Ioakeim Perros

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We present an integrated approach to structure and parameter estimation in latent tree graphical models, where some nodes are hidden. Our approach follows a “divide-and-conquer” strategy, and learns models over small groups of variables (where the grouping is obtained through preprocessing). A global solution is obtained in the end through simple merge(More)
This paper describes a novel framework, called I<scp>n</scp>T<scp>ens</scp>L<scp>i</scp> ("intensely"), for producing fast single-node implementations of dense tensor-times-matrix multiply (T<scp>tm</scp>) of arbitrary dimension. Whereas conventional implementations of T<scp>tm</scp> rely on explicitly converting the input tensor operand into a matrix---in(More)
We propose a new tensor factorization method, called the Sparse Hierarchical-Tucker (Sparse H-Tucker), for sparse and high-order data tensors. Sparse H-Tucker is inspired by its namesake, the classical Hierarchical Tucker method, which aims to compute a tree-structured factorization of an input data set that may be readily interpreted by a domain expert.(More)
Tensor CANDECOMP/PARAFAC (CP) decomposition is a powerful but computationally challenging tool in modern data analytics. In this paper, we show ways of sampling intermediate steps of alternating minimization algorithms for computing low rank tensor CP decompositions, leading to the sparse alternating least squares (SPALS) method. Specifically, we sample the(More)
Given an input tensor, its CANDECOMP/PARAFAC decomposition (or CPD) is a low-rank representation. CPDs are of particular interest in data analysis and mining, especially when the data tensor is sparse and of higher order (dimension). This paper focuses on the central bottleneck of a CPD algorithm, which is evaluating a sequence of matricized tensor times(More)
In exploratory tensor mining, a common problem is how to analyze a set of variables across a set of subjects whose observations do not align naturally. For example, when modeling medical features across a set of patients, the number and duration of treatments may vary widely in time, meaning there is no meaningful way to align their clinical records across(More)
Extracting patterns and deriving insights from spatio-temporal data finds many target applications in various domains, such as in urban planning and computational sustainability. Due to their inherent capability of simultaneously modeling the spatial and temporal aspects of multiple instances, tensors have been successfully used to analyze such(More)
The current document contains material supplementing the paper “Polyadic Regression and its Application to Chemogenomics”, published in the proceedings of the SIAM International Conference on Data Mining 2017. We first provide discussions on algorithmic derivations and details regarding the implementation. Then, we include our experimental evaluation with(More)
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