
Linear Space Streaming Lower Bounds for Approximating CSPs
We consider the approximability of constraint satisfaction problems in t...
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Scaling up Kernel Ridge Regression via Locality Sensitive Hashing
Random binning features, introduced in the seminal paper of Rahimi and R...
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Pure Differentially Private Summation from Anonymous Messages
The shuffled (aka anonymous) model has recently generated significant in...
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Private Aggregation from Fewer Anonymous Messages
Consider the setup where n parties are each given a number x_i ∈F_q and ...
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Oblivious Sketching of HighDegree Polynomial Kernels
Kernel methods are fundamental tools in machine learning that allow dete...
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Private Heavy Hitters and Range Queries in the Shuffled Model
An exciting new development in differential privacy is the shuffled mode...
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Scalable and Differentially Private Distributed Aggregation in the Shuffled Model
Federated learning promises to make machine learning feasible on distrib...
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Dimensionindependent Sparse Fourier Transform
The Discrete Fourier Transform (DFT) is a fundamental computational prim...
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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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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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Ameya Velingker
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