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Books and Theses
- 2015
[b1]Anshumali Shrivastava:
Probabilistic Hashing Techniques for Big Data. Cornell University, USA, 2015
Journal Articles
- 2022
- [j4]Zhenwei Dai, Anshumali Shrivastava, Pedro Reviriego
, José Alberto Hernández:
Optimizing Learned Bloom Filters: How Much Should Be Learned? IEEE Embed. Syst. Lett. 14(3): 123-126 (2022) - 2021
- [j3]Anastasios Kyrillidis, Anshumali Shrivastava, Moshe Y. Vardi, Zhiwei Zhang:
Solving hybrid Boolean constraints in continuous space via multilinear Fourier expansions. Artif. Intell. 299: 103559 (2021) - 2019
- [j2]Sicong Liu, Junzhao Du, Anshumali Shrivastava, Lin Zhong:
Privacy Adversarial Network: Representation Learning for Mobile Data Privacy. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3(4): 144:1-144:18 (2019) - [j1]Andrew M. Wells, Neil T. Dantam, Anshumali Shrivastava, Lydia E. Kavraki:
Learning Feasibility for Task and Motion Planning in Tabletop Environments. IEEE Robotics Autom. Lett. 4(2): 1255-1262 (2019)
Conference and Workshop Papers
- 2024
- [c88]Aditya Desai, Anshumali Shrivastava:
In defense of parameter sharing for model-compression. ICLR 2024 - [c87]Zhaozhuo Xu, Zirui Liu, Beidi Chen, Shaochen Zhong, Yuxin Tang, Jue Wang, Kaixiong Zhou, Xia Hu, Anshumali Shrivastava:
Soft Prompt Recovers Compressed LLMs, Transferably. ICML 2024 - [c86]Tianyi Zhang, Haoteng Yin, Rongzhe Wei, Pan Li, Anshumali Shrivastava:
Learning Scalable Structural Representations for Link Prediction with Bloom Signatures. WWW 2024: 980-991 - 2023
- [c85]Zhaozhuo Xu, Zhao Song, Anshumali Shrivastava:
A Tale of Two Efficient Value Iteration Algorithms for Solving Linear MDPs with Large Action Space. AISTATS 2023: 788-836 - [c84]Gaurav Gupta, Anup B. Rao, Tung Mai, Ryan A. Rossi, Xiang Chen, Saayan Mitra, Anshumali Shrivastava:
Near Neighbor Search for Constraint Queries. IEEE Big Data 2023: 1707-1715 - [c83]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: 4738-4744 - [c82]Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson:
Learning Multimodal Data Augmentation in Feature Space. ICLR 2023 - [c81]Aditya Desai, Keren Zhou, Anshumali Shrivastava:
Hardware-Aware Compression with Random Operation Access Specific Tile (ROAST) Hashing. ICML 2023: 7732-7749 - [c80]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: 22137-22176 - [c79]Joshua Engels, Benjamin Coleman, Vihan Lakshman, Anshumali Shrivastava:
DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries. NeurIPS 2023 - [c78]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 - [c77]Zichang Liu, Zhaozhuo Xu, Benjamin Coleman, Anshumali Shrivastava:
One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning. NeurIPS 2023 - [c76]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: 1071-1074 - [c75]Tianyi Zhang, Zhenwei Dai, Zhaozhuo Xu, Anshumali Shrivastava:
Graph Self-supervised Learning via Proximity Distribution Minimization. UAI 2023: 2498-2508 - 2022
- [c74]Tianyi Zhang, Zhaozhuo Xu, Tharun Medini, Anshumali Shrivastava:
Structural Contrastive Representation Learning for Zero-shot Multi-label Text Classification. EMNLP (Findings) 2022: 4937-4947 - [c73]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: 4202-4218 - [c72]Zhuang Wang, Zhaozhuo Xu, Xinyu Crystal Wu, Anshumali Shrivastava, T. S. Eugene Ng:
DRAGONN: Distributed Randomized Approximate Gradients of Neural Networks. ICML 2022: 23274-23291 - [c71]Constantinos Chamzas, Aedan Cullen, Anshumali Shrivastava, Lydia E. Kavraki:
