2 citations · 4 across the 3 of their papers we have counts for
5 papers
Finding the Mode of a Kernel Density Estimate
Jasper C. H. Lee, Jerry Li, Christopher Musco +2
Given points in , how do we find a point which maximizes ? In other words, how do we find the maxim…
Approximate Guarantees for Dictionary Learning
Aditya Bhaskara, Wai Ming Tai
In the dictionary learning (or sparse coding) problem, we are given a collection of signals (vectors in ), and the goal is to find a "basis" in which the signals have…
Learning In Practice: Reasoning About Quantization
Annie Cherkaev, Waiming Tai, Jeff Phillips +1
There is a mismatch between the standard theoretical analyses of statistical machine learning and how learning is used in practice. The foundational assumption supporting the theor…
The GaussianSketch for Almost Relative Error Kernel Distance
Jeff M. Phillips, Wai Ming Tai
We introduce two versions of a new sketch for approximately embedding the Gaussian kernel into Euclidean inner product space. These work by truncating infinite expansions of the Ga…
Improved Coresets for Kernel Density Estimates
Jeff M. Phillips, Wai Ming Tai
We study the construction of coresets for kernel density estimates. That is we show how to approximate the kernel density estimate described by a large point set with another kerne…