
Sublinear Time Eigenvalue Approximation via Random Sampling
We study the problem of approximating the eigenspectrum of a symmetric m...
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Error bounds for Lanczosbased matrix function approximation
We analyze the Lanczos method for matrix function approximation (Lanczos...
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Coresets for Classification – Simplified and Strengthened
We give relative error coresets for training linear classifiers with a b...
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DeepWalking Backwards: From Embeddings Back to Graphs
Lowdimensional node embeddings play a key role in analyzing graph datas...
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Faster Kernel Matrix Algebra via Density Estimation
We study fast algorithms for computing fundamental properties of a posit...
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Faster Kernel Interpolation for Gaussian Processes
A key challenge in scaling Gaussian Process (GP) regression to massive d...
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Intervention Efficient Algorithms for Approximate Learning of Causal Graphs
We study the problem of learning the causal relationships between a set ...
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Estimation of Shortest Path Covariance Matrices
We study the sample complexity of estimating the covariance matrix Σ∈ℝ^d...
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Modelspecific Data Subsampling with Influence Functions
Model selection requires repeatedly evaluating models on a given dataset...
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Hutch++: Optimal Stochastic Trace Estimation
We study the problem of estimating the trace of a matrix A that can only...
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Subspace Embeddings Under Nonlinear Transformations
We consider lowdistortion embeddings for subspaces under entrywise nonl...
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Spiking Neural Networks Through the Lens of Streaming Algorithms
We initiate the study of biological neural networks from the perspective...
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Fourier Sparse Leverage Scores and Approximate Kernel Learning
We prove new explicit upper bounds on the leverage scores of Fourier spa...
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Node Embeddings and Exact LowRank Representations of Complex Networks
Lowdimensional embeddings, from classical spectral embeddings to modern...
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InfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a Nonlinearity
The skipgram model for learning word embeddings (Mikolov et al. 2013) h...
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Efficient Intervention Design for Causal Discovery with Latents
We consider recovering a causal graph in presence of latent variables, w...
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ProjectionCostPreserving Sketches: Proof Strategies and Constructions
In this note we illustrate how common matrix approximation methods, such...
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LowRank Toeplitz Matrix Estimation via Random UltraSparse Rulers
We study how to estimate a nearly lowrank Toeplitz covariance matrix T ...
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Importance Sampling via Local Sensitivity
Given a loss function F:X→R^+ that can be written as the sum of losses o...
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Toward a Characterization of Loss Functions for Distribution Learning
In this work we study loss functions for learning and evaluating probabi...
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Sample Efficient Toeplitz Covariance Estimation
We study the query complexity of estimating the covariance matrix T of a...
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Learning to Prune: Speeding up Repeated Computations
It is common to encounter situations where one must solve a sequence of ...
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WinnerTakeAll Computation in Spiking Neural Networks
In this work we study biological neural networks from an algorithmic per...
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LowRank Approximation from Communication Complexity
In lowrank approximation with missing entries, given A∈R^n× n and binar...
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Faster Spectral Sparsification in Dynamic Streams
Graph sketching has emerged as a powerful technique for processing massi...
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A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms
Reconstructing continuous signals from a small number of discrete sample...
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A Basic Compositional Model for Spiking Neural Networks
This paper is part of a project on developing an algorithmic theory of b...
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Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees
Random Fourier features is one of the most popular techniques for scalin...
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Eigenvector Computation and Community Detection in Asynchronous Gossip Models
We give a simple distributed algorithm for computing adjacency matrix ei...
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Learning Networks from Random WalkBased Node Similarities
Digital presence in the world of online social media entails significant...
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Minimizing Polarization and Disagreement in Social Networks
The rise of social media and online social networks has been a disruptiv...
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Is Input Sparsity Time Possible for Kernel LowRank Approximation?
Lowrank approximation is a common tool used to accelerate kernel method...
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NeuroRAM Unit with Applications to Similarity Testing and Compression in Spiking Neural Networks
We study distributed algorithms implemented in a simplified biologically...
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Computational Tradeoffs in Biological Neural Networks: SelfStabilizing WinnerTakeAll Networks
We initiate a line of investigation into biological neural networks from...
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Recursive Sampling for the Nyström Method
We give the first algorithm for kernel Nyström approximation that runs i...
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Principal Component Projection Without Principal Component Analysis
We show how to efficiently project a vector onto the top principal compo...
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Cameron Musco
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