Hashing Algorithms for Large-Scale Learning. - Ping Li 001; Anshumali Shrivastava; Joshua L. Moore; Arnd Christian König (NIPS, 2011)

Training Logistic Regression and SVM on 200GB Data Using b-Bit Minwise Hashing and Comparisons with Vowpal Wabbit (VW) - Ping Li 001; Anshumali Shrivastava; Arnd Christian König (CoRR, 2011)

Fast multi-task learning for query spelling correction. - Xu Sun 001; Anshumali Shrivastava; Ping Li 001 (CIKM, 2012)

Fast Near Neighbor Search in High-Dimensional Binary Data. - Anshumali Shrivastava; Ping Li 001 (ECML/PKDD (1), 2012)

GPU-based minwise hashing: GPU-based minwise hashing. - Ping Li 001; Anshumali Shrivastava; Arnd Christian König (WWW (Companion Volume), 2012)

Query spelling correction using multi-task learning. - Xu Sun 001; Anshumali Shrivastava; Ping Li 001 (WWW (Companion Volume), 2012)

b-Bit Minwise Hashing in Practice: Large-Scale Batch and Online Learning and Using GPUs for Fast Preprocessing with Simple Hash Functions. - Ping Li 001; Anshumali Shrivastava; Arnd Christian König (CoRR, 2012)

b-bit minwise hashing in practice. - Ping Li 001; Anshumali Shrivastava; Arnd Christian König (Internetware, 2013)

Beyond Pairwise: Provably Fast Algorithms for Approximate k-Way Similarity Search. - Anshumali Shrivastava; Ping Li 001 (NIPS, 2013)

In Defense of Minhash over Simhash. - Anshumali Shrivastava; Ping Li 001 (AISTATS, 2014)

A new space for comparing graphs. - Anshumali Shrivastava; Ping Li 001 (ASONAM, 2014)

Coding for Random Projections. - Ping Li 001; Michael Mitzenmacher; Anshumali Shrivastava (ICML, 2014)

Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search. - Anshumali Shrivastava; Ping Li 001 (ICML, 2014)

Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS). - Anshumali Shrivastava; Ping Li 001 (NIPS, 2014)

Improved Densification of One Permutation Hashing. - Anshumali Shrivastava; Ping Li 001 (UAI, 2014)

Coding for Random Projections and Approximate Near Neighbor Search. - Ping Li 001; Michael Mitzenmacher; Anshumali Shrivastava (CoRR, 2014)

Graph Kernels via Functional Embedding. - Anshumali Shrivastava; Ping Li 001 (CoRR, 2014)

An Improved Scheme for Asymmetric LSH. - Anshumali Shrivastava; Ping Li 001 (CoRR, 2014)

Asymmetric Minwise Hashing. - Anshumali Shrivastava; Ping Li 001 (CoRR, 2014)

Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS). - Anshumali Shrivastava; Ping Li 001 (UAI, 2015)

Asymmetric Minwise Hashing for Indexing Binary Inner Products and Set Containment. - Anshumali Shrivastava; Ping Li 001 (WWW, 2015)

Blocking Methods Applied to Casualty Records from the Syrian Conflict. - Peter Sadosky; Anshumali Shrivastava; Megan Price; Rebecca C. Steorts (CoRR, 2015)

Probabilistic Hashing Techniques for Big Data. - Anshumali Shrivastava (2015)

SSH (Sketch, Shingle, & Hash) for Indexing Massive-Scale Time Series. - Chen Luo 001; Anshumali Shrivastava (NIPS Time Series Workshop, 2016)

Simple and Efficient Weighted Minwise Hashing. - Anshumali Shrivastava (NIPS, 2016)

Time Adaptive Sketches (Ada-Sketches) for Summarizing Data Streams. - Anshumali Shrivastava; Arnd Christian König; Mikhail Bilenko (SIGMOD Conference, 2016)

CaPSuLe: A camera-based positioning system using learning. - Yongshik Moon; Soonhyun Noh; Daedong Park; Chen Luo 001; Anshumali Shrivastava; Seongsoo Hong; Krishna V. Palem (SoCC, 2016)

