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Benjamin Coleman
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2020 – today
- 2024
- [i19]Noveen Sachdeva, Benjamin Coleman, Wang-Cheng Kang, Jianmo Ni, Lichan Hong, Ed H. Chi, James Caverlee, Julian J. McAuley, Derek Zhiyuan Cheng:
How to Train Data-Efficient LLMs. CoRR abs/2402.09668 (2024) - 2023
- [c14]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 - [c13]Benjamin Coleman, Wang-Cheng Kang, Matthew Fahrbach, Ruoxi Wang, Lichan Hong, Ed H. Chi, Derek Zhiyuan Cheng:
Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems. NeurIPS 2023 - [c12]Joshua Engels, Benjamin Coleman, Vihan Lakshman, Anshumali Shrivastava:
DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries. NeurIPS 2023 - [c11]Zichang Liu, Zhaozhuo Xu, Benjamin Coleman, Anshumali Shrivastava:
One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning. NeurIPS 2023 - [c10]Derek Zhiyuan Cheng
, Ruoxi Wang
, Wang-Cheng Kang
, Benjamin Coleman
, Yin Zhang
, Jianmo Ni
, Jonathan Valverde
, Lichan Hong
, Ed H. Chi:
Efficient Data Representation Learning in Google-scale Systems. RecSys 2023: 267-271 - [i18]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) - [i17]Benjamin Coleman, Wang-Cheng Kang, Matthew Fahrbach, Ruoxi Wang, Lichan Hong, Ed H. Chi, Derek Zhiyuan Cheng:
Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems. CoRR abs/2305.12102 (2023) - [i16]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) - [i15]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) - [i14]Shabnam Daghaghi, Benjamin Coleman, Benito Geordie, Anshumali Shrivastava:
Adaptive Sampling for Deep Learning via Efficient Nonparametric Proxies. CoRR abs/2311.13583 (2023) - 2022
- [c9]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 - [c8]Benjamin Coleman, Santiago Segarra, Alexander J. Smola, Anshumali Shrivastava:
Graph Reordering for Cache-Efficient Near Neighbor Search. NeurIPS 2022 - [c7]Zichang Liu, Benjamin Coleman, Tianyi Zhang, Anshumali Shrivastava:
Retaining Knowledge for Learning with Dynamic Definition. NeurIPS 2022 - [i13]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) - 2021
- [j2]Mazhar Sher, Benjamin Coleman, Massimo Caputi, Waseem Asghar
:
Development of a Point-of-Care Assay for HIV-1 Viral Load Using Higher Refractive Index Antibody-Coated Microbeads. Sensors 21(5): 1819 (2021) - [c6]John Chen, Benjamin Coleman, Anshumali Shrivastava:
Revisiting Consistent Hashing with Bounded Loads. AAAI 2021: 3976-3983 - [c5]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 - [c4]Joshua Engels, Benjamin Coleman, Anshumali Shrivastava:
Practical Near Neighbor Search via Group Testing. NeurIPS 2021: 9950-9962 - [c3]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 - [i12]Aditya Desai, Benjamin Coleman, Anshumali Shrivastava:
Density Sketches for Sampling and Estimation. CoRR abs/2102.12301 (2021) - [i11]Benjamin Coleman, Santiago Segarra, Anshumali Shrivastava, Alex Smola:
Graph Reordering for Cache-Efficient Near Neighbor Search. CoRR abs/2104.03221 (2021) - [i10]Zichang Liu, Benjamin Coleman, Anshumali Shrivastava:
Efficient Inference via Universal LSH Kernel. CoRR abs/2106.11426 (2021) - [i9]Joshua Engels, Benjamin Coleman, Anshumali Shrivastava:
Practical Near Neighbor Search via Group Testing. CoRR abs/2106.11565 (2021) - 2020
- [c2]Benjamin Coleman, Richard G. Baraniuk, Anshumali Shrivastava:
Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data. ICML 2020: 2089-2099 - [c1]Benjamin Coleman, Anshumali Shrivastava:
Sub-linear RACE Sketches for Approximate Kernel Density Estimation on Streaming Data. WWW 2020: 1739-1749 - [i8]Benjamin Coleman, Anshumali Shrivastava:
A One-Pass Private Sketch for Most Machine Learning Tasks. CoRR abs/2006.09352 (2020) - [i7]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) - [i6]Louis Abraham, Gary Bécigneul, Benjamin Coleman, Bernhard Schölkopf, Anshumali Shrivastava, Alexander J. Smola:
Bloom Origami Assays: Practical Group Testing. CoRR abs/2008.02641 (2020)
2010 – 2019
- 2019
- [i5]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) - [i4]John Chen, Benjamin Coleman, Anshumali Shrivastava:
Revisiting Consistent Hashing with Bounded Loads. CoRR abs/1908.08762 (2019) - [i3]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) - [i2]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) - [i1]Benjamin Coleman, Anshumali Shrivastava:
Sub-linear RACE Sketches for Approximate Kernel Density Estimation on Streaming Data. CoRR abs/1912.02283 (2019) - 2018
- [j1]Iván Dotú, Scott I. Adamson
, Benjamin Coleman, Cyril Fournier
, Emma Ricart-Altimiras, Eduardo Eyras
, Jeffrey H. Chuang
:
SARNAclust: Semi-automatic detection of RNA protein binding motifs from immunoprecipitation data. PLoS Comput. Biol. 14(3) (2018)
Coauthor Index
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last updated on 2024-08-05 21:21 CEST by the dblp team
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