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05/17/2023
An unambiguous and robust formulation for Wannier localization
We provide a new variational definition for the spread of an orbital und...
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05/31/2022
Communication-efficient distributed eigenspace estimation with arbitrary node failures
We develop an eigenspace estimation algorithm for distributed environmen...
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02/08/2022
Linear Time Kernel Matrix Approximation via Hyperspherical Harmonics
We propose a new technique for constructing low-rank approximations of m...
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07/30/2021
Pruning Neural Networks with Interpolative Decompositions
We introduce a principled approach to neural network pruning that casts ...
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06/08/2021
The Fast Kernel Transform
Kernel methods are a highly effective and widely used collection of mode...
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05/16/2021
Data-driven Algorithms for signal processing with rational functions
Rational approximation schemes for reconstructing signals from samples w...
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10/29/2020
Over-parametrized neural networks as under-determined linear systems
We draw connections between simple neural networks and under-determined ...
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09/05/2020
Communication-efficient distributed eigenspace estimation
Distributed computing is a standard way to scale up machine learning and...
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06/19/2020
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Matrix square roots and their inverses arise frequently in machine learn...
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02/19/2020
Entrywise convergence of iterative methods for eigenproblems
Several problems in machine learning, statistics, and other fields rely ...
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01/31/2020
Parallel Skeletonization for Integral Equations in Evolving Multiply-Connected Domains
This paper presents a general method for applying hierarchical matrix sk...
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09/03/2019
Incrementally Updated Spectral Embeddings
Several fundamental tasks in data science rely on computing an extremal ...
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05/20/2019