2-Bit Random Projections, NonLinear Estimators, and Approximate Near Neighbor Search. - Ping Li 001; Michael Mitzenmacher; Anshumali Shrivastava (CoRR, 2016)

Near-Isometric Binary Hashing for Large-scale Datasets. - Amirali Aghazadeh; Andrew S. Lan; Anshumali Shrivastava; Richard G. Baraniuk (CoRR, 2016)

Revisiting Winner Take All (WTA) Hashing for Sparse Datasets. - Beidi Chen; Anshumali Shrivastava (CoRR, 2016)

Sub-linear Privacy-preserving Search with Untrusted Server and Semi-honest Parties. - M. Sadegh Riazi; Beidi Chen; Anshumali Shrivastava; Dan S. Wallach; Farinaz Koushanfar (CoRR, 2016)

Exact Weighted Minwise Hashing in Constant Time. - Anshumali Shrivastava (CoRR, 2016)

Location detection for navigation using IMUs with a map through coarse-grained machine learning. - E. J. Jose Gonzalez; Chen Luo 001; Anshumali Shrivastava; Krishna V. Palem; Yongshik Moon; Soonhyun Noh; Daedong Park; Seongsoo Hong (DATE, 2017)

Optimal Densification for Fast and Accurate Minwise Hashing. - Anshumali Shrivastava (ICML, 2017)

RHash: Robust Hashing via L_infinity-norm Distortion. - Amirali Aghazadeh; Andrew S. Lan; Anshumali Shrivastava; Richard G. Baraniuk (IJCAI, 2017)

Scalable and Sustainable Deep Learning via Randomized Hashing. - Ryan Spring; Anshumali Shrivastava (KDD, 2017)

Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups. - Chen Luo 001; Anshumali Shrivastava (CoRR, 2017)

A New Unbiased and Efficient Class of LSH-Based Samplers and Estimators for Partition Function Computation in Log-Linear Models. - Ryan Spring; Anshumali Shrivastava (CoRR, 2017)

Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer. - Chen Luo 001; Zhengzhang Chen; Lu-An Tang; Anshumali Shrivastava; Zhichun Li (CoRR, 2017)

FLASH: Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search. - Yiqiu Wang; Anshumali Shrivastava; Junghee Ryu (CoRR, 2017)

Unique Entity Estimation with Application to the Syrian Conflict. - Beidi Chen; Anshumali Shrivastava; Rebecca C. Steorts (CoRR, 2017)

Jaccard Affiliation Graph (JAG) Model For Explaining Overlapping Community Behaviors. - Chen Luo 001; Anshumali Shrivastava (ASONAM, 2018)

Lsh-Sampling breaks the Computational chicken-and-egg Loop in adaptive stochastic Gradient estimation. - Beidi Chen; Yingchen Xu; Anshumali Shrivastava (ICLR (Workshop), 2018)

Scalable Estimation via LSH Samplers (LSS). - Ryan Spring; Anshumali Shrivastava (ICLR (Workshop), 2018)

MISSION: Ultra Large-Scale Feature Selection using Count-Sketches. - Amirali Aghazadeh; Ryan Spring; Daniel LeJeune; Gautam Dasarathy; Anshumali Shrivastava; Richard G. Baraniuk (ICML, 2018)

TINET: Learning Invariant Networks via Knowledge Transfer. - Chen Luo 001; Zhengzhang Chen; Lu-An Tang; Anshumali Shrivastava; Zhichun Li; Haifeng Chen; Jieping Ye (KDD, 2018)

Topkapi: Parallel and Fast Sketches for Finding Top-K Frequent Elements. - Ankush Mandal; He Jiang; Anshumali Shrivastava; Vivek Sarkar (NeurIPS, 2018)

Probabilistic Blocking with an Application to the Syrian Conflict. - Rebecca C. Steorts; Anshumali Shrivastava (PSD, 2018)

Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search. - Yiqiu Wang; Anshumali Shrivastava; Jonathan Wang; Junghee Ryu (SIGMOD Conference, 2018)

Densified Winner Take All (WTA) Hashing for Sparse Datasets. - Beidi Chen; Anshumali Shrivastava (UAI, 2018)

Arrays of (locality-sensitive) Count Estimators (ACE): Anomaly Detection on the Edge. - Chen Luo 001; Anshumali Shrivastava (WWW, 2018)

Training 100, 000 Classes on a Single Titan X in 7 Hours or 15 Minutes with 25 Titan Xs. - Anshumali Shrivastava (WWW (Companion Volume), 2018)

Want to bring a community together? Create more sub-communities. - Chen Luo 001; Anshumali Shrivastava (CoRR, 2018)

Extreme Classification in Log Memory. - Qixuan Huang; Yiqiu Wang; Tharun Medini; Anshumali Shrivastava (CoRR, 2018)

Scaling-Up Split-Merge MCMC with Locality Sensitive Sampling (LSS). - Chen Luo 001; Anshumali Shrivastava (AAAI, 2019)

Compressing Gradient Optimizers via Count-Sketches. - Ryan Spring; Anastasios Kyrillidis; Vijai Mohan; Anshumali Shrivastava (ICML, 2019)

Using Local Experiences for Global Motion Planning. - Constantinos Chamzas; Anshumali Shrivastava; Lydia E. Kavraki (ICRA, 2019)

Fast and Accurate Stochastic Gradient Estimation. - Beidi Chen; Yingchen Xu; Anshumali Shrivastava (NeurIPS, 2019)

Cross-Modal Mapping for Generalized Zero-Shot Learning by Soft-Labeling. - Shabnam Daghaghi; Anshumali Shrivastava; Tharun Medini (ViGIL@NeurIPS, 2019)

Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products. - Tharun Medini; Qixuan Huang; Yiqiu Wang; Vijai Mohan; Anshumali Shrivastava (NeurIPS, 2019)

Better accuracy with quantified privacy: representations learned via reconstructive adversarial network. - Sicong Liu; Anshumali Shrivastava; Junzhao Du; Lin Zhong (CoRR, 2019)

RACE: Sub-Linear Memory Sketches for Approximate Near-Neighbor Search on Streaming Data. - Benjamin Coleman; Anshumali Shrivastava; Richard G. Baraniuk (CoRR, 2019)

SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems. - Beidi Chen; Tharun Medini; Anshumali Shrivastava (CoRR, 2019)

Semantic Similarity Based Softmax Classifier for Zero-Shot Learning. - Shabnam Daghaghi; Tharun Medini; Anshumali Shrivastava (CoRR, 2019)

RAMBO: Repeated And Merged Bloom Filter for Multiple Set Membership Testing (MSMT) in Sub-linear time. - Gaurav Gupta; Benjamin Coleman; Tharun Medini; Vijai Mohan; Anshumali Shrivastava (CoRR, 2019)

Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier. - Zhenwei Dai; Anshumali Shrivastava (CoRR, 2019)

Lsh-sampling Breaks the Computation Chicken-and-egg Loop in Adaptive Stochastic Gradient Estimation. - Beidi Chen; Yingchen Xu; Anshumali Shrivastava (CoRR, 2019)

Sub-Linear Privacy-Preserving Near-Neighbor Search. - M. Sadegh Riazi; Beidi Chen; Anshumali Shrivastava; Dan S. Wallach; Farinaz Koushanfar (IACR Cryptol. ePrint Arch., 2019)

Privacy Adversarial Network: Representation Learning for Mobile Data Privacy. - Sicong Liu; Junzhao Du; Anshumali Shrivastava; Lin Zhong (Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2019)

Learning Feasibility for Task and Motion Planning in Tabletop Environments. - Andrew M. Wells; Neil T. Dantam; Anshumali Shrivastava; Lydia E. Kavraki (IEEE Robotics Autom. Lett., 2019)

FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints. - Anastasios Kyrillidis; Anshumali Shrivastava; Moshe Y. Vardi; Zhiwei Zhang 001 (AAAI, 2020)

