Özgür Sümer’s research while affiliated with University of Chicago and other places

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 this paper, we present algorithms for adaptive exact inference on general graphs that can be used to efficiently compute marginals and update MAP configurations under arbitrary changes to the input factor graph and its associated elimination tree. After a linear time preprocessing step, our approach enables updates to the model and the computation of any marginal in time that is logarithmic in the size of the input model. Moreover, in contrast to max-product our approach can also be used to update MAP configurations in time that is roughly proportional to the number of updated entries, rather than the size of the input model. To evaluate the practical effectiveness of our algorithms, we implement and test them using synthetic data as well as for two real-world computational biology applications. Our experiments show that adaptive inference can achieve substantial speedups over performing complete inference as the model undergoes small changes over time.

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 major role: larger subproblems tend to improve the bound more per iteration, while smaller subproblems enable highly parallel solvers and can benefit from re-using past solutions when there are few changes between iterations. We propose an algorithm that can balance many of these aspects to speed up convergence. Our method uses a cluster tree data structure that has been proposed for adaptive exact inference tasks, and we apply it in this paper to dual-decomposition approximate inference. This approach allows us to process large subproblems to improve the bounds at each iteration, while allowing a high degree of parallelizability and taking advantage of subproblems with sparse updates. For both synthetic inputs and a real-world stereo matching problem, we demonstrate that our algorithm is able to achieve significant improvement in convergence time. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

Many algorithms and applications involve re- peatedly 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 pre- served in the model and perform inference more rapidly than from scratch. In this paper, we de- scribe techniques for adaptive inference on gen- eral graphs that support marginal computation and updates to the conditional probabilities and dependencies in logarithmic time. We give ex- perimental results for an implementation of our algorithm, and demonstrate its potential perfor- mance benefit in the study of protein structure.