ADA Lab @ UCSD
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Project Triptych |
Overview
Triptych is an end-to-end model selection management system (MSMS) that aims to simplify and accelerate the process of sourcing data/features and selecting ML models. Our guiding principles are to exploit the semantics of the data and the ML task to the extent possible to reduce work for the data scientist and reduce runtimes and costs. We apply these principles to remove or mitigate different bottlenecks in this end-to-end process, eventually unifying these components to yield an integrated ‘‘operating system’’ for ML analytics tasks. Please refer to the ACM SIGMOD Record paper below for more details of this vision.
Active Component Projects
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Cerebro |
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Morpheus |
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SortingHat |
Publications
Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches
Yuhao Zhang, Frank McQuillan, Nandish Jayaram, Nikhil Kak, Ekta Khanna, Orhan Kislal, Domino Valdano, and Arun Kumar
VLDB 2021 (To appear) | Paper PDF | TechReport | Code release
Towards an Optimized GROUP BY Abstraction for Large-Scale Machine Learning
Side Li and Arun Kumar
VLDB 2021 (To appear) | Paper PDF coming soon | TechReport | Code coming soon
Intermittent Human-in-the-Loop Model Selection using Cerebro: A Demonstration
Liangde Li, Supun Nakandala, and Arun Kumar
VLDB 2021 Demo (To appear) | Paper PDF coming soon | TechReport | Video
Towards A Polyglot Framework for Factorized ML
David Justo, Shaoqing Yi, Lukas Stadler, Nadia Polikarpova, and Arun Kumar
VLDB 2021 (Industrial Track; to appear) | Paper PDF coming soon | TechReport | Code coming soon
Towards Benchmarking Feature Type Inference for AutoML Platforms
Vraj Shah, Jonathan Lacanlale, Premanand Kumar, Kevin Yang, and Arun Kumar
ACM SIGMOD 2021 | Paper PDF | TechReport | Talk Videos: Short Talk Long Talk | Data, Code, and Pre-trained Models on GitHub | Python library
The CNN Hip Accelerometer Posture (CHAP) Method for Classifying Sitting Patterns from Hip Accelerometers: A Validation Study
Mikael Anne Greenwood-Hickman, Supun Nakandala, Marta M. Jankowska, Fatima Tuz-Zahra, John Bellettiere, Jordan Carlson, Paul R. Hibbing, Jingjing Zou, Andrea Z. LaCroix, Arun Kumar, and Loki Natarajan
Medicine and Science in Sports and Exercise Journal, 2021 (To appear) | Paper PDF coming soon | Code
Application of Convolutional Neural Network Algorithms for Advancing Sedentary and Activity Bout Classification
Supun Nakandala, Marta Jankowska, Fatima Tuz-Zahra, John Bellettiere, Jordan Carlson, Andrea LaCroix, Sheri Hartman, Dori Rosenberg, Jingjing Zou, Arun Kumar, and Loki Natarajan
Journal for the Measurement of Physical Behaviour, 2021 | Paper PDF and BibTeX | Code
Cerebro: A Layered Data Platform for Scalable Deep Learning
Arun Kumar, Supun Nakandala, Yuhao Zhang, Side Li, Advitya Gemawat, and Kabir Nagrecha
CIDR 2021 (Vision paper) | Paper PDF and BibTeX | Talk video
Cerebro: A Data System for Optimized Deep Learning Model Selection
Supun Nakandala, Yuhao Zhang, and Arun Kumar
VLDB 2020 | Paper PDF and BibTeX | Errata | TechReport | Talk videos: Youtube Bilibili | Blog post | SAIS Talk video | Source code and documentation