Angular Visual Hardness. - Beidi Chen; Weiyang Liu; Zhiding Yu; Jan Kautz; Anshumali Shrivastava; Animesh Garg; Animashree Anandkumar (ICML, 2020)

Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data. - Benjamin Coleman; Richard G. Baraniuk; Anshumali Shrivastava (ICML, 2020)

Mutual Information Estimation using LSH Sampling. - Ryan Spring; Anshumali Shrivastava (IJCAI, 2020)

SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems. - Beidi Chen; Tharun Medini; James Farwell; Sameh Gobriel; Tsung-Yuan Charlie Tai; Anshumali Shrivastava (MLSys, 2020)

Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier with Application to Real-Time Information Filtering on the Web. - Zhenwei Dai; Anshumali Shrivastava (NeurIPS, 2020)

Sub-linear RACE Sketches for Approximate Kernel Density Estimation on Streaming Data. - Benjamin Coleman; Anshumali Shrivastava (WWW, 2020)

A One-Pass Private Sketch for Most Machine Learning Tasks. - Benjamin Coleman; Anshumali Shrivastava (CoRR, 2020)

STORM: Foundations of End-to-End Empirical Risk Minimization on the Edge. - Benjamin Coleman; Gaurav Gupta; John Chen 002; Anshumali Shrivastava (CoRR, 2020)

Climbing the WOL: Training for Cheaper Inference. - Zichang Liu; Zhaozhuo Xu; Alan Baonan Ji; Jonathan Li 002; Beidi Chen; Anshumali Shrivastava (CoRR, 2020)

Bloom Origami Assays: Practical Group Testing. - Louis Abraham; Gary Bécigneul; Benjamin Coleman; Bernhard Schölkopf; Anshumali Shrivastava; Alexander J. Smola (CoRR, 2020)

Distributed Tera-Scale Similarity Search with MPI: Provably Efficient Similarity Search over billions without a Single Distance Computation. - Nicholas Meisburger; Anshumali Shrivastava (CoRR, 2020)

A Constant-time Adaptive Negative Sampling. - Shabnam Daghaghi; Tharun Medini; Beidi Chen; Mengnan Zhao; Anshumali Shrivastava (CoRR, 2020)

Revisiting Consistent Hashing with Bounded Loads. - John Chen 002; Benjamin Coleman; Anshumali Shrivastava (AAAI, 2021)

A One-Pass Distributed and Private Sketch for Kernel Sums with Applications to Machine Learning at Scale. - Benjamin Coleman; Anshumali Shrivastava (CCS, 2021)

Neighbor Oblivious Learning (NObLe) for Device Localization and Tracking. - Zichang Liu; Li Chou; Anshumali Shrivastava (DATE, 2021)

Learned Bloom Filters in Adversarial Environments: A Malicious URL Detection Use-Case. - Pedro Reviriego; José Alberto Hernández 001; Zhenwei Dai; Anshumali Shrivastava (HPSR, 2021)

MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training. - Beidi Chen; Zichang Liu; Binghui Peng; Zhaozhuo Xu; Jonathan Lingjie Li; Tri Dao; Zhao Song 002; Anshumali Shrivastava; Christopher Ré (ICLR, 2021)

SOLAR: Sparse Orthogonal Learned and Random Embeddings. - Tharun Medini; Beidi Chen; Anshumali Shrivastava (ICLR, 2021)

A Tale of Two Efficient and Informative Negative Sampling Distributions. - Shabnam Daghaghi; Tharun Medini; Nicholas Meisburger; Beidi Chen; Mengnan Zhao; Anshumali Shrivastava (ICML, 2021)

Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions. - Constantinos Chamzas; Zachary Kingston; Carlos Quintero-Peña; Anshumali Shrivastava; Lydia E. Kavraki (ICRA, 2021)

Accelerating SLIDE Deep Learning on Modern CPUs: Vectorization, Quantizations, Memory Optimizations, and More. - Shabnam Daghaghi; Nicholas Meisburger; Mengnan Zhao; Anshumali Shrivastava (MLSys, 2021)

