activity
20172022
most citedTASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records

7 citations · 19 across the 7 of their papers we have counts for

collaborators

7 papers

math.NT2022

A note on Halász's Theorem in

Ardavan Afshar

In the setting of the integers, Granville, Harper and Soundararajan showed that the upper bound in Halász's Theorem can be improved for smoothly supported functions. We derive the…

cs.LG2020★ 1 cited

SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors

Ardavan Afshar, Kejing Yin, Sherry Yan +4

Existing tensor factorization methods assume that the input tensor follows some specific distribution (i.e. Poisson, Bernoulli, and Gaussian), and solve the factorization by minimi…

math.NT2020

A "Proto-Pellet's Formula" for the Möbius Function

Ardavan Afshar

We give a short proof of "Pellet's Formula" for the Möbius Function on , deriving an intermediate formula (which we call "Proto-Pellet's Formula") along the way. W…

math.NT2020

Highly Composite Polynomials and the maximum order of the Divisor Function in

Ardavan Afshar

We investigate the analogues, in , of highly composite numbers and the maximum order of the divisor function, as studied by Ramanujan. In particular, we determine…

cs.LG2019★ 7 cited

TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records

Ardavan Afshar, Ioakeim Perros, Haesun Park +5

Phenotyping electronic health records (EHR) focuses on defining meaningful patient groups (e.g., heart failure group and diabetes group) and identifying the temporal evolution of p…

cs.LG2018★ 5 cited

COPA: Constrained PARAFAC2 for Sparse & Large Datasets

Ardavan Afshar, Ioakeim Perros, Evangelos E. Papalexakis +3

PARAFAC2 has demonstrated success in modeling irregular tensors, where the tensor dimensions vary across one of the modes. An example scenario is modeling treatments across a set o…