52 citations · 150 across the 24 of their papers we have counts for
26 papers
An unambiguous and robust formulation for Wannier localization
Kangbo Li, Hsin-Yu Ko, Robert A. DiStasio +1
We provide a new variational definition for the spread of an orbital under periodic boundary conditions (PBCs) that is continuous with respect to the gauge, consistent in the therm…
Communication-efficient distributed eigenspace estimation with arbitrary node failures
Vasileios Charisopoulos, Anil Damle
We develop an eigenspace estimation algorithm for distributed environments with arbitrary node failures, where a subset of computing nodes can return structurally valid but otherwi…
Linear Time Kernel Matrix Approximation via Hyperspherical Harmonics
John Paul Ryan, Anil Damle
We propose a new technique for constructing low-rank approximations of matrices that arise in kernel methods for machine learning. Our approach pairs a novel automatically construc…
Selected Columns of the Density Matrix in an Atomic Orbital Basis I: An Intrinsic and Non-Iterative Orbital Localization Scheme for the Occupied Space
Eric G. Fuemmeler, Anil Damle, Robert A. DiStasio
We extend the selected columns of the density matrix (SCDM) methodology [J. Chem. Theory Comput. 2015, 11, 1463--1469]---a non-iterative procedure for generating localized occupied…
Model Preserving Compression for Neural Networks
Jerry Chee, Megan Renz, Anil Damle +1
After training complex deep learning models, a common task is to compress the model to reduce compute and storage demands. When compressing, it is desirable to preserve the origina…
The Fast Kernel Transform
John Paul Ryan, Sebastian Ament, Carla P. Gomes +1
Kernel methods are a highly effective and widely used collection of modern machine learning algorithms. A fundamental limitation of virtually all such methods are computations invo…
Data-driven Algorithms for signal processing with trigonometric rational functions
Heather Wilber, Anil Damle, Alex Townsend
Rational approximation schemes for reconstructing periodic signals from samples with poorly separated spectral content are described. These methods are automatic and adaptive, requ…
Over-parametrized neural networks as under-determined linear systems
Austin R. Benson, Anil Damle, Alex Townsend
We draw connections between simple neural networks and under-determined linear systems to comprehensively explore several interesting theoretical questions in the study of neural n…
Communication-efficient distributed eigenspace estimation
Vasileios Charisopoulos, Austin R. Benson, Anil Damle
Distributed computing is a standard way to scale up machine learning and data science algorithms to process large amounts of data. In such settings, avoiding communication amongst…
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Geoff Pleiss, Martin Jankowiak, David Eriksson +2
Matrix square roots and their inverses arise frequently in machine learning, e.g., when sampling from high-dimensional Gaussians or whitening a…
Entrywise convergence of iterative methods for eigenproblems
Vasileios Charisopoulos, Austin R. Benson, Anil Damle
Several problems in machine learning, statistics, and other fields rely on computing eigenvectors. For large scale problems, the computation of these eigenvectors is typically perf…
Parallel Skeletonization for Integral Equations in Evolving Multiply-Connected Domains
John Paul Ryan, Anil Damle
This paper presents a general method for applying hierarchical matrix skeletonization factorizations to the numerical solution of boundary integral equations with possibly rank-def…
Incrementally Updated Spectral Embeddings
Vasileios Charisopoulos, Austin R. Benson, Anil Damle
Several fundamental tasks in data science rely on computing an extremal eigenspace of size , where is the underlying problem dimension. For example, spectral clusterin…
Uniform bounds for invariant subspace perturbations
Anil Damle, Yuekai Sun
For a fixed symmetric matrix A and symmetric perturbation E we develop purely deterministic bounds on how invariant subspaces of A and A+E can differ when measured by a suitable "r…
Network interpolation
Thomas Reeves, Anil Damle, Austin R. Benson
Given a set of snapshots from a temporal network we develop, analyze, and experimentally validate a so-called network interpolation scheme. Our method allows us to build a plausibl…
Variational formulation for Wannier functions with entangled band structure
Anil Damle, Antoine Levitt, Lin Lin
Wannier functions provide a localized representation of spectral subspaces of periodic Hamiltonians, and play an important role for interpreting and accelerating Hartree-Fock and K…
Disentanglement via entanglement: A unified method for Wannier localization
Anil Damle, Lin Lin
The Wannier localization problem in quantum physics is mathematically analogous to finding a localized representation of a subspace corresponding to a nonlinear eigenvalue problem.…
Robust and efficient multi-way spectral clustering
Anil Damle, Victor Minden, Lexing Ying
We present a new algorithm for spectral clustering based on a column-pivoted QR factorization that may be directly used for cluster assignment or to provide an initial guess for k-…
A recursive skeletonization factorization based on strong admissibility
Victor Minden, Kenneth L. Ho, Anil Damle +1
We introduce the strong recursive skeletonization factorization (RS-S), a new approximate matrix factorization based on recursive skeletonization for solving discretizations of lin…
Fast spatial Gaussian process maximum likelihood estimation via skeletonization factorizations
Victor Minden, Anil Damle, Kenneth L. Ho +1
Maximum likelihood estimation for parameter-fitting given observations from a Gaussian process in space is a computationally-demanding task that restricts the use of such methods t…
SCDM-k: Localized orbitals for solids via selected columns of the density matrix
Anil Damle, Lin Lin, Lexing Ying
The recently developed selected columns of the density matrix (SCDM) method [J. Chem. Theory Comput. 11, 1463, 2015] is a simple, robust, efficient and highly parallelizable method…
A technique for updating hierarchical skeletonization-based factorizations of integral operators
Victor Minden, Anil Damle, Kenneth L. Ho +1
We present a method for updating certain hierarchical factorizations for solving linear integral equations with elliptic kernels. In particular, given a factorization corresponding…
Compressed representation of Kohn-Sham orbitals via selected columns of the density matrix
Anil Damle, Lin Lin, Lexing Ying
Given a set of Kohn-Sham orbitals from an insulating system, we present a simple, robust, efficient and highly parallelizable method to construct a set of, optionally orthogonal, l…
A geometric approach to archetypal analysis and non-negative matrix factorization
Anil Damle, Yuekai Sun
Archetypal analysis and non-negative matrix factorization (NMF) are staples in a statisticians toolbox for dimension reduction and exploratory data analysis. We describe a geometri…