Raw Nav-merge Seismic Data to Subsurface Properties with MLP based Multi-Modal Information Unscrambler. - Aditya Desai; Zhaozhuo Xu; Menal Gupta; Anu Chandran; Antoine Vial-Aussavy; Anshumali Shrivastava (NeurIPS, 2021)

Practical Near Neighbor Search via Group Testing. - Joshua Engels; Benjamin Coleman; Anshumali Shrivastava (NeurIPS, 2021)

Locality Sensitive Teaching. - Zhaozhuo Xu; Beidi Chen; Chaojian Li; Weiyang Liu; Le Song; Yingyan Lin; Anshumali Shrivastava (NeurIPS, 2021)

Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structures. - Zhaozhuo Xu; Zhao Song 002; Anshumali Shrivastava (NeurIPS, 2021)

Efficient and Less Centralized Federated Learning. - Li Chou; Zichang Liu; Zhuang Wang; Anshumali Shrivastava (ECML/PKDD (1), 2021)

Active Sampling Count Sketch (ASCS) for Online Sparse Estimation of a Trillion Scale Covariance Matrix. - Zhenwei Dai; Aditya Desai; Reinhard Heckel; Anshumali Shrivastava (SIGMOD Conference, 2021)

Fast Processing and Querying of 170TB of Genomics Data via a Repeated And Merged BloOm Filter (RAMBO). - Gaurav Gupta; Minghao Yan; Benjamin Coleman; Bryce Kille; Ryan A. Leo Elworth; Tharun Medini; Todd J. Treangen; Anshumali Shrivastava (SIGMOD Conference, 2021)

SDM-Net: A simple and effective model for generalized zero-shot learning. - Shabnam Daghaghi; Tharun Medini; Anshumali Shrivastava (UAI, 2021)

Solving hybrid Boolean constraints in continuous space via multilinear Fourier expansions. - Anastasios Kyrillidis; Anshumali Shrivastava; Moshe Y. Vardi; Zhiwei Zhang 001 (Artif. Intell., 2021)

Density Sketches for Sampling and Estimation. - Aditya Desai; Benjamin Coleman; Anshumali Shrivastava (CoRR, 2021)

Beyond Convolutions: A Novel Deep Learning Approach for Raw Seismic Data Ingestion. - Zhaozhuo Xu; Aditya Desai; Menal Gupta; Anu Chandran; Antoine Vial-Aussavy; Anshumali Shrivastava (CoRR, 2021)

Semantically Constrained Memory Allocation (SCMA) for Embedding in Efficient Recommendation Systems. - Aditya Desai; Yanzhou Pan; Kuangyuan Sun; Li Chou; Anshumali Shrivastava (CoRR, 2021)

IRLI: Iterative Re-partitioning for Learning to Index. - Gaurav Gupta; Tharun Medini; Anshumali Shrivastava; Alexander J. Smola (CoRR, 2021)

Accelerating SLIDE Deep Learning on Modern CPUs: Vectorization, Quantizations, Memory Optimizations, and More. - Shabnam Daghaghi; Nicholas Meisburger; Mengnan Zhao; Yong Wu; Sameh Gobriel; Charlie Tai; Anshumali Shrivastava (CoRR, 2021)

Sublinear Least-Squares Value Iteration via Locality Sensitive Hashing. - Anshumali Shrivastava; Zhao Song 002; Zhaozhuo Xu (CoRR, 2021)

PairConnect: A Compute-Efficient MLP Alternative to Attention. - Zhaozhuo Xu; Minghao Yan; Junyan Zhang; Anshumali Shrivastava (CoRR, 2021)

Efficient Inference via Universal LSH Kernel. - Zichang Liu; Benjamin Coleman; Anshumali Shrivastava (CoRR, 2021)

Random Offset Block Embedding Array (ROBE) for CriteoTB Benchmark MLPerf DLRM Model : 1000× Compression and 2.7× Faster Inference. - Aditya Desai; Li Chou; Anshumali Shrivastava (CoRR, 2021)

Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks. - Zhenwei Dai; Chen Dun; Yuxin Tang; Anastasios Kyrillidis; Anshumali Shrivastava (CoRR, 2021)

Satellite Images and Deep Learning to Identify Discrepancy in Mailing Addresses with Applications to Census 2020 in Houston. - Zhaozhuo Xu; Alan Baonan Ji; Andrew Woods; Beidi Chen; Anshumali Shrivastava (CoRR, 2021)

Structural Contrastive Representation Learning for Zero-shot Multi-label Text Classification. - Tianyi Zhang 011; Zhaozhuo Xu; Tharun Medini; Anshumali Shrivastava (EMNLP (Findings), 2022)

One-Pass Diversified Sampling with Application to Terabyte-Scale Genomic Sequence Streams. - Benjamin Coleman; Benito Geordie; Li Chou; Ryan A. Leo Elworth; Todd J. Treangen; Anshumali Shrivastava (ICML, 2022)

DRAGONN: Distributed Randomized Approximate Gradients of Neural Networks. - Zhuang Wang; Zhaozhuo Xu; Xinyu Crystal Wu; Anshumali Shrivastava; T. S. Eugene Ng (ICML, 2022)

Learning to Retrieve Relevant Experiences for Motion Planning. - Constantinos Chamzas; Aedan Cullen; Anshumali Shrivastava; Lydia E. Kavraki (ICRA, 2022)

BLISS: A Billion scale Index using Iterative Re-partitioning. - Gaurav Gupta; Tharun Medini; Anshumali Shrivastava; Alexander J. Smola (KDD, 2022)

Random Offset Block Embedding (ROBE) for compressed embedding tables in deep learning recommendation systems. - Aditya Desai; Li Chou; Anshumali Shrivastava (MLSys, 2022)

HALOS: Hashing Large Output Space for Cheap Inference. - Zichang Liu; Zhaozhuo Xu; Alan Baonan Ji; Junyan Zhang; Jonathan Li 002; Beidi Chen; Anshumali Shrivastava (MLSys, 2022)

Graph Reordering for Cache-Efficient Near Neighbor Search. - Benjamin Coleman; Santiago Segarra; Alexander J. Smola; Anshumali Shrivastava (NeurIPS, 2022)

The trade-offs of model size in large recommendation models : 100GB to 10MB Criteo-tb DLRM model. - Aditya Desai; Anshumali Shrivastava (NeurIPS, 2022)

Retaining Knowledge for Learning with Dynamic Definition. - Zichang Liu; Benjamin Coleman; Tianyi Zhang 011; Anshumali Shrivastava (NeurIPS, 2022)

ROSE: Robust Caches for Amazon Product Search. - Chen Luo 003; Vihan Lakshman; Anshumali Shrivastava; Tianyu Cao 001; Sreyashi Nag; Rahul Goutam; Hanqing Lu; Yiwei Song; Bing Yin (WWW (Companion Volume), 2022)

Distributed SLIDE: Enabling Training Large Neural Networks on Low Bandwidth and Simple CPU-Clusters via Model Parallelism and Sparsity. - Minghao Yan; Nicholas Meisburger; Tharun Medini; Anshumali Shrivastava (CoRR, 2022)

Efficient model compression with Random Operation Access Specific Tile (ROAST) hashing. - Aditya Desai; Keren Zhou 001; Anshumali Shrivastava (CoRR, 2022)

The trade-offs of model size in large recommendation models : A 10000 × compressed criteo-tb DLRM model (100 GB parameters to mere 10MB). - Aditya Desai; Anshumali Shrivastava (CoRR, 2022)

Coordinated Science Laboratory 70th Anniversary Symposium: The Future of Computing. - 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; et al. (CoRR, 2022)

Optimizing Learned Bloom Filters: How Much Should Be Learned? - Zhenwei Dai; Anshumali Shrivastava; Pedro Reviriego; José Alberto Hernández 001 (IEEE Embed. Syst. Lett., 2022)

