no code implementations • 18 Jun 2014 • Furong Huang, Niranjan U. N., Ioakeim Perros, Robert Chen, Jimeng Sun, Anima Anandkumar
We present an integrated approach for structure and parameter estimation in latent tree graphical models.
no code implementations • 25 Oct 2016 • Ioakeim Perros, Robert Chen, Richard Vuduc, Jimeng Sun
It can also do so more accurately and in less time than the state-of-the-art: on a 12th order subset of the input data, Sparse H-Tucker is 18x more accurate and 7. 5x faster than a previously state-of-the-art method.
no code implementations • NeurIPS 2016 • Dehua Cheng, Richard Peng, Yan Liu, Ioakeim Perros
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.
no code implementations • 13 Mar 2017 • Ioakeim Perros, Evangelos E. Papalexakis, Fei Wang, Richard Vuduc, Elizabeth Searles, Michael Thompson, Jimeng Sun
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 time points for analysis purposes.
1 code implementation • 12 Mar 2018 • Ardavan Afshar, Ioakeim Perros, Evangelos E. Papalexakis, Elizabeth Searles, Joyce Ho, Jimeng Sun
To tackle these challenges, we propose a {\it CO}nstrained {\it PA}RAFAC2 (COPA) method, which carefully incorporates optimization constraints such as temporal smoothness, sparsity, and non-negativity in the resulting factors.
no code implementations • 14 Mar 2018 • Ioakeim Perros, Evangelos E. Papalexakis, Haesun Park, Richard Vuduc, Xiaowei Yan, Christopher deFilippi, Walter F. Stewart, Jimeng Sun
We propose two variants, SUSTain_M and SUSTain_T, to handle both matrix and tensor inputs, respectively.
no code implementations • 13 Nov 2019 • Ardavan Afshar, Ioakeim Perros, Haesun Park, Christopher deFilippi, Xiaowei Yan, Walter Stewart, Joyce Ho, Jimeng Sun
TASTE combines the PARAFAC2 model with non-negative matrix factorization to model a temporal and a static tensor.