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Random instances of Constraint Satisfaction Problems (CSP’s) appear to be hard for all known algorithms, when the number of constraints per variable lies in a certain interval. Contributing to the gen...
The EM algorithm is a popular tool for clustering observations via a parametric mixture model. Two disadvantages of this approach are that its success depends on the appropriateness of the assumed pa...
This paper considers the problem of clustering a collection of unlabeled data points assumed to lie near a union of lower dimensional planes. As is common in computer vision or unsupervised learning a...
Subspace clustering refers to the task of finding a multi-subspace representation that best fits a collection of points taken from a high-dimensional space. This paper introduces an algorithm inspired...
We consider sparse random intersection graphs with the property that the clustering coefficient does not vanish as the number of nodes tends to infinity. We find explicit asymptotic expressions for th...
A clustering algorithm partitions a set of data points into smaller sets (clusters) such that each subset is more tightly packed than the whole. Many approaches to clustering translate the vector data...
1-D scalar conservation laws with convex flux and Markov initial data are now known to yield a completely integrable Hamiltonian system. In this article, we rederive the analogue of Loitsiansky’s in...
In this work we investigate the multivariate statistical description of the matter distribution in the nonlinear regime.
Many problems in machine learning and statistics can be formulated as (generalized)eigenproblems. In terms of the associated optimization problem, computing linear eigenvectors amounts to finding crit...
In this work we investigate the multivariate statistical description of the matter distribution in the nonlinear regime. We introduce the multivariate Edgeworth expansion of the lognormal distribution...
Many problems in machine learning and statistics can be formulated as (generalized) eigenproblems. In terms of the associated optimization problem, computing linear eigenvectors amounts to finding cri...
We study algorithms for clustering data that were recently proposed by Balcan, Blum and Gupta in SODA’09 [4] and that have already given rise to two follow-up papers. The input for the clustering prob...
A new space-time model for interacting agents on the financial market is presented. It is a combination of the Curie-Weiss model and a space-time model introduced by J\"arpe 2005. Properties of the mo...
An analysis of the stylized facts in financial time series is carried out. We find that, instead of the heavy tails in asset return distributions, the slow decay behaviour in autocorrelation functions...
It might be expected that trajectories for a dynamical system which has no negative Lyapunov exponent (implying exponential growth of small separations will not cluster together. However, clustering c...

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