Benjamin Coleman

According to our database, Benjamin Coleman authored at least 29 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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

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

Proceedings of the 18th ACM Conference on Recommender Systems, 2024

2023

Adaptive Sampling for Deep Learning via Efficient Nonparametric Proxies.

Shabnam Daghaghi, Benjamin Coleman, Benito Geordie, Anshumali Shrivastava

CoRR, 2023

CAPS: A Practical Partition Index for Filtered Similarity Search.

Gaurav Gupta, Jonah Yi, Benjamin Coleman, Chen Luo, Vihan Lakshman, Anshumali Shrivastava

CoRR, 2023

CARAMEL: A Succinct Read-Only Lookup Table via Compressed Static Functions.

Benjamin Coleman, David Torres Ramos, Vihan Lakshman, Chen Luo, Anshumali Shrivastava

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

Efficient Data Representation Learning in Google-scale Systems.

Derek Zhiyuan Cheng, Ruoxi Wang, Wang-Cheng Kang, Benjamin Coleman, Yin Zhang, Jianmo Ni, Jonathan Valverde, Lichan Hong, Ed H. Chi

Proceedings of the 17th ACM Conference on Recommender Systems, 2023

One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning.

Zichang Liu, Zhaozhuo Xu, Benjamin Coleman, Anshumali Shrivastava

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries.

Joshua Engels, Benjamin Coleman, Vihan Lakshman, Anshumali Shrivastava

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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, Anshumali Shrivastava

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

2022

Retaining Knowledge for Learning with Dynamic Definition.

Zichang Liu, Benjamin Coleman, Tianyi Zhang, Anshumali Shrivastava

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Graph Reordering for Cache-Efficient Near Neighbor Search.

Benjamin Coleman, Santiago Segarra, Alexander J. Smola, Anshumali Shrivastava

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 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

Proceedings of the International Conference on Machine Learning, 2022

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

Efficient Inference via Universal LSH Kernel.

Zichang Liu, Benjamin Coleman, Anshumali Shrivastava

CoRR, 2021

Density Sketches for Sampling and Estimation.

Aditya Desai, Benjamin Coleman, Anshumali Shrivastava

CoRR, 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

Proceedings of the SIGMOD '21: International Conference on Management of Data, 2021

Practical Near Neighbor Search via Group Testing.

Joshua Engels, Benjamin Coleman, Anshumali Shrivastava

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A One-Pass Distributed and Private Sketch for Kernel Sums with Applications to Machine Learning at Scale.

Benjamin Coleman, Anshumali Shrivastava

Proceedings of the CCS '21: 2021 ACM SIGSAC Conference on Computer and Communications Security, Virtual Event, Republic of Korea, November 15, 2021

Revisiting Consistent Hashing with Bounded Loads.

John Chen, Benjamin Coleman, Anshumali Shrivastava

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Bloom Origami Assays: Practical Group Testing.

Louis Abraham, Gary Bécigneul, Benjamin Coleman, Bernhard Schölkopf, Anshumali Shrivastava, Alexander J. Smola

CoRR, 2020

STORM: Foundations of End-to-End Empirical Risk Minimization on the Edge.

Benjamin Coleman, Gaurav Gupta, John Chen, Anshumali Shrivastava

CoRR, 2020

A One-Pass Private Sketch for Most Machine Learning Tasks.

Benjamin Coleman, Anshumali Shrivastava

CoRR, 2020

Sub-linear RACE Sketches for Approximate Kernel Density Estimation on Streaming Data.

Benjamin Coleman, Anshumali Shrivastava

Proceedings of the WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, 2020

Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data.

Benjamin Coleman, Richard G. Baraniuk, Anshumali Shrivastava

Proceedings of the 37th International Conference on Machine Learning, 2020

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

RACE: Sub-Linear Memory Sketches for Approximate Near-Neighbor Search on Streaming Data.

Benjamin Coleman, Anshumali Shrivastava, Richard G. Baraniuk

CoRR, 2019

2018

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