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Steven J. Phillips

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10 papers
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10

JMLR Journal 2007 Journal Article

Maximum Entropy Density Estimation with Generalized Regularization and an Application to Species Distribution Modeling

  • Miroslav Dudík
  • Steven J. Phillips
  • Robert E. Schapire

We present a unified and complete account of maximum entropy density estimation subject to constraints represented by convex potential functions or, alternatively, by convex regularization. We provide fully general performance guarantees and an algorithm with a complete convergence proof. As special cases, we easily derive performance guarantees for many known regularization types, including l 1, l 2, l 2 2, and l 2 + l 2 2 style regularization. We propose an algorithm solving a large and general subclass of generalized maximum entropy problems, including all discussed in the paper, and prove its convergence. Our approach generalizes and unifies techniques based on information geometry and Bregman divergences as well as those based more directly on compactness. Our work is motivated by a novel application of maximum entropy to species distribution modeling, an important problem in conservation biology and ecology. In a set of experiments on real-world data, we demonstrate the utility of maximum entropy in this setting. We explore effects of different feature types, sample sizes, and regularization levels on the performance of maxent, and discuss interpretability of the resulting models. [abs] [ pdf ][ bib ] &copy JMLR 2007. ( edit, beta )

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FOCS Conference 1994 Conference Paper

IP over connection-oriented networks and distributional paging

  • Carsten Lund
  • Steven J. Phillips
  • Nick Reingold

Next generation wide area network are very likely to use connection-oriented protocols such as Asynchronous Transfer Mode (ATM). For the huge existing investment in current IP networks such as the Internet to remain useful, me must devise mechanisms to carry IP traffic over connection-oriented networks. A basic issue is to devise holding policies for virtual circuits carrying datagrams. In this paper we consider two variants of the paging problem that arise in the design of such holding policies. In the IP-paging problem the page inter-request times are chosen according to independent distributions. For this model we construct a very simple deterministic algorithm whose page fault rate is at most 5 times that of the best online algorithm (that knows the inter-request time distributions). We also show that some natural algorithms for this problem do not have constant competitive ratio. In distributional paging the inter-request time distributions may be dependent, and hence any probabilistic model of page request sequences can be represented. We construct a simple randomized algorithm whose page fault rate is at most 4 times that of the best online algorithm. >

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FOCS Conference 1992 Conference Paper

Markov Paging (Extended Abstract)

  • Anna R. Karlin
  • Steven J. Phillips
  • Prabhakar Raghavan

This paper considers the problem of paging under the assumption that the sequence of pages accessed is generated by a Markov chain. The authors use this model to study the fault-rate of paging algorithms, a quantity of interest to practitioners. They first draw on the theory of Markov decision processes to characterize the paging algorithm that achieves optimal fault-rate on any Markov chain. They address the problem of efficiently devising a paging strategy with low fault-rate for a given Markov chain. They show that a number of intuitively good approaches fail. Their main result is an efficient procedure that, on any Markov chain, will give a paging algorithm with fault-rate at most a constant times optimal. Their techniques also show that some algorithms that do poorly in practice fail in the Markov setting, despite known (good) performance guarantees when the requests are generated independently from a probability distribution. >

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FOCS Conference 1991 Conference Paper

Finding the Hidden Path: Time Bounds for All-Pairs Shortest Paths

  • David R. Karger
  • Daphne Koller
  • Steven J. Phillips

The all-pairs shortest paths problem in weighted graphs is investigated. An algorithm called the hidden paths algorithm, which finds these paths in time O(m*+n n/sup 2/ log n), where m* is the number of edges participating in shortest paths, is presented. It is argued that m* is likely to be small in practice, since m*=O(n log n) with high probability for many probability distributions on edge weights. An Omega (mn) lower bound on the running time of any path-comparison-based algorithm for the all-pairs shortest paths problem is proved. >

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