Learning to Retrieve Relevant Experiences for Motion Planning. ICRA 2022: 7233-7240 - [c70]Gaurav Gupta, Tharun Medini, Anshumali Shrivastava, Alexander J. Smola:
BLISS: A Billion scale Index using Iterative Re-partitioning. KDD 2022: 486-495 - [c69]Aditya Desai, Li Chou, Anshumali Shrivastava:
Random Offset Block Embedding (ROBE) for compressed embedding tables in deep learning recommendation systems. MLSys 2022 - [c68]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 - [c67]Benjamin Coleman, Santiago Segarra, Alexander J. Smola, Anshumali Shrivastava:
Graph Reordering for Cache-Efficient Near Neighbor Search. NeurIPS 2022 - [c66]Aditya Desai, Anshumali Shrivastava:
The trade-offs of model size in large recommendation models : 100GB to 10MB Criteo-tb DLRM model. NeurIPS 2022 - [c65]Zichang Liu, Benjamin Coleman, Tianyi Zhang, Anshumali Shrivastava:
Retaining Knowledge for Learning with Dynamic Definition. NeurIPS 2022 - [c64]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: 89-93 - 2021
- [c63]John Chen, Benjamin Coleman, Anshumali Shrivastava:
Revisiting Consistent Hashing with Bounded Loads. AAAI 2021: 3976-3983 - [c62]Benjamin Coleman, Anshumali Shrivastava:
A One-Pass Distributed and Private Sketch for Kernel Sums with Applications to Machine Learning at Scale. CCS 2021: 3252-3265 - [c61]Zichang Liu, Li Chou, Anshumali Shrivastava:
Neighbor Oblivious Learning (NObLe) for Device Localization and Tracking. DATE 2021: 1426-1429 - [c60]Pedro Reviriego, José Alberto Hernández, Zhenwei Dai, Anshumali Shrivastava:
Learned Bloom Filters in Adversarial Environments: A Malicious URL Detection Use-Case. HPSR 2021: 1-6 - [c59]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 - [c58]Tharun Medini, Beidi Chen, Anshumali Shrivastava:
SOLAR: Sparse Orthogonal Learned and Random Embeddings. ICLR 2021 - [c57]Shabnam Daghaghi, Tharun Medini, Nicholas Meisburger, Beidi Chen, Mengnan Zhao, Anshumali Shrivastava:
A Tale of Two Efficient and Informative Negative Sampling Distributions. ICML 2021: 2319-2329 - [c56]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: 1283-1289 - [c55]Shabnam Daghaghi, Nicholas Meisburger, Mengnan Zhao, Anshumali Shrivastava:
Accelerating SLIDE Deep Learning on Modern CPUs: Vectorization, Quantizations, Memory Optimizations, and More. MLSys 2021 - [c54]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: 5576-5589 - [c53]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: 8740-8752 - [c52]Joshua Engels, Benjamin Coleman, Anshumali Shrivastava:
Practical Near Neighbor Search via Group Testing. NeurIPS 2021: 9950-9962 - [c51]Zhaozhuo Xu, Beidi Chen, Chaojian Li, Weiyang Liu, Le Song, Yingyan Lin, Anshumali Shrivastava:
Locality Sensitive Teaching. NeurIPS 2021: 18049-18062 - [c50]Li Chou, Zichang Liu, Zhuang Wang, Anshumali Shrivastava:
Efficient and Less Centralized Federated Learning. ECML/PKDD (1) 2021: 772-787 - [c49]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: 352-364 - [c48]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: 2226-2234 - [c47]Shabnam Daghaghi, Tharun Medini, Anshumali Shrivastava:
SDM-Net: A simple and effective model for generalized zero-shot learning. UAI 2021: 2103-2113 - 2020
- [c46]Anastasios Kyrillidis, Anshumali Shrivastava, Moshe Y. Vardi, Zhiwei Zhang:
FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints. AAAI 2020: 1552-1560 - [c45]Beidi Chen, Weiyang Liu, Zhiding Yu, Jan Kautz, Anshumali Shrivastava, Animesh Garg, Animashree Anandkumar:
Angular Visual Hardness. ICML 2020: 1637-1648 - [c44]Benjamin Coleman, Richard G. Baraniuk, Anshumali Shrivastava:
Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data. ICML 2020: 2089-2099 - [c43]Ryan Spring, Anshumali Shrivastava:
Mutual Information Estimation using LSH Sampling. IJCAI 2020: 2807-2815 - [c42]Beidi Chen, Tharun Medini, James Farwell, Sameh Gobriel, Tsung-Yuan Charlie Tai, Anshumali Shrivastava:
SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems. MLSys 2020 - [c41]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 - [c40]Benjamin Coleman, Anshumali Shrivastava:
Sub-linear RACE Sketches for Approximate Kernel Density Estimation on Streaming Data. WWW 2020: 1739-1749 - 2019
- [c39]Chen Luo, Anshumali Shrivastava:
Scaling-Up Split-Merge MCMC with Locality Sensitive Sampling (LSS). AAAI 2019: 4464-4471 - [c38]Ryan Spring, Anastasios Kyrillidis, Vijai Mohan, Anshumali Shrivastava:
Compressing Gradient Optimizers via Count-Sketches. ICML 2019: 5946-5955 - [c37]Constantinos Chamzas, Anshumali Shrivastava, Lydia E. Kavraki:
Using Local Experiences for Global Motion Planning. ICRA 2019: 8606-8612 - [c36]Beidi Chen, Yingchen Xu, Anshumali Shrivastava:
Fast and Accurate Stochastic Gradient Estimation. NeurIPS 2019: 12339-12349 - [c35]Tharun Medini, Qixuan Huang, Yiqiu Wang, Vijai Mohan, Anshumali Shrivastava:
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products. NeurIPS 2019: 13244-13254 - 2018
- [c34]Chen Luo, Anshumali Shrivastava:
Jaccard Affiliation Graph (JAG) Model For Explaining Overlapping Community Behaviors. ASONAM 2018: 1-8 - [c33]Beidi Chen, Yingchen Xu, Anshumali Shrivastava:
Lsh-Sampling breaks the Computational chicken-and-egg Loop in adaptive stochastic Gradient estimation. ICLR (Workshop) 2018 - [c32]Ryan Spring, Anshumali Shrivastava:
Scalable Estimation via LSH Samplers (LSS). ICLR (Workshop) 2018 - [c31]Amirali Aghazadeh, Ryan Spring, Daniel LeJeune, Gautam Dasarathy, Anshumali Shrivastava, Richard G. Baraniuk:
MISSION: Ultra Large-Scale Feature Selection using Count-Sketches. ICML 2018: 80-88 - [c30]Chen Luo, Zhengzhang Chen, Lu-An Tang, Anshumali Shrivastava, Zhichun Li, Haifeng Chen, Jieping Ye:
TINET: Learning Invariant Networks via Knowledge Transfer. KDD 2018: 1890-1899 - [c29]Ankush Mandal, He Jiang, Anshumali Shrivastava, Vivek Sarkar:
Topkapi: Parallel and Fast Sketches for Finding Top-K Frequent Elements. NeurIPS 2018: 10921-10931 - [c28]Rebecca C. Steorts, Anshumali Shrivastava:
Probabilistic Blocking with an Application to the Syrian Conflict. PSD 2018: 314-327 - [c27]Yiqiu Wang, Anshumali Shrivastava, Jonathan Wang, Junghee Ryu:
Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search. SIGMOD Conference 2018: 889-903 - [c26]Beidi Chen, Anshumali Shrivastava:
Densified Winner Take All (WTA) Hashing for Sparse Datasets. UAI 2018: 906-916 - [c25]Chen Luo, Anshumali Shrivastava:
Arrays of (locality-sensitive) Count Estimators (ACE): Anomaly Detection on the Edge. WWW 2018: 1439-1448 - [c24]Anshumali Shrivastava:
Training 100, 000 Classes on a Single Titan X in 7 Hours or 15 Minutes with 25 Titan Xs. WWW (Companion Volume) 2018: 1895 - 2017
- [c23]E. J. Jose Gonzalez, Chen Luo, Anshumali Shrivastava, Krishna V. Palem, Yongshik Moon, Soonhyun Noh, Daedong Park, Seongsoo Hong:
Location detection for navigation using IMUs with a map through coarse-grained machine learning. DATE 2017: 500-505 - [c22]Anshumali Shrivastava:
Optimal Densification for Fast and Accurate Minwise Hashing. ICML 2017: 3154-3163 - [c21]Amirali Aghazadeh, Andrew S. Lan, Anshumali Shrivastava, Richard G. Baraniuk:
