12 citations · 33 across the 6 of their papers we have counts for
6 papers
Latent Temporal Flows for Multivariate Analysis of Wearables Data
Magda Amiridi, Gregory Darnell, Sean Jewell
Increased use of sensor signals from wearable devices as rich sources of physiological data has sparked growing interest in developing health monitoring systems to identify changes…
Learning Multivariate CDFs and Copulas using Tensor Factorization
Magda Amiridi, Nicholas D. Sidiropoulos
Learning the multivariate distribution of data is a core challenge in statistics and machine learning. Traditional methods aim for the probability density function (PDF) and are li…
Low-rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density Estimation
Magda Amiridi, Nikos Kargas, Nicholas D. Sidiropoulos
Learning generative probabilistic models is a core problem in machine learning, which presents significant challenges due to the curse of dimensionality. This paper proposes a join…
Information-theoretic Feature Selection via Tensor Decomposition and Submodularity
Magda Amiridi, Nikos Kargas, Nicholas D. Sidiropoulos
Feature selection by maximizing high-order mutual information between the selected feature vector and a target variable is the gold standard in terms of selecting the best subset o…
Low-rank Characteristic Tensor Density Estimation Part I: Foundations
Magda Amiridi, Nikos Kargas, Nicholas D. Sidiropoulos
Effective non-parametric density estimation is a key challenge in high-dimensional multivariate data analysis. In this paper,we propose a novel approach that builds upon tensor fac…
From Gene Expression to Drug Response: A Collaborative Filtering Approach
Cheng Qian, Nicholas D. Sidiropoulos, Magda Amiridi +1
Predicting the response of cancer cells to drugs is an important problem in pharmacogenomics. Recent efforts in generation of large scale datasets profiling gene expression and dru…