Search Results for author: Benjamin Coleman

Found 17 papers, 4 papers with code

Adaptive Sampling for Deep Learning via Efficient Nonparametric Proxies

no code implementations22 Nov 2023 Shabnam Daghaghi, Benjamin Coleman, Benito Geordie, Anshumali Shrivastava

To address this problem, we propose a novel sampling distribution based on nonparametric kernel regression that learns an effective importance score as the neural network trains.

regression

CAPS: A Practical Partition Index for Filtered Similarity Search

no code implementations29 Aug 2023 Gaurav Gupta, Jonah Yi, Benjamin Coleman, Chen Luo, Vihan Lakshman, Anshumali Shrivastava

With the surging popularity of approximate near-neighbor search (ANNS), driven by advances in neural representation learning, the ability to serve queries accompanied by a set of constraints has become an area of intense interest.

Representation Learning

STORM: Sketch Toward Online Risk Minimization

no code implementations29 Sep 2021 Gaurav Gupta, Benjamin Coleman, John Chen, Anshumali Shrivastava

To this end, we propose STORM, an online sketching-based method for empirical risk minimization.

Classification regression

Efficient Inference via Universal LSH Kernel

no code implementations21 Jun 2021 Zichang Liu, Benjamin Coleman, Anshumali Shrivastava

Large machine learning models achieve unprecedented performance on various tasks and have evolved as the go-to technique.

Knowledge Distillation Quantization

Density Sketches for Sampling and Estimation

no code implementations24 Feb 2021 Aditya Desai, Benjamin Coleman, Anshumali Shrivastava

We introduce Density sketches (DS): a succinct online summary of the data distribution.

Bloom Origami Assays: Practical Group Testing

no code implementations21 Jul 2020 Louis Abraham, Gary Becigneul, Benjamin Coleman, Bernhard Scholkopf, Anshumali Shrivastava, Alexander Smola

Group testing is a well-studied problem with several appealing solutions, but recent biological studies impose practical constraints for COVID-19 that are incompatible with traditional methods.

A One-Pass Private Sketch for Most Machine Learning Tasks

no code implementations16 Jun 2020 Benjamin Coleman, Anshumali Shrivastava

Existing methods for DP kernel density estimation scale poorly, often exponentially slower with an increase in dimensions.

BIG-bench Machine Learning Density Estimation

Sub-linear RACE Sketches for Approximate Kernel Density Estimation on Streaming Data

no code implementations4 Dec 2019 Benjamin Coleman, Anshumali Shrivastava

We evaluate our method on real-world high-dimensional datasets and show that our sketch achieves 10x better compression compared to competing methods.

Density Estimation

Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data

no code implementations18 Feb 2019 Benjamin Coleman, Richard G. Baraniuk, Anshumali Shrivastava

We present the first sublinear memory sketch that can be queried to find the nearest neighbors in a dataset.

Density Estimation

Cannot find the paper you are looking for? You can Submit a new open access paper.