RHash: Robust Hashing via L_infinity-norm Distortion. IJCAI 2017: 1386-1394 - [c20]Ryan Spring, Anshumali Shrivastava:
Scalable and Sustainable Deep Learning via Randomized Hashing. KDD 2017: 445-454 - 2016
- [c19]Chen Luo, Anshumali Shrivastava:
SSH (Sketch, Shingle, & Hash) for Indexing Massive-Scale Time Series. NIPS Time Series Workshop 2016: 38-58 - [c18]Anshumali Shrivastava:
Simple and Efficient Weighted Minwise Hashing. NIPS 2016: 1498-1506 - [c17]Anshumali Shrivastava, Arnd Christian König, Mikhail Bilenko:
Time Adaptive Sketches (Ada-Sketches) for Summarizing Data Streams. SIGMOD Conference 2016: 1417-1432 - [c16]Yongshik Moon, Soonhyun Noh, Daedong Park, Chen Luo, Anshumali Shrivastava, Seongsoo Hong, Krishna V. Palem:
CaPSuLe: A camera-based positioning system using learning. SoCC 2016: 235-240 - 2015
- [c15]Anshumali Shrivastava, Ping Li:
Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS). UAI 2015: 812-821 - [c14]Anshumali Shrivastava, Ping Li:
Asymmetric Minwise Hashing for Indexing Binary Inner Products and Set Containment. WWW 2015: 981-991 - 2014
- [c13]Anshumali Shrivastava, Ping Li:
In Defense of Minhash over Simhash. AISTATS 2014: 886-894 - [c12]Anshumali Shrivastava, Ping Li:
A new space for comparing graphs. ASONAM 2014: 62-71 - [c11]Anshumali Shrivastava, Ping Li:
Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search. ICML 2014: 557-565 - [c10]Ping Li, Michael Mitzenmacher, Anshumali Shrivastava:
Coding for Random Projections. ICML 2014: 676-684 - [c9]Anshumali Shrivastava, Ping Li:
Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS). NIPS 2014: 2321-2329 - [c8]Anshumali Shrivastava, Ping Li:
Improved Densification of One Permutation Hashing. UAI 2014: 732-741 - 2013
- [c7]Ping Li, Anshumali Shrivastava, Arnd Christian König:
b-bit minwise hashing in practice. Internetware 2013: 13:1-13:10 - [c6]Anshumali Shrivastava, Ping Li:
Beyond Pairwise: Provably Fast Algorithms for Approximate k-Way Similarity Search. NIPS 2013: 791-799 - 2012
- [c5]Xu Sun, Anshumali Shrivastava, Ping Li:
Fast multi-task learning for query spelling correction. CIKM 2012: 285-294 - [c4]Anshumali Shrivastava, Ping Li:
Fast Near Neighbor Search in High-Dimensional Binary Data. ECML/PKDD (1) 2012: 474-489 - [c3]Ping Li, Anshumali Shrivastava, Arnd Christian König:
GPU-based minwise hashing: GPU-based minwise hashing. WWW (Companion Volume) 2012: 565-566 - [c2]Xu Sun, Anshumali Shrivastava, Ping Li:
Query spelling correction using multi-task learning. WWW (Companion Volume) 2012: 613-614 - 2011
- [c1]Ping Li, Anshumali Shrivastava, Joshua L. Moore, Arnd Christian König:
Hashing Algorithms for Large-Scale Learning. NIPS 2011: 2672-2680
Informal and Other Publications
- 2024
- [i100]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 abs/2402.14035 (2024) - [i99]Tianyi Zhang, Jonah Wonkyu Yi, Bowen Yao, Zhaozhuo Xu, Anshumali Shrivastava:
NoMAD-Attention: Efficient LLM Inference on CPUs Through Multiply-add-free Attention. CoRR abs/2403.01273 (2024) - [i98]Tianyi Zhang, Jonah Yi, Zhaozhuo Xu, Anshumali Shrivastava:
KV Cache is 1 Bit Per Channel: Efficient Large Language Model Inference with Coupled Quantization. CoRR abs/2405.03917 (2024) - [i97]Aditya Desai, Gaurav Gupta, Tianyi Zhang, Anshumali Shrivastava:
IDentity with Locality: An ideal hash for gene sequence search. CoRR abs/2406.14901 (2024) - [i96]Tianyi Zhang, Anshumali Shrivastava:
LeanQuant: Accurate Large Language Model Quantization with Loss-Error-Aware Grid. CoRR abs/2407.10032 (2024) - 2023
- [i95]Anshumali Shrivastava, Zhao Song, Zhaozhuo Xu:
