maximum and minimum nonzero interpoint distances, and let Ê Û denote the(More)
Many algorithms and applications involve repeatedly solving variations of the same inference problem; for example we may want to introduce new evidence to the model or perform updates to conditional dependencies. The goal of adap-tive inference is to take advantage of what is preserved in the model and perform inference more rapidly than from scratch. In(More)
Motivated by stochastic systems in which observed evidence and conditional dependencies between states of the network change over time, and certain quantities of interest (marginal distributions, likelihood estimates etc.) must be updated, we study the problem of adaptive inference in tree-structured Bayesian networks. We describe an algorithm for adaptive(More)
Many algorithms and applications involve repeatedly solving variations of the same inference problem , for example to introduce new evidence to the model or to change conditional dependencies. As the model is updated, the goal of adaptive inference is to take advantage of previously computed quantities to perform inference more rapidly than from scratch. In(More)
In this article we describe a computational method that automatically generates chemically relevant compound ideas from an initial molecule, closely integrated with in silico models, and a probabilistic scoring algorithm to highlight the compound ideas most likely to satisfy a user-defined profile of required properties. The new compound ideas are generated(More)
Our paper describes the first provably-efficient algorithm for determining protein structures de novo, solely from experimental data. We show how the global nature of a certain kind of NMR data provides quantifiable complexity-theoretic benefits, allowing us to classify our algorithm as running in polynomial time. While our algorithm uses NMR data as input,(More)
Many applications involve repeatedly computing the optimal, maximum a posteriori (MAP) configuration of a graphical model as the model changes, often slowly or incrementally over time, e.g., due to input from a user. Small changes to the model often require updating only a small fraction of the MAP configuration, suggesting the possibility of performing(More)
Dual-decomposition (DD) methods are quickly becoming important tools for estimating the minimum energy state of a graphical model. DD methods decompose a complex model into a collection of simpler subproblems that can be solved exactly (such as trees), that in combination provide upper and lower bounds on the exact solution. Subproblem choice can play a(More)