A Tale of Two Efficient Value Iteration Algorithms for Solving Linear MDPs with Large Action Space. - Zhaozhuo Xu; Zhao Song 002; Anshumali Shrivastava (AISTATS, 2023)

Near Neighbor Search for Constraint Queries. - Gaurav Gupta; Anup B. Rao; Tung Mai; Ryan A. Rossi; Xiang Chen; Saayan Mitra; Anshumali Shrivastava (IEEE Big Data, 2023)

BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Search and Recommendation Models on Commodity CPU Hardware. - 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; et al. (CIKM, 2023)

Learning Multimodal Data Augmentation in Feature Space. - Zichang Liu; Zhiqiang Tang 001; Xingjian Shi; Aston Zhang; Mu Li 003; Anshumali Shrivastava; Andrew Gordon Wilson (ICLR, 2023)

Hardware-Aware Compression with Random Operation Access Specific Tile (ROAST) Hashing. - Aditya Desai; Keren Zhou 001; Anshumali Shrivastava (ICML, 2023)

Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time. - Zichang Liu; Jue Wang; Tri Dao; Tianyi Zhou 002; Binhang Yuan; Zhao Song 002; Anshumali Shrivastava; Ce Zhang 001; Yuandong Tian; Christopher Ré; Beidi Chen (ICML, 2023)

DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries. - Joshua Engels; Benjamin Coleman; Vihan Lakshman; Anshumali Shrivastava (NeurIPS, 2023)

Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time. - Zichang Liu; Aditya Desai; Fangshuo Liao; Weitao Wang; Victor Xie; Zhaozhuo Xu; Anastasios Kyrillidis; Anshumali Shrivastava (NeurIPS, 2023)

One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning. - Zichang Liu; Zhaozhuo Xu; Benjamin Coleman; Anshumali Shrivastava (NeurIPS, 2023)

From Research to Production: Towards Scalable and Sustainable Neural Recommendation Models on Commodity CPU Hardware. - Anshumali Shrivastava; Vihan Lakshman; Tharun Medini; Nicholas Meisburger; Joshua Engels; David Torres Ramos; Benito Geordie; Pratik Pranav; Shubh Gupta; Yashwanth Adunukota; Siddharth Jain (RecSys, 2023)

Graph Self-supervised Learning via Proximity Distribution Minimization. - Tianyi Zhang 011; Zhenwei Dai; Zhaozhuo Xu; Anshumali Shrivastava (UAI, 2023)

A Theoretical Analysis Of Nearest Neighbor Search On Approximate Near Neighbor Graph. - Anshumali Shrivastava; Zhao Song 002; Zhaozhuo Xu (CoRR, 2023)

BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Neural Networks on Commodity CPU Hardware. - 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 (CoRR, 2023)

Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt. - Zhaozhuo Xu; Zirui Liu 001; Beidi Chen; Yuxin Tang; Jue Wang; Kaixiong Zhou; Xia Hu 001; Anshumali Shrivastava (CoRR, 2023)

CARAMEL: A Succinct Read-Only Lookup Table via Compressed Static Functions. - Benjamin Coleman; David Torres Ramos; Vihan Lakshman; Chen Luo 003; Anshumali Shrivastava (CoRR, 2023)

Algorithmic Foundations of Inexact Computing. - John Augustine; Dror Fried; Krishna V. Palem; Duc-Hung Pham; Anshumali Shrivastava (CoRR, 2023)

CAPS: A Practical Partition Index for Filtered Similarity Search. - Gaurav Gupta; Jonah Yi; Benjamin Coleman; Chen Luo 001; Vihan Lakshman; Anshumali Shrivastava (CoRR, 2023)

Zen: Near-Optimal Sparse Tensor Synchronization for Distributed DNN Training. - Zhuang Wang; Zhaozhuo Xu; Anshumali Shrivastava; T. S. Eugene Ng (CoRR, 2023)

Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random Subspaces. - Aditya Desai; Benjamin Meisburger; Zichang Liu; Anshumali Shrivastava (CoRR, 2023)

Adaptive Sampling for Deep Learning via Efficient Nonparametric Proxies. - Shabnam Daghaghi; Benjamin Coleman; Benito Geordie; Anshumali Shrivastava (CoRR, 2023)

Contractive error feedback for gradient compression. - Bingcong Li; Shuai Zheng; Parameswaran Raman; Anshumali Shrivastava; Georgios B. Giannakis (CoRR, 2023)

In defense of parameter sharing for model-compression. - Aditya Desai; Anshumali Shrivastava (ICLR, 2024)

Soft Prompt Recovers Compressed LLMs, Transferably. - Zhaozhuo Xu; Zirui Liu 001; Beidi Chen; Shaochen (Henry) Zhong; Yuxin Tang; Jue Wang; Kaixiong Zhou; Xia Hu 001; Anshumali Shrivastava (ICML, 2024)

KV Cache is 1 Bit Per Channel: Efficient Large Language Model Inference with Coupled Quantization. - Tianyi Zhang 011; Jonah Yi; Zhaozhuo Xu; Anshumali Shrivastava (NeurIPS, 2024)

NoMAD-Attention: Efficient LLM Inference on CPUs Through Multiply-add-free Attention. - Tianyi Zhang 011; Jonah Yi; Bowen Yao; Zhaozhuo Xu; Anshumali Shrivastava (NeurIPS, 2024)

SS1: Accelerating Inference with Fast and Expressive Sketch Structured Transform. - Aditya Desai; Kimia Saedi; Apoorv Walia; Jihyeong Lee; Keren Zhou 001; Anshumali Shrivastava (NeurIPS, 2024)

Learning Scalable Structural Representations for Link Prediction with Bloom Signatures. - Tianyi Zhang 011; Haoteng Yin; Rongzhe Wei; Pan Li 005; Anshumali Shrivastava (WWW, 2024)

Wisdom of Committee: Distilling from Foundation Model to Specialized Application Model. - Zichang Liu; Qingyun Liu 003; Yuening Li; Liang Liu 017; Anshumali Shrivastava; Shuchao Bi; Lichan Hong; Ed H. Chi; Zhe Zhao 001 (CoRR, 2024)

LeanQuant: Accurate Large Language Model Quantization with Loss-Error-Aware Grid. - Tianyi Zhang 011; Anshumali Shrivastava (CoRR, 2024)

SpaLLM: Unified Compressive Adaptation of Large Language Models with Sketching. - Tianyi Zhang 011; Junda Su; Oscar Wu; Zhaozhuo Xu; Anshumali Shrivastava (CoRR, 2024)

CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems. - Haochen Zhang; Tianyi Zhang 011; Junze Yin; Oren Gal; Anshumali Shrivastava; Vladimir Braverman (ACL (Findings), 2025)

LeanQuant: Accurate and Scalable Large Language Model Quantization with Loss-error-aware Grid. - Tianyi Zhang 011; Anshumali Shrivastava (ICLR, 2025)

IDentity with Locality: An Ideal Hash for Gene Sequence Search. - Tianyi Zhang 011; Gaurav Gupta; Aditya Desai; Anshumali Shrivastava (KDD (1), 2025)

ZEN: Empowering Distributed Training with Sparsity-driven Data Synchronization. - Zhuang Wang; Zhaozhuo Xu; Jingyi Xi; Yuke Wang; Anshumali Shrivastava; T. S. Eugene Ng (OSDI, 2025)

I3S: Importance Sampling Subspace Selection for Low-Rank Optimization in LLM Pretraining. - Haochen Zhang; Junze Yin; Guanchu Wang; Zirui Liu 001; Tianyi Zhang 011; Anshumali Shrivastava; Lin Yang 011; Vladimir Braverman (CoRR, 2025)

70% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float. - Tianyi Zhang; Yang Sui; Shaochen Zhong; Vipin Chaudhary; Xia Hu 001; Anshumali Shrivastava (CoRR, 2025)