A Theoretical Analysis Of Nearest Neighbor Search On Approximate Near Neighbor Graph. CoRR abs/2303.06210 (2023) - [i94]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 abs/2303.17727 (2023) - [i93]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 abs/2305.11186 (2023) - [i92]Benjamin Coleman, David Torres Ramos, Vihan Lakshman, Chen Luo, Anshumali Shrivastava:
CARAMEL: A Succinct Read-Only Lookup Table via Compressed Static Functions. CoRR abs/2305.16545 (2023) - [i91]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 abs/2305.17118 (2023) - [i90]John Augustine, Dror Fried, Krishna V. Palem, Duc-Hung Pham, Anshumali Shrivastava:
Algorithmic Foundations of Inexact Computing. CoRR abs/2305.18705 (2023) - [i89]Gaurav Gupta, Jonah Yi, Benjamin Coleman, Chen Luo, Vihan Lakshman, Anshumali Shrivastava:
CAPS: A Practical Partition Index for Filtered Similarity Search. CoRR abs/2308.15014 (2023) - [i88]Zhuang Wang, Zhaozhuo Xu, Anshumali Shrivastava, T. S. Eugene Ng:
Zen: Near-Optimal Sparse Tensor Synchronization for Distributed DNN Training. CoRR abs/2309.13254 (2023) - [i87]Aditya Desai, Anshumali Shrivastava:
In defense of parameter sharing for model-compression. CoRR abs/2310.11611 (2023) - [i86]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 abs/2310.17157 (2023) - [i85]Aditya Desai, Benjamin Meisburger, Zichang Liu, Anshumali Shrivastava:
Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random Subspaces. CoRR abs/2311.01722 (2023) - [i84]Shabnam Daghaghi, Benjamin Coleman, Benito Geordie, Anshumali Shrivastava:
Adaptive Sampling for Deep Learning via Efficient Nonparametric Proxies. CoRR abs/2311.13583 (2023) - [i83]Bingcong Li, Shuai Zheng, Parameswaran Raman, Anshumali Shrivastava, Georgios B. Giannakis:
Contractive error feedback for gradient compression. CoRR abs/2312.08538 (2023) - [i82]Tianyi Zhang, Haoteng Yin, Rongzhe Wei, Pan Li, Anshumali Shrivastava:
Learning Scalable Structural Representations for Link Prediction with Bloom Signatures. CoRR abs/2312.16784 (2023) - 2022
- [i81]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 abs/2201.12667 (2022) - [i80]Constantinos Chamzas, Aedan Cullen, Anshumali Shrivastava, Lydia E. Kavraki:
Learning to Retrieve Relevant Experiences for Motion Planning. CoRR abs/2204.08550 (2022) - [i79]Aditya Desai, Keren Zhou, Anshumali Shrivastava:
Efficient model compression with Random Operation Access Specific Tile (ROAST) hashing. CoRR abs/2207.10702 (2022) - [i78]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 abs/2207.10731 (2022) - [i77]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 G. 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 abs/2210.08974 (2022) - [i76]Joshua Engels, Benjamin Coleman, Vihan Lakshman, Anshumali Shrivastava:
DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries. CoRR abs/2210.15748 (2022) - [i75]Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson:
Learning Multimodal Data Augmentation in Feature Space. CoRR abs/2212.14453 (2022) - 2021
- [i74]Aditya Desai, Benjamin Coleman, Anshumali Shrivastava:
Density Sketches for Sampling and Estimation. CoRR abs/2102.12301 (2021) - [i73]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 abs/2102.13631 (2021) - [i72]Aditya Desai, Yanzhou Pan, Kuangyuan Sun, Li Chou, Anshumali Shrivastava:
Semantically Constrained Memory Allocation (SCMA) for Embedding in Efficient Recommendation Systems. CoRR abs/2103.06124 (2021) - [i71]Gaurav Gupta, Tharun Medini, Anshumali Shrivastava, Alexander J. Smola:
