Anshumali Shrivastava

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cornell university ∙ Rice University

Assistant Professor of Department of Computer Science at Rice University since 2015, Professor at Rice University since 2015, Ph.D. in Computer Science at Cornell University from2010-2015, Analytic Software Scientist at Fair Isaac from 2008-2010

Graph Reordering for Cache-Efficient Near Neighbor Search

Graph search is one of the most successful algorithmic trends in near ne...
04/07/2021 ∙ by Benjamin Coleman, et al. ∙ 0 ∙ share

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IRLI: Iterative Re-partitioning for Learning to Index

Neural models have transformed the fundamental information retrieval pro...
03/17/2021 ∙ by Gaurav Gupta, et al. ∙ 8 ∙ share

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Accelerating SLIDE Deep Learning on Modern CPUs: Vectorization, Quantizations, Memory Optimizations, and More

Deep learning implementations on CPUs (Central Processing Units) are gai...
03/06/2021 ∙ by Shabnam Daghaghi, et al. ∙ 0 ∙ share

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Beyond Convolutions: A Novel Deep Learning Approach for Raw Seismic Data Ingestion

Traditional seismic processing workflows (SPW) are expensive, requiring ...
02/26/2021 ∙ by Zhaozhuo Xu, et al. ∙ 17 ∙ share

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Semantically Constrained Memory Allocation (SCMA) for Embedding in Efficient Recommendation Systems

Deep learning-based models are utilized to achieve state-of-the-art perf...
02/24/2021 ∙ by Aditya Desai, et al. ∙ 0 ∙ share

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Density Sketches for Sampling and Estimation

We introduce Density sketches (DS): a succinct online summary of the dat...
02/24/2021 ∙ by Aditya Desai, et al. ∙ 5 ∙ share

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A Constant-time Adaptive Negative Sampling

Softmax classifiers with a very large number of classes naturally occur ...
12/31/2020 ∙ by Shabnam Daghaghi, et al. ∙ 13 ∙ share

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Active Sampling Count Sketch (ASCS) for Online Sparse Estimation of a Trillion Scale Covariance Matrix

Estimating and storing the covariance (or correlation) matrix of high-di...
10/29/2020 ∙ by Zhenwei Dai, et al. ∙ 0 ∙ share

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Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions

Earlier work has shown that reusing experience from prior motion plannin...
10/29/2020 ∙ by Constantinos Chamzas, et al. ∙ 0 ∙ share

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SOLAR: Sparse Orthogonal Learned and Random Embeddings

Dense embedding models are commonly deployed in commercial search engine...
08/30/2020 ∙ by Tharun Medini, et al. ∙ 11 ∙ share

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Distributed Tera-Scale Similarity Search with MPI: Provably Efficient Similarity Search over billions without a Single Distance Computation

We present SLASH (Sketched LocAlity Sensitive Hashing), an MPI (Message ...
08/05/2020 ∙ by Nicholas Meisburger, et al. ∙ 0 ∙ share

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Bloom Origami Assays: Practical Group Testing

We study the problem usually referred to as group testing in the context...
07/21/2020 ∙ by Louis Abraham, et al. ∙ 0 ∙ share

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Climbing the WOL: Training for Cheaper Inference

Efficient inference for wide output layers (WOLs) is an essential yet ch...
07/02/2020 ∙ by Zichang Liu, et al. ∙ 0 ∙ share

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STORM: Foundations of End-to-End Empirical Risk Minimization on the Edge

Empirical risk minimization is perhaps the most influential idea in stat...
06/25/2020 ∙ by Benjamin Coleman, et al. ∙ 6 ∙ share

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A One-Pass Private Sketch for Most Machine Learning Tasks

Differential privacy (DP) is a compelling privacy definition that explai...
06/16/2020 ∙ by Benjamin Coleman, et al. ∙ 1 ∙ share

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Privacy Adversarial Network: Representation Learning for Mobile Data Privacy

The remarkable success of machine learning has fostered a growing number...
06/08/2020 ∙ by Sicong Liu, et al. ∙ 32 ∙ share

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

Kernel density estimation is a simple and effective method that lies at ...
12/04/2019 ∙ by Benjamin Coleman, et al. ∙ 0 ∙ share

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Angular Visual Hardness

Although convolutional neural networks (CNNs) are inspired by the mechan...
12/04/2019 ∙ by Beidi Chen, et al. ∙ 66 ∙ share

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FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints

The Boolean SATisfiability problem (SAT) is of central importance in com...
12/02/2019 ∙ by Anastasios Kyrillidis, et al. ∙ 0 ∙ share

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Lsh-sampling Breaks the Computation Chicken-and-egg Loop in Adaptive Stochastic Gradient Estimation

Stochastic Gradient Descent or SGD is the most popular optimization algo...
10/30/2019 ∙ by Beidi Chen, et al. ∙ 14 ∙ share

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Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products

In the last decade, it has been shown that many hard AI tasks, especiall...
10/28/2019 ∙ by Tharun Medini, et al. ∙ 22 ∙ share

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Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier

Recent work suggests improving the performance of Bloom filter by incorp...
10/21/2019 ∙ by Zhenwei Dai, et al. ∙ 0 ∙ share

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RAMBO: Repeated And Merged Bloom Filter for Multiple Set Membership Testing (MSMT) in Sub-linear time

Approximate set membership is a common problem with wide applications in...
10/07/2019 ∙ by Gaurav Gupta, et al. ∙ 0 ∙ share

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Semantic Similarity Based Softmax Classifier for Zero-Shot Learning

Zero-Shot Learning (ZSL) is a classification task where we do not have e...
09/10/2019 ∙ by Shabnam Daghaghi, et al. ∙ 1 ∙ share

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Revisiting Consistent Hashing with Bounded Loads

Dynamic load balancing lies at the heart of distributed caching. Here, t...
08/23/2019 ∙ by John Chen, et al. ∙ 0 ∙ share

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Using Local Experiences for Global Motion Planning

Sampling-based planners are effective in many real-world applications su...
03/20/2019 ∙ by Constantinos Chamzas, et al. ∙ 12 ∙ share

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SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems

Deep Learning (DL) algorithms are the central focus of modern machine le...
03/07/2019 ∙ by Beidi Chen, et al. ∙ 0 ∙ share

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RACE: Sub-Linear Memory Sketches for Approximate Near-Neighbor Search on Streaming Data

We demonstrate the first possibility of a sub-linear memory sketch for s...
02/18/2019 ∙ by Benjamin Coleman, et al. ∙ 0 ∙ share

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Compressing Gradient Optimizers via Count-Sketches

Many popular first-order optimization methods (e.g., Momentum, AdaGrad, ...
02/01/2019 ∙ by Ryan Spring, et al. ∙ 4 ∙ share

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Better accuracy with quantified privacy: representations learned via reconstructive adversarial network

The remarkable success of machine learning, especially deep learning, ha...
01/25/2019 ∙ by Sicong Liu, et al. ∙ 4 ∙ share

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Probabilistic Blocking with An Application to the Syrian Conflict

Entity resolution seeks to merge databases as to remove duplicate entrie...
10/11/2018 ∙ by Rebecca C. Steorts, et al. ∙ 0 ∙ share

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Extreme Classification in Log Memory

We present Merged-Averaged Classifiers via Hashing (MACH) for K-classifi...
10/09/2018 ∙ by Qixuan Huang, et al. ∙ 0 ∙ share

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MISSION: Ultra Large-Scale Feature Selection using Count-Sketches

Feature selection is an important challenge in machine learning. It play...
06/12/2018 ∙ by Amirali Aghazadeh, et al. ∙ 0 ∙ share

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Scaling-up Split-Merge MCMC with Locality Sensitive Sampling (LSS)

Split-Merge MCMC (Monte Carlo Markov Chain) is one of the essential and ...
02/21/2018 ∙ by Chen Luo, et al. ∙ 0 ∙ share

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Unique Entity Estimation with Application to the Syrian Conflict

Entity resolution identifies and removes duplicate entities in large, no...
10/07/2017 ∙ by Beidi Chen, et al. ∙ 0 ∙ share

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FLASH: Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search

We present FLASH ( Fast LSH Algorithm for Similarity search accelerat...
09/04/2017 ∙ by Yiqiu Wang, et al. ∙ 0 ∙ share

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Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer

Dependency graph, as a heterogeneous graph representing the intrinsic re...
08/25/2017 ∙ by Chen Luo, et al. ∙ 0 ∙ share

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Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups

Anomaly detection is one of the frequent and important subroutines deplo...
06/20/2017 ∙ by Chen Luo, et al. ∙ 0 ∙ share

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A New Unbiased and Efficient Class of LSH-Based Samplers and Estimators for Partition Function Computation in Log-Linear Models

Log-linear models are arguably the most successful class of graphical mo...
03/15/2017 ∙ by Ryan Spring, et al. ∙ 0 ∙ share

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Revisiting Winner Take All (WTA) Hashing for Sparse Datasets

WTA (Winner Take All) hashing has been successfully applied in many larg...
12/06/2016 ∙ by Beidi Chen, et al. ∙ 0 ∙ share

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Scalable and Sustainable Deep Learning via Randomized Hashing

Current deep learning architectures are growing larger in order to learn...
02/26/2016 ∙ by Ryan Spring, et al. ∙ 0 ∙ share

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2-Bit Random Projections, NonLinear Estimators, and Approximate Near Neighbor Search

The method of random projections has become a standard tool for machine ...
02/21/2016 ∙ by Ping Li, et al. ∙ 0 ∙ share

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Asymmetric Minwise Hashing

Minwise hashing (Minhash) is a widely popular indexing scheme in practic...
11/14/2014 ∙ by Anshumali Shrivastava, et al. ∙ 0 ∙ share

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Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS)

Recently it was shown that the problem of Maximum Inner Product Search (...
10/20/2014 ∙ by Anshumali Shrivastava, et al. ∙ 0 ∙ share

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In Defense of MinHash Over SimHash

MinHash and SimHash are the two widely adopted Locality Sensitive Hashin...
07/16/2014 ∙ by Anshumali Shrivastava, et al. ∙ 0 ∙ share

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Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)

We present the first provably sublinear time algorithm for approximate M...
05/22/2014 ∙ by Anshumali Shrivastava, et al. ∙ 0 ∙ share

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Graph Kernels via Functional Embedding

We propose a representation of graph as a functional object derived from...
04/21/2014 ∙ by Anshumali Shrivastava, et al. ∙ 0 ∙ share

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A New Space for Comparing Graphs

Finding a new mathematical representations for graph, which allows direc...
04/17/2014 ∙ by Anshumali Shrivastava, et al. ∙ 0 ∙ share

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Training Logistic Regression and SVM on 200GB Data Using b-Bit Minwise Hashing and Comparisons with Vowpal Wabbit (VW)

We generated a dataset of 200 GB with 10^9 features, to test our recent ...
08/15/2011 ∙ by Ping Li, et al. ∙ 0 ∙ share

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Hashing Algorithms for Large-Scale Learning

In this paper, we first demonstrate that b-bit minwise hashing, whose es...
06/06/2011 ∙ by Ping Li, et al. ∙ 0 ∙ share

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Anshumali Shrivastava

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