| 2025 | ACL | CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems. | Haochen Zhang, Tianyi Zhang, Junze Yin, Oren Gal, Anshumali Shrivastava, Vladimir Braverman |
| 2025 | ICLR | LeanQuant: Accurate and Scalable Large Language Model Quantization with Loss-error-aware Grid. | Tianyi Zhang, Anshumali Shrivastava |
| 2025 | ICML | Sketch to Adapt: Fine-Tunable Sketches for Efficient LLM Adaptation. | Tianyi Zhang, Junda Su, Aditya Desai, Oscar Wu, Zhaozhuo Xu, Anshumali Shrivastava |
| 2025 | KDD | IDentity with Locality: An Ideal Hash for Gene Sequence Search. | Tianyi Zhang, Gaurav Gupta, Aditya Desai, Anshumali Shrivastava |
| 2025 | OSDI | ZEN: Empowering Distributed Training with Sparsity-driven Data Synchronization. | Zhuang Wang, Zhaozhuo Xu, Jingyi Xi, Yuke Wang, Anshumali Shrivastava, T. S. Eugene Ng |
| 2024 | ICLR | In defense of parameter sharing for model-compression. | Aditya Desai, Anshumali Shrivastava |
| 2024 | ICML | Soft Prompt Recovers Compressed LLMs, Transferably. | Zhaozhuo Xu, Zirui Liu, Beidi Chen, Shaochen (Henry) Zhong, Yuxin Tang, Jue Wang, Kaixiong Zhou, Xia Hu, Anshumali Shrivastava |
| 2024 | WWW | Learning Scalable Structural Representations for Link Prediction with Bloom Signatures. | Tianyi Zhang, Haoteng Yin, Rongzhe Wei, Pan Li, Anshumali Shrivastava |
| 2023 | AISTATS | A Tale of Two Efficient Value Iteration Algorithms for Solving Linear MDPs with Large Action Space. | Zhaozhuo Xu, Zhao Song, Anshumali Shrivastava |
| 2023 | CIKM | 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, Anshumali Shrivastava |
| 2023 | ICLR | Learning Multimodal Data Augmentation in Feature Space. | Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson |
| 2023 | ICML | Hardware-Aware Compression with Random Operation Access Specific Tile (ROAST) Hashing. | Aditya Desai, Keren Zhou, Anshumali Shrivastava |
| 2023 | ICML | Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time. | Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher R, Beidi Chen |
| 2023 | RecSys | 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 |
| 2023 | UAI | Graph Self-supervised Learning via Proximity Distribution Minimization. | Tianyi Zhang, Zhenwei Dai, Zhaozhuo Xu, Anshumali Shrivastava |
| 2022 | EMNLP | Structural Contrastive Representation Learning for Zero-shot Multi-label Text Classification. | Tianyi Zhang, Zhaozhuo Xu, Tharun Medini, Anshumali Shrivastava |
| 2022 | ICML | 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 |
| 2022 | ICML | DRAGONN: Distributed Randomized Approximate Gradients of Neural Networks. | Zhuang Wang, Zhaozhuo Xu, Xinyu Crystal Wu, Anshumali Shrivastava, T. S. Eugene Ng |
| 2022 | ICRA | Learning to Retrieve Relevant Experiences for Motion Planning. | Constantinos Chamzas, Aedan Cullen, Anshumali Shrivastava, Lydia E. Kavraki |
| 2022 | KDD | BLISS: A Billion scale Index using Iterative Re-partitioning. | Gaurav Gupta, Tharun Medini, Anshumali Shrivastava, Alexander J. Smola |
| 2022 | WWW | ROSE: Robust Caches for Amazon Product Search. | Chen Luo, Vihan Lakshman, Anshumali Shrivastava, Tianyu Cao, Sreyashi Nag, Rahul Goutam, Hanqing Lu, Yiwei Song, Bing Yin |
| 2021 | AAAI | Revisiting Consistent Hashing with Bounded Loads. | John Chen, Benjamin Coleman, Anshumali Shrivastava |
| 2021 | CCS | A One-Pass Distributed and Private Sketch for Kernel Sums with Applications to Machine Learning at Scale. | Benjamin Coleman, Anshumali Shrivastava |
| 2021 | DATE | Neighbor Oblivious Learning (NObLe) for Device Localization and Tracking. | Zichang Liu, Li Chou, Anshumali Shrivastava |
| 2021 | HPSR | Learned Bloom Filters in Adversarial Environments: A Malicious URL Detection Use-Case. | Pedro Reviriego, Jos Alberto Hernndez, Zhenwei Dai, Anshumali Shrivastava |
| 2021 | ICLR | 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, Anshumali Shrivastava, Christopher R |
| 2021 | ICLR | SOLAR: Sparse Orthogonal Learned and Random Embeddings. | Tharun Medini, Beidi Chen, Anshumali Shrivastava |
| 2021 | ICML | A Tale of Two Efficient and Informative Negative Sampling Distributions. | Shabnam Daghaghi, Tharun Medini, Nicholas Meisburger, Beidi Chen, Mengnan Zhao, Anshumali Shrivastava |
| 2021 | ICRA | Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions. | Constantinos Chamzas, Zachary Kingston, Carlos Quintero-Pea, Anshumali Shrivastava, Lydia E. Kavraki |
| 2021 | SIGMOD | Active Sampling Count Sketch (ASCS) for Online Sparse Estimation of a Trillion Scale Covariance Matrix. | Zhenwei Dai, Aditya Desai, Reinhard Heckel, Anshumali Shrivastava |
| 2021 | SIGMOD | 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 |
| 2021 | UAI | SDM-Net: A simple and effective model for generalized zero-shot learning. | Shabnam Daghaghi, Tharun Medini, Anshumali Shrivastava |
| 2020 | AAAI | FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints. | Anastasios Kyrillidis, Anshumali Shrivastava, Moshe Y. Vardi, Zhiwei Zhang |
| 2020 | ICML | Angular Visual Hardness. | Beidi Chen, Weiyang Liu, Zhiding Yu, Jan Kautz, Anshumali Shrivastava, Animesh Garg, Animashree Anandkumar |
| 2020 | ICML | Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data. | Benjamin Coleman, Richard G. Baraniuk, Anshumali Shrivastava |
| 2020 | IJCAI | Mutual Information Estimation using LSH Sampling. | Ryan Spring, Anshumali Shrivastava |
| 2020 | WWW | Sub-linear RACE Sketches for Approximate Kernel Density Estimation on Streaming Data. | Benjamin Coleman, Anshumali Shrivastava |
| 2019 | AAAI | Scaling-Up Split-Merge MCMC with Locality Sensitive Sampling (LSS). | Chen Luo, Anshumali Shrivastava |
| 2019 | ICML | Compressing Gradient Optimizers via Count-Sketches. | Ryan Spring, Anastasios Kyrillidis, Vijai Mohan, Anshumali Shrivastava |
| 2019 | ICRA | Using Local Experiences for Global Motion Planning. | Constantinos Chamzas, Anshumali Shrivastava, Lydia E. Kavraki |
| 2018 | ICLR | Lsh-Sampling breaks the Computational chicken-and-egg Loop in adaptive stochastic Gradient estimation. | Beidi Chen, Yingchen Xu, Anshumali Shrivastava |
| 2018 | ICLR | Scalable Estimation via LSH Samplers (LSS). | Ryan Spring, Anshumali Shrivastava |
| 2018 | ICML | MISSION: Ultra Large-Scale Feature Selection using Count-Sketches. | Amirali Aghazadeh, Ryan Spring, Daniel LeJeune, Gautam Dasarathy, Anshumali Shrivastava, Richard G. Baraniuk |
| 2018 | KDD | TINET: Learning Invariant Networks via Knowledge Transfer. | Chen Luo, Zhengzhang Chen, Lu-An Tang, Anshumali Shrivastava, Zhichun Li, Haifeng Chen, Jieping Ye |
| 2018 | PSD | Probabilistic Blocking with an Application to the Syrian Conflict. | Rebecca C. Steorts, Anshumali Shrivastava |
| 2018 | WWW | Arrays of (locality-sensitive) Count Estimators (ACE): Anomaly Detection on the Edge. | Chen Luo, Anshumali Shrivastava |
| 2018 | WWW | Training 100, 000 Classes on a Single Titan X in 7 Hours or 15 Minutes with 25 Titan Xs. | Anshumali Shrivastava |
| 2018 | SIGMOD | Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search. | Yiqiu Wang, Anshumali Shrivastava, Jonathan Wang, Junghee Ryu |
| 2018 | UAI | Densified Winner Take All (WTA) Hashing for Sparse Datasets. | Beidi Chen, Anshumali Shrivastava |
| 2017 | DATE | Location detection for navigation using IMUs with a map through coarse-grained machine learning. | E. J. Jose Gonzalez, Chen Luo, Anshumali Shrivastava, Krishna V. Palem, Yongshik Moon, Soonhyun Noh, Daedong Park, Seongsoo Hong |
| 2017 | ICML | Optimal Densification for Fast and Accurate Minwise Hashing. | Anshumali Shrivastava |
| 2017 | IJCAI | RHash: Robust Hashing via L_infinity-norm Distortion. | Amirali Aghazadeh, Andrew S. Lan, Anshumali Shrivastava, Richard G. Baraniuk |
| 2017 | KDD | Scalable and Sustainable Deep Learning via Randomized Hashing. | Ryan Spring, Anshumali Shrivastava |
| 2016 | SIGMOD | Time Adaptive Sketches (Ada-Sketches) for Summarizing Data Streams. | Anshumali Shrivastava, Arnd Christian Knig, Mikhail Bilenko |
| 2015 | WWW | Asymmetric Minwise Hashing for Indexing Binary Inner Products and Set Containment. | Anshumali Shrivastava, Ping Li |
| 2015 | UAI | Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS). | Anshumali Shrivastava, Ping Li |
| 2014 | AISTATS | In Defense of Minhash over Simhash. | Anshumali Shrivastava, Ping Li |
| 2014 | ICML | Coding for Random Projections. | Ping Li, Michael Mitzenmacher, Anshumali Shrivastava |
| 2014 | ICML | Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search. | Anshumali Shrivastava, Ping Li |
| 2014 | UAI | Improved Densification of One Permutation Hashing. | Anshumali Shrivastava, Ping Li |
| 2012 | CIKM | Fast multi-task learning for query spelling correction. | Xu Sun, Anshumali Shrivastava, Ping Li |
| 2012 | WWW | GPU-based minwise hashing: GPU-based minwise hashing. | Ping Li, Anshumali Shrivastava, Arnd Christian Knig |
| 2012 | WWW | Query spelling correction using multi-task learning. | Xu Sun, Anshumali Shrivastava, Ping Li |