IRLI: Iterative Re-partitioning for Learning to Index. CoRR abs/2103.09944 (2021) - [i70]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 abs/2103.10891 (2021) - [i69]Benjamin Coleman, Santiago Segarra, Anshumali Shrivastava, Alex Smola:
Graph Reordering for Cache-Efficient Near Neighbor Search. CoRR abs/2104.03221 (2021) - [i68]Anshumali Shrivastava, Zhao Song, Zhaozhuo Xu:
Sublinear Least-Squares Value Iteration via Locality Sensitive Hashing. CoRR abs/2105.08285 (2021) - [i67]Li Chou, Zichang Liu, Zhuang Wang, Anshumali Shrivastava:
Efficient and Less Centralized Federated Learning. CoRR abs/2106.06627 (2021) - [i66]Zhaozhuo Xu, Minghao Yan, Junyan Zhang, Anshumali Shrivastava:
PairConnect: A Compute-Efficient MLP Alternative to Attention. CoRR abs/2106.08235 (2021) - [i65]Zichang Liu, Benjamin Coleman, Anshumali Shrivastava:
Efficient Inference via Universal LSH Kernel. CoRR abs/2106.11426 (2021) - [i64]Joshua Engels, Benjamin Coleman, Anshumali Shrivastava:
Practical Near Neighbor Search via Group Testing. CoRR abs/2106.11565 (2021) - [i63]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 abs/2108.02191 (2021) - [i62]Zhenwei Dai, Chen Dun, Yuxin Tang, Anastasios Kyrillidis, Anshumali Shrivastava:
Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks. CoRR abs/2110.12292 (2021) - [i61]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 abs/2111.06562 (2021) - [i60]Anshumali Shrivastava, Zhao Song, Zhaozhuo Xu:
Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structures. CoRR abs/2111.15139 (2021) - 2020
- [i59]Sicong Liu, Junzhao Du, Anshumali Shrivastava, Lin Zhong:
Privacy Adversarial Network: Representation Learning for Mobile Data Privacy. CoRR abs/2006.06535 (2020) - [i58]Benjamin Coleman, Anshumali Shrivastava:
A One-Pass Private Sketch for Most Machine Learning Tasks. CoRR abs/2006.09352 (2020) - [i57]Benjamin Coleman, Gaurav Gupta, John Chen, Anshumali Shrivastava:
STORM: Foundations of End-to-End Empirical Risk Minimization on the Edge. CoRR abs/2006.14554 (2020) - [i56]Zichang Liu, Zhaozhuo Xu, Alan Baonan Ji, Jonathan Li, Beidi Chen, Anshumali Shrivastava:
Climbing the WOL: Training for Cheaper Inference. CoRR abs/2007.01230 (2020) - [i55]Louis Abraham, Gary Bécigneul, Benjamin Coleman, Bernhard Schölkopf, Anshumali Shrivastava, Alexander J. Smola:
Bloom Origami Assays: Practical Group Testing. CoRR abs/2008.02641 (2020) - [i54]Nicholas Meisburger, Anshumali Shrivastava:
Distributed Tera-Scale Similarity Search with MPI: Provably Efficient Similarity Search over billions without a Single Distance Computation. CoRR abs/2008.03260 (2020) - [i53]Tharun Medini, Beidi Chen, Anshumali Shrivastava:
SOLAR: Sparse Orthogonal Learned and Random Embeddings. CoRR abs/2008.13225 (2020) - [i52]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 abs/2010.15335 (2020) - [i51]Zhenwei Dai, Aditya Desai, Reinhard Heckel, Anshumali Shrivastava:
Active Sampling Count Sketch (ASCS) for Online Sparse Estimation of a Trillion Scale Covariance Matrix. CoRR abs/2010.15951 (2020) - [i50]Zichang Liu, Li Chou, Anshumali Shrivastava:
Neighbor Oblivious Learning (NObLe) for Device Localization and Tracking. CoRR abs/2011.14954 (2020) - [i49]Shabnam Daghaghi, Tharun Medini, Beidi Chen, Mengnan Zhao, Anshumali Shrivastava:
A Constant-time Adaptive Negative Sampling. CoRR abs/2012.15843 (2020) - 2019
- [i48]Shabnam Daghaghi, Anshumali Shrivastava, Tharun Medini:
Cross-Modal Mapping for Generalized Zero-Shot Learning by Soft-Labeling. ViGIL@NeurIPS 2019 - [i47]Sicong Liu, Anshumali Shrivastava, Junzhao Du, Lin Zhong:
Better accuracy with quantified privacy: representations learned via reconstructive adversarial network. CoRR abs/1901.08730 (2019) - [i46]Ryan Spring, Anastasios Kyrillidis, Vijai Mohan, Anshumali Shrivastava:
Compressing Gradient Optimizers via Count-Sketches. CoRR abs/1902.00179 (2019) - [i45]Benjamin Coleman, Anshumali Shrivastava, Richard G. Baraniuk:
RACE: Sub-Linear Memory Sketches for Approximate Near-Neighbor Search on Streaming Data. CoRR abs/1902.06687 (2019) - [i44]Beidi Chen, Tharun Medini, Anshumali Shrivastava:
SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems. CoRR abs/1903.03129 (2019) - [i43]Constantinos Chamzas, Anshumali Shrivastava, Lydia E. Kavraki:
Using Local Experiences for Global Motion Planning. CoRR abs/1903.08693 (2019) - [i42]John Chen, Benjamin Coleman, Anshumali Shrivastava:
Revisiting Consistent Hashing with Bounded Loads. CoRR abs/1908.08762 (2019) - [i41]Shabnam Daghaghi, Tharun Medini, Anshumali Shrivastava:
Semantic Similarity Based Softmax Classifier for Zero-Shot Learning. CoRR abs/1909.04790 (2019) - [i40]Gaurav Gupta, Benjamin Coleman, Tharun Medini, Vijai Mohan, Anshumali Shrivastava:
RAMBO: Repeated And Merged Bloom Filter for Multiple Set Membership Testing (MSMT) in Sub-linear time. CoRR abs/1910.02611 (2019) - [i39]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). CoRR abs/1910.04358 (2019) - [i38]Zhenwei Dai, Anshumali Shrivastava:
Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier. CoRR abs/1910.09131 (2019) - [i37]Tharun Medini, Qixuan Huang, Yiqiu Wang, Vijai Mohan, Anshumali Shrivastava:
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products. CoRR abs/1910.13830 (2019) - [i36]Beidi Chen, Yingchen Xu, Anshumali Shrivastava:
Lsh-sampling Breaks the Computation Chicken-and-egg Loop in Adaptive Stochastic Gradient Estimation. CoRR abs/1910.14162 (2019) - [i35]Anastasios Kyrillidis, Anshumali Shrivastava, Moshe Y. Vardi, Zhiwei Zhang:
FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints. CoRR abs/1912.01032 (2019) - [i34]Beidi Chen, Weiyang Liu, Animesh Garg, Zhiding Yu, Anshumali Shrivastava, Jan Kautz, Anima Anandkumar:
Angular Visual Hardness. CoRR abs/1912.02279 (2019) - [i33]Benjamin Coleman, Anshumali Shrivastava:
Sub-linear RACE Sketches for Approximate Kernel Density Estimation on Streaming Data. CoRR abs/1912.02283 (2019) - [i32]M. Sadegh Riazi, Beidi Chen, Anshumali Shrivastava, Dan S. Wallach, Farinaz Koushanfar:
Sub-Linear Privacy-Preserving Near-Neighbor Search. IACR Cryptol. ePrint Arch. 2019: 1222 (2019) - 2018
- [i31]Chen Luo, Anshumali Shrivastava:
Scaling-up Split-Merge MCMC with Locality Sensitive Sampling (LSS). CoRR abs/1802.07444 (2018) - [i30]Amirali Aghazadeh, Ryan Spring, Daniel LeJeune, Gautam Dasarathy, Anshumali Shrivastava, Richard G. Baraniuk:
MISSION: Ultra Large-Scale Feature Selection using Count-Sketches. CoRR abs/1806.04310 (2018) - [i29]Chen Luo, Anshumali Shrivastava:
Want to bring a community together? Create more sub-communities. CoRR abs/1807.04911 (2018) - [i28]Qixuan Huang, Yiqiu Wang, Tharun Medini, Anshumali Shrivastava:
Extreme Classification in Log Memory. CoRR abs/1810.04254 (2018) - [i27]Rebecca C. Steorts, Anshumali Shrivastava:
Probabilistic Blocking with An Application to the Syrian Conflict. CoRR abs/1810.05497 (2018) - 2017
- [i26]Anshumali Shrivastava:
Optimal Densification for Fast and Accurate Minwise Hashing. CoRR abs/1703.04664 (2017) - [i25]Ryan Spring, Anshumali Shrivastava:
A New Unbiased and Efficient Class of LSH-Based Samplers and Estimators for Partition Function Computation in Log-Linear Models. CoRR abs/1703.05160 (2017) - [i24]Chen Luo, Anshumali Shrivastava:
Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups. CoRR abs/1706.06664 (2017) - [i23]Chen Luo, Zhengzhang Chen, Lu-An Tang, Anshumali Shrivastava, Zhichun Li:
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer. CoRR abs/1708.07867 (2017) - [i22]Yiqiu Wang, Anshumali Shrivastava, Junghee Ryu:
FLASH: Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search. CoRR abs/1709.01190 (2017) - [i21]Beidi Chen, Anshumali Shrivastava, Rebecca C. Steorts:
Unique Entity Estimation with Application to the Syrian Conflict. CoRR abs/1710.02690 (2017) - 2016
- [i20]Ping Li, Michael Mitzenmacher, Anshumali Shrivastava:
2-Bit Random Projections, NonLinear Estimators, and Approximate Near Neighbor Search. CoRR abs/1602.06577 (2016) - [i19]Ryan Spring, Anshumali Shrivastava:
Scalable and Sustainable Deep Learning via Randomized Hashing. CoRR abs/1602.08194 (2016) - [i18]Anshumali Shrivastava:
Exact Weighted Minwise Hashing in Constant Time. CoRR abs/1602.08393 (2016) - [i17]Amirali Aghazadeh, Andrew S. Lan, Anshumali Shrivastava, Richard G. Baraniuk:
Near-Isometric Binary Hashing for Large-scale Datasets. CoRR abs/1603.03836 (2016) - [i16]Chen Luo, Anshumali Shrivastava:
SSH (Sketch, Shingle, & Hash) for Indexing Massive-Scale Time Series. CoRR abs/1610.07328 (2016) - [i15]Beidi Chen, Anshumali Shrivastava:
Revisiting Winner Take All (WTA) Hashing for Sparse Datasets. CoRR abs/1612.01834 (2016) - [i14]M. Sadegh Riazi, Beidi Chen, Anshumali Shrivastava, Dan S. Wallach, Farinaz Koushanfar:
Sub-linear Privacy-preserving Search with Untrusted Server and Semi-honest Parties. CoRR abs/1612.01835 (2016) - 2015
- [i13]Peter Sadosky, Anshumali Shrivastava, Megan Price, Rebecca C. Steorts:
Blocking Methods Applied to Casualty Records from the Syrian Conflict. CoRR abs/1510.07714 (2015) - 2014
- [i12]Ping Li, Michael Mitzenmacher, Anshumali Shrivastava:
Coding for Random Projections and Approximate Near Neighbor Search. CoRR abs/1403.8144 (2014) - [i11]Anshumali Shrivastava, Ping Li:
A New Space for Comparing Graphs. CoRR abs/1404.4644 (2014) - [i10]Anshumali Shrivastava, Ping Li:
Graph Kernels via Functional Embedding. CoRR abs/1404.5214 (2014) - [i9]Anshumali Shrivastava, Ping Li:
Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS). CoRR abs/1405.5869 (2014) - [i8]Anshumali Shrivastava, Ping Li:
Improved Densification of One Permutation Hashing. CoRR abs/1406.4784 (2014) - [i7]Anshumali Shrivastava, Ping Li:
In Defense of MinHash Over SimHash. CoRR abs/1407.4416 (2014) - [i6]Anshumali Shrivastava, Ping Li:
An Improved Scheme for Asymmetric LSH. CoRR abs/1410.5410 (2014) - [i5]Anshumali Shrivastava, Ping Li:
Asymmetric Minwise Hashing. CoRR abs/1411.3787 (2014) - 2013
- [i4]Ping Li, Michael Mitzenmacher, Anshumali Shrivastava:
Coding for Random Projections. CoRR abs/1308.2218 (2013) - 2012
- [i3]Ping Li, Anshumali Shrivastava, Arnd Christian König:
b-Bit Minwise Hashing in Practice: Large-Scale Batch and Online Learning and Using GPUs for Fast Preprocessing with Simple Hash Functions. CoRR abs/1205.2958 (2012) - 2011
- [i2]Ping Li, Anshumali Shrivastava, Joshua L. Moore, Arnd Christian König:
Hashing Algorithms for Large-Scale Learning. CoRR abs/1106.0967 (2011) - [i1]Ping Li, Anshumali Shrivastava, Arnd Christian König:
Training Logistic Regression and SVM on 200GB Data Using b-Bit Minwise Hashing and Comparisons with Vowpal Wabbit (VW). CoRR abs/1108.3072 (2011)
Coauthor Index
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