#### Filter Results:

#### Publication Year

2007

2016

#### Publication Type

#### Co-author

#### Key Phrase

#### Publication Venue

Learn More

We study optimization in the presence of uncertainty such as noise in measurements, and advocate a novel approach of tackling it. The main difference to any existing approach is that we do not assume any knowledge about the nature of the uncertainty (such as for instance a probability distribution). Instead, we are given several instances of the same… (More)

In this paper, we introduce the on-line Viterbi algorithm for decoding hidden Markov models (HMMs) in much smaller than linear space. Our analysis on two-state HMMs suggests that the expected maximum memory used to decode sequence of length n with m-state HMM can be as low as Θ(m log n), without a significant slowdown compared to the classical Viterbi… (More)

We study the problem of robust routing in urban public transportation networks. In order to propose solutions that are robust for typical delays, we assume that we have past observations of real traffic situations available. In particular, we assume that we have " daily records " containing the observed travel times in the whole network for a few past days.… (More)

Given a directed acyclic graph with positive edge-weights, two vertices s and t, and a threshold-weight L, we present a fully-polynomial time approximation-scheme for the problem of counting the s-t paths of length at most L. We extend the algorithm for the case of two (or more) instances of the same problem. That is, given two graphs that have the same… (More)

Hidden Markov models (HMMs) are routinely used for analysis of long genomic sequences to identify various features such as genes, CpG islands, and conserved elements. A commonly used Viterbi algorithm requires O(mn) memory to annotate a sequence of length n with an m-state HMM, which is impractical for analyzing whole chromosomes. In this paper, we… (More)

We study the minimum cut problem in the presence of uncertainty and show how to apply a novel robust optimization approach, which aims to exploit the similarity in subsequent graph measurements or similar graph instances, without posing any assumptions on the way they have been obtained. With experiments we show that the approach works well when compared to… (More)