SARNAclust: Semi-automatic detection of RNA protein binding motifs from immunoprecipitation data. - Iván Dotú; Scott I. Adamson; Benjamin Coleman; Cyril Fournier; Emma Ricart-Altimiras; Eduardo Eyras; Jeffrey H. Chuang (PLoS Comput. Biol., 2018)

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

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

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

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

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

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

Development of a Point-of-Care Assay for HIV-1 Viral Load Using Higher Refractive Index Antibody-Coated Microbeads. - Mazhar Sher; Benjamin Coleman; Massimo Caputi; Waseem Asghar (Sensors, 2021)

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)

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

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

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)

Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems. - Benjamin Coleman; Wang-Cheng Kang; Matthew Fahrbach; Ruoxi Wang; Lichan Hong; Ed H. Chi; Derek Zhiyuan Cheng (NeurIPS, 2023)

DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries. - Joshua Engels; Benjamin Coleman; Vihan Lakshman; 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)

Efficient Data Representation Learning in Google-scale Systems. - Derek Zhiyuan Cheng; Ruoxi Wang; Wang-Cheng Kang; Benjamin Coleman; Yin Zhang 011; Jianmo Ni; Jonathan Valverde; Lichan Hong; Ed H. Chi (RecSys, 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)

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)

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

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

Improving Data Efficiency for Recommenders and LLMs. - Noveen Sachdeva; Benjamin Coleman; Wang-Cheng Kang; Jianmo Ni; James Caverlee; Lichan Hong; Ed H. Chi; Derek Zhiyuan Cheng (RecSys, 2024)

How to Train Data-Efficient LLMs. - Noveen Sachdeva; Benjamin Coleman; Wang-Cheng Kang; Jianmo Ni; Lichan Hong; Ed H. Chi; James Caverlee; Julian J. McAuley; Derek Zhiyuan Cheng (CoRR, 2024)

Down with the Hierarchy: The 'H' in HNSW Stands for "Hubs". - Blaise Munyampirwa; Vihan Lakshman; Benjamin Coleman (CoRR, 2024)

Gemma 3 Technical Report. - Aishwarya Kamath; Johan Ferret; Shreya Pathak; Nino Vieillard; Ramona Merhej; Sarah Perrin; Tatiana Matejovicova; Alexandre Ramé; Morgane Rivière; Louis Rouillard; Thomas Mesnard; Geoffrey Cideron; et al. (CoRR, 2025)