258 citations · 935 across the 92 of their papers we have counts for
15 papers · 1 filter
Sub-linear RACE Sketches for Approximate Kernel Density Estimation on Streaming Data
Benjamin Coleman, Anshumali Shrivastava
Kernel density estimation is a simple and effective method that lies at the heart of many important machine learning applications. Unfortunately, kernel methods scale poorly for la…
Angular Visual Hardness
Beidi Chen, Weiyang Liu, Zhiding Yu +4
Recent convolutional neural networks (CNNs) have led to impressive performance but often suffer from poor calibration. They tend to be overconfident, with the model confidence not…
FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints
Anastasios Kyrillidis, Anshumali Shrivastava, Moshe Y. Vardi +1
The Boolean SATisfiability problem (SAT) is of central importance in computer science. Although SAT is known to be NP-complete, progress on the engineering side, especially that of…
Lsh-sampling Breaks the Computation Chicken-and-egg Loop in Adaptive Stochastic Gradient Estimation
Beidi Chen, Yingchen Xu, Anshumali Shrivastava
Stochastic Gradient Descent or SGD is the most popular optimization algorithm for large-scale problems. SGD estimates the gradient by uniform sampling with sample size one. There h…
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products
Tharun Medini, Qixuan Huang, Yiqiu Wang +2
In the last decade, it has been shown that many hard AI tasks, especially in NLP, can be naturally modeled as extreme classification problems leading to improved precision. However…
Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier
Zhenwei Dai, Anshumali Shrivastava
Recent work suggests improving the performance of Bloom filter by incorporating a machine learning model as a binary classifier. However, such learned Bloom filter does not take fu…