Anshumali Shrivastava

ORCID

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 166

Top Venues

Publications

Tianyi Zhang, Haoteng Yin, Rongzhe Wei, Pan Li, Anshumali Shrivastava
Learning Scalable Structural Representations for Link Prediction with Bloom Signatures. WWW (2024)
Tianyi Zhang, Jonah Yi, Zhaozhuo Xu, Anshumali Shrivastava
KV Cache is 1 Bit Per Channel: Efficient Large Language Model Inference with Coupled Quantization. CoRR (2024)
Zichang Liu, Qingyun Liu, Yuening Li, Liang Liu, Anshumali Shrivastava, Shuchao Bi, Lichan Hong, Ed H. Chi, Zhe Zhao
Wisdom of Committee: Distilling from Foundation Model to Specialized Application Model. CoRR (2024)
Tianyi Zhang, Anshumali Shrivastava
LeanQuant: Accurate Large Language Model Quantization with Loss-Error-Aware Grid. CoRR (2024)
Aditya Desai, Anshumali Shrivastava
In defense of parameter sharing for model-compression. ICLR (2024)
Tianyi Zhang, Jonah Wonkyu Yi, Bowen Yao, Zhaozhuo Xu, Anshumali Shrivastava
NoMAD-Attention: Efficient LLM Inference on CPUs Through Multiply-add-free Attention. CoRR (2024)
Aditya Desai, Gaurav Gupta, Tianyi Zhang, Anshumali Shrivastava
IDentity with Locality: An ideal hash for gene sequence search. CoRR (2024)
Tianyi Zhang, Haoteng Yin, Rongzhe Wei, Pan Li, Anshumali Shrivastava
Learning Scalable Structural Representations for Link Prediction with Bloom Signatures. CoRR (2023)
Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Ré, Beidi Chen
Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time. ICML (2023)
Zhaozhuo Xu, Zhao Song, Anshumali Shrivastava
A Tale of Two Efficient Value Iteration Algorithms for Solving Linear MDPs with Large Action Space. AISTATS (2023)
John Augustine, Dror Fried, Krishna V. Palem, Duc-Hung Pham, Anshumali Shrivastava
Algorithmic Foundations of Inexact Computing. CoRR (2023)
Aditya Desai, Anshumali Shrivastava
In defense of parameter sharing for model-compression. CoRR (2023)
Gaurav Gupta, Jonah Yi, Benjamin Coleman, Chen Luo, Vihan Lakshman, Anshumali Shrivastava
CAPS: A Practical Partition Index for Filtered Similarity Search. CoRR (2023)
Nicholas Meisburger, Vihan Lakshman, Benito Geordie, Joshua Engels, David Torres Ramos, Pratik Pranav, Benjamin Coleman, Benjamin Meisburger, Shubh Gupta, Yashwanth Adunukota, Siddharth Jain, Tharun Medini, Anshumali Shrivastava
BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Search and Recommendation Models on Commodity CPU Hardware. CIKM (2023)
Zhuang Wang, Zhaozhuo Xu, Anshumali Shrivastava, T. S. Eugene Ng
Zen: Near-Optimal Sparse Tensor Synchronization for Distributed DNN Training. CoRR (2023)
Zichang Liu, Zhaozhuo Xu, Benjamin Coleman, Anshumali Shrivastava
One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning. NeurIPS (2023)
Joshua Engels, Benjamin Coleman, Vihan Lakshman, Anshumali Shrivastava
DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries. NeurIPS (2023)
Zhaozhuo Xu, Zirui Liu, Beidi Chen, Yuxin Tang, Jue Wang, Kaixiong Zhou, Xia Hu, Anshumali Shrivastava
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt. CoRR (2023)
Zichang Liu, Aditya Desai, Fangshuo Liao, Weitao Wang, Victor Xie, Zhaozhuo Xu, Anastasios Kyrillidis, Anshumali Shrivastava
Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time. CoRR (2023)
Bingcong Li, Shuai Zheng, Parameswaran Raman, Anshumali Shrivastava, Georgios B. Giannakis
Contractive error feedback for gradient compression. CoRR (2023)
Anshumali Shrivastava, Vihan Lakshman, Tharun Medini, Nicholas Meisburger, Joshua Engels, David Torres Ramos, Benito Geordie, Pratik Pranav, Shubh Gupta, Yashwanth Adunukota, Siddharth Jain
From Research to Production: Towards Scalable and Sustainable Neural Recommendation Models on Commodity CPU Hardware. RecSys (2023)
Zichang Liu, Aditya Desai, Fangshuo Liao, Weitao Wang, Victor Xie, Zhaozhuo Xu, Anastasios Kyrillidis, Anshumali Shrivastava
Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time. NeurIPS (2023)
Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson
Learning Multimodal Data Augmentation in Feature Space. ICLR (2023)
Shabnam Daghaghi, Benjamin Coleman, Benito Geordie, Anshumali Shrivastava
Adaptive Sampling for Deep Learning via Efficient Nonparametric Proxies. CoRR (2023)
Aditya Desai, Benjamin Meisburger, Zichang Liu, Anshumali Shrivastava
Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random Subspaces. CoRR (2023)
Aditya Desai, Keren Zhou, Anshumali Shrivastava
Hardware-Aware Compression with Random Operation Access Specific Tile (ROAST) Hashing. ICML (2023)
Benjamin Coleman, David Torres Ramos, Vihan Lakshman, Chen Luo, Anshumali Shrivastava
CARAMEL: A Succinct Read-Only Lookup Table via Compressed Static Functions. CoRR (2023)
Anshumali Shrivastava, Zhao Song, Zhaozhuo Xu
A Theoretical Analysis Of Nearest Neighbor Search On Approximate Near Neighbor Graph. CoRR (2023)
Nicholas Meisburger, Vihan Lakshman, Benito Geordie, Joshua Engels, David Torres Ramos, Pratik Pranav, Benjamin Coleman, Benjamin Meisburger, Shubh Gupta, Yashwanth Adunukota, Tharun Medini, Anshumali Shrivastava
BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Neural Networks on Commodity CPU Hardware. CoRR (2023)
Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Ré, Beidi Chen
Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time. CoRR (2023)
Tianyi Zhang, Zhenwei Dai, Zhaozhuo Xu, Anshumali Shrivastava
Graph Self-supervised Learning via Proximity Distribution Minimization. UAI (2023)
Gaurav Gupta, Anup B. Rao, Tung Mai, Ryan A. Rossi, Xiang Chen, Saayan Mitra, Anshumali Shrivastava
Near Neighbor Search for Constraint Queries. BigData (2023)
Zichang Liu, Zhaozhuo Xu, Alan Baonan Ji, Junyan Zhang, Jonathan Li, Beidi Chen, Anshumali Shrivastava
HALOS: Hashing Large Output Space for Cheap Inference. MLSys (2022)
Aditya Desai, Keren Zhou, Anshumali Shrivastava
Efficient model compression with Random Operation Access Specific Tile (ROAST) hashing. CoRR (2022)
Zhenwei Dai, Anshumali Shrivastava, Pedro Reviriego, José Alberto Hernández
Optimizing Learned Bloom Filters: How Much Should Be Learned? IEEE Embed. Syst. Lett. 14 (3) (2022)
Aditya Desai, Anshumali Shrivastava
The trade-offs of model size in large recommendation models : A 10000 × compressed criteo-tb DLRM model (100 GB parameters to mere 10MB). CoRR (2022)
Constantinos Chamzas, Aedan Cullen, Anshumali Shrivastava, Lydia E. Kavraki
Learning to Retrieve Relevant Experiences for Motion Planning. ICRA (2022)
Zhuang Wang, Zhaozhuo Xu, Xinyu Crystal Wu, Anshumali Shrivastava, T. S. Eugene Ng
DRAGONN: Distributed Randomized Approximate Gradients of Neural Networks. ICML (2022)
Gaurav Gupta, Tharun Medini, Anshumali Shrivastava, Alexander J. Smola
BLISS: A Billion scale Index using Iterative Re-partitioning. KDD (2022)
Joshua Engels, Benjamin Coleman, Vihan Lakshman, Anshumali Shrivastava
DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries. CoRR (2022)
Tianyi Zhang, Zhaozhuo Xu, Tharun Medini, Anshumali Shrivastava
Structural Contrastive Representation Learning for Zero-shot Multi-label Text Classification. EMNLP (Findings) (2022)
Constantinos Chamzas, Aedan Cullen, Anshumali Shrivastava, Lydia E. Kavraki
Learning to Retrieve Relevant Experiences for Motion Planning. CoRR (2022)
Aditya Desai, Anshumali Shrivastava
The trade-offs of model size in large recommendation models : 100GB to 10MB Criteo-tb DLRM model. NeurIPS (2022)
Benjamin Coleman, Benito Geordie, Li Chou, Ryan A. Leo Elworth, Todd J. Treangen, Anshumali Shrivastava
One-Pass Diversified Sampling with Application to Terabyte-Scale Genomic Sequence Streams. ICML (2022)
Benjamin Coleman, Santiago Segarra, Alexander J. Smola, Anshumali Shrivastava
Graph Reordering for Cache-Efficient Near Neighbor Search. NeurIPS (2022)
Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson
Learning Multimodal Data Augmentation in Feature Space. CoRR (2022)
Aditya Desai, Li Chou, Anshumali Shrivastava
Random Offset Block Embedding (ROBE) for compressed embedding tables in deep learning recommendation systems. MLSys (2022)
Chen Luo, Vihan Lakshman, Anshumali Shrivastava, Tianyu Cao, Sreyashi Nag, Rahul Goutam, Hanqing Lu, Yiwei Song, Bing Yin
ROSE: Robust Caches for Amazon Product Search. WWW (Companion Volume) (2022)
Klara Nahrstedt, Naresh R. Shanbhag, Vikram S. Adve, Nancy M. Amato, Romit Roy Choudhury, Carl A. Gunter, Nam Sung Kim, Olgica Milenkovic, Sayan Mitra, Lav R. Varshney, Yurii Vlasov, Sarita V. Adve, Rashid Bashir, Andreas Cangellaris, James DiCarlo, Katie Driggs Campbell, Nick Feamster, Mattia Gazzola, Karrie Karahalios, Sanmi Koyejo, Paul Kwiat, Bo Li, Negar Mehr, Ravish Mehra, Andrew Miller, Daniela Rus, Alexander G. Schwing, Anshumali Shrivastava
Coordinated Science Laboratory 70th Anniversary Symposium: The Future of Computing. CoRR (2022)
Minghao Yan, Nicholas Meisburger, Tharun Medini, Anshumali Shrivastava
Distributed SLIDE: Enabling Training Large Neural Networks on Low Bandwidth and Simple CPU-Clusters via Model Parallelism and Sparsity. CoRR (2022)
Zichang Liu, Benjamin Coleman, Tianyi Zhang, Anshumali Shrivastava
Retaining Knowledge for Learning with Dynamic Definition. NeurIPS (2022)
Shabnam Daghaghi, Nicholas Meisburger, Mengnan Zhao, Yong Wu, Sameh Gobriel, Charlie Tai, Anshumali Shrivastava
Accelerating SLIDE Deep Learning on Modern CPUs: Vectorization, Quantizations, Memory Optimizations, and More. CoRR (2021)
Benjamin Coleman, Anshumali Shrivastava
A One-Pass Distributed and Private Sketch for Kernel Sums with Applications to Machine Learning at Scale. CCS (2021)
John Chen, Benjamin Coleman, Anshumali Shrivastava
Revisiting Consistent Hashing with Bounded Loads. AAAI (2021)
Joshua Engels, Benjamin Coleman, Anshumali Shrivastava
Practical Near Neighbor Search via Group Testing. NeurIPS (2021)
Benjamin Coleman, Santiago Segarra, Anshumali Shrivastava, Alex Smola
Graph Reordering for Cache-Efficient Near Neighbor Search. CoRR (2021)
Zichang Liu, Benjamin Coleman, Anshumali Shrivastava
Efficient Inference via Universal LSH Kernel. CoRR (2021)
Zhaozhuo Xu, Alan Baonan Ji, Andrew Woods, Beidi Chen, Anshumali Shrivastava
Satellite Images and Deep Learning to Identify Discrepancy in Mailing Addresses with Applications to Census 2020 in Houston. CoRR (2021)
Anastasios Kyrillidis, Anshumali Shrivastava, Moshe Y. Vardi, Zhiwei Zhang
Solving hybrid Boolean constraints in continuous space via multilinear Fourier expansions. Artif. Intell. 299 (2021)
Pedro Reviriego, José Alberto Hernández, Zhenwei Dai, Anshumali Shrivastava
Learned Bloom Filters in Adversarial Environments: A Malicious URL Detection Use-Case. HPSR (2021)
Zhenwei Dai, Chen Dun, Yuxin Tang, Anastasios Kyrillidis, Anshumali Shrivastava
Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks. CoRR (2021)
Gaurav Gupta, Tharun Medini, Anshumali Shrivastava, Alexander J. Smola
IRLI: Iterative Re-partitioning for Learning to Index. CoRR (2021)
Zhaozhuo Xu, Beidi Chen, Chaojian Li, Weiyang Liu, Le Song, Yingyan Lin, Anshumali Shrivastava
Locality Sensitive Teaching. NeurIPS (2021)
Aditya Desai, Benjamin Coleman, Anshumali Shrivastava
Density Sketches for Sampling and Estimation. CoRR (2021)
Anshumali Shrivastava, Zhao Song, Zhaozhuo Xu
Sublinear Least-Squares Value Iteration via Locality Sensitive Hashing. CoRR (2021)
Tharun Medini, Beidi Chen, Anshumali Shrivastava
SOLAR: Sparse Orthogonal Learned and Random Embeddings. ICLR (2021)
Gaurav Gupta, Minghao Yan, Benjamin Coleman, Bryce Kille, Ryan A. Leo Elworth, Tharun Medini, Todd J. Treangen, Anshumali Shrivastava
Fast Processing and Querying of 170TB of Genomics Data via a Repeated And Merged BloOm Filter (RAMBO). SIGMOD Conference (2021)
Aditya Desai, Li Chou, Anshumali Shrivastava
Random Offset Block Embedding Array (ROBE) for CriteoTB Benchmark MLPerf DLRM Model : 1000× Compression and 2.7× Faster Inference. CoRR (2021)
Aditya Desai, Zhaozhuo Xu, Menal Gupta, Anu Chandran, Antoine Vial-Aussavy, Anshumali Shrivastava
Raw Nav-merge Seismic Data to Subsurface Properties with MLP based Multi-Modal Information Unscrambler. NeurIPS (2021)
Joshua Engels, Benjamin Coleman, Anshumali Shrivastava
Practical Near Neighbor Search via Group Testing. CoRR (2021)
Constantinos Chamzas, Zachary K. Kingston, Carlos Quintero-Peña, Anshumali Shrivastava, Lydia E. Kavraki
Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions. ICRA (2021)
Li Chou, Zichang Liu, Zhuang Wang, Anshumali Shrivastava
Efficient and Less Centralized Federated Learning. ECML/PKDD (1) (2021)
Zichang Liu, Li Chou, Anshumali Shrivastava
Neighbor Oblivious Learning (NObLe) for Device Localization and Tracking. DATE (2021)
Shabnam Daghaghi, Nicholas Meisburger, Mengnan Zhao, Anshumali Shrivastava
Accelerating SLIDE Deep Learning on Modern CPUs: Vectorization, Quantizations, Memory Optimizations, and More. MLSys (2021)
Zhaozhuo Xu, Minghao Yan, Junyan Zhang, Anshumali Shrivastava
PairConnect: A Compute-Efficient MLP Alternative to Attention. CoRR (2021)
Zhenwei Dai, Aditya Desai, Reinhard Heckel, Anshumali Shrivastava
Active Sampling Count Sketch (ASCS) for Online Sparse Estimation of a Trillion Scale Covariance Matrix. SIGMOD Conference (2021)
Li Chou, Zichang Liu, Zhuang Wang, Anshumali Shrivastava
Efficient and Less Centralized Federated Learning. CoRR (2021)
Shabnam Daghaghi, Tharun Medini, Nicholas Meisburger, Beidi Chen, Mengnan Zhao, Anshumali Shrivastava
A Tale of Two Efficient and Informative Negative Sampling Distributions. ICML (2021)
Anshumali Shrivastava, Zhao Song, Zhaozhuo Xu
Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structures. CoRR (2021)
Beidi Chen, Zichang Liu, Binghui Peng, Zhaozhuo Xu, Jonathan Lingjie Li, Tri Dao, Zhao Song, Anshumali Shrivastava, Christopher Ré
MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training. ICLR (2021)
Shabnam Daghaghi, Tharun Medini, Anshumali Shrivastava
SDM-Net: A simple and effective model for generalized zero-shot learning. UAI (2021)
Zhaozhuo Xu, Zhao Song, Anshumali Shrivastava
Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structures. NeurIPS (2021)
Zhaozhuo Xu, Aditya Desai, Menal Gupta, Anu Chandran, Antoine Vial-Aussavy, Anshumali Shrivastava
Beyond Convolutions: A Novel Deep Learning Approach for Raw Seismic Data Ingestion. CoRR (2021)
Aditya Desai, Yanzhou Pan, Kuangyuan Sun, Li Chou, Anshumali Shrivastava
Semantically Constrained Memory Allocation (SCMA) for Embedding in Efficient Recommendation Systems. CoRR (2021)
Nicholas Meisburger, Anshumali Shrivastava
Distributed Tera-Scale Similarity Search with MPI: Provably Efficient Similarity Search over billions without a Single Distance Computation. CoRR (2020)
Shabnam Daghaghi, Tharun Medini, Beidi Chen, Mengnan Zhao, Anshumali Shrivastava
A Constant-time Adaptive Negative Sampling. CoRR (2020)
Zichang Liu, Zhaozhuo Xu, Alan Baonan Ji, Jonathan Li, Beidi Chen, Anshumali Shrivastava
Climbing the WOL: Training for Cheaper Inference. CoRR (2020)
Beidi Chen, Weiyang Liu, Zhiding Yu, Jan Kautz, Anshumali Shrivastava, Animesh Garg, Animashree Anandkumar
Angular Visual Hardness. ICML (2020)
Constantinos Chamzas, Zachary K. Kingston, Carlos Quintero-Peña, Anshumali Shrivastava, Lydia E. Kavraki
Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions. CoRR (2020)
Benjamin Coleman, Gaurav Gupta, John Chen, Anshumali Shrivastava
STORM: Foundations of End-to-End Empirical Risk Minimization on the Edge. CoRR (2020)
Zhenwei Dai, Anshumali Shrivastava
Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier with Application to Real-Time Information Filtering on the Web. NeurIPS (2020)
Sicong Liu, Junzhao Du, Anshumali Shrivastava, Lin Zhong
Privacy Adversarial Network: Representation Learning for Mobile Data Privacy. CoRR (2020)
Benjamin Coleman, Anshumali Shrivastava
A One-Pass Private Sketch for Most Machine Learning Tasks. CoRR (2020)
Zhenwei Dai, Aditya Desai, Reinhard Heckel, Anshumali Shrivastava
Active Sampling Count Sketch (ASCS) for Online Sparse Estimation of a Trillion Scale Covariance Matrix. CoRR (2020)
Ryan Spring, Anshumali Shrivastava
Mutual Information Estimation using LSH Sampling. IJCAI (2020)
Anastasios Kyrillidis, Anshumali Shrivastava, Moshe Y. Vardi, Zhiwei Zhang
FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints. AAAI (2020)
Benjamin Coleman, Richard G. Baraniuk, Anshumali Shrivastava
Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data. ICML (2020)
Zichang Liu, Li Chou, Anshumali Shrivastava
Neighbor Oblivious Learning (NObLe) for Device Localization and Tracking. CoRR (2020)
Louis Abraham, Gary Bécigneul, Benjamin Coleman, Bernhard Schölkopf, Anshumali Shrivastava, Alexander J. Smola
Bloom Origami Assays: Practical Group Testing. CoRR (2020)
Tharun Medini, Beidi Chen, Anshumali Shrivastava
SOLAR: Sparse Orthogonal Learned and Random Embeddings. CoRR (2020)

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Publication Activity (10 Years)

Top Topics

Top Venues

Anshumali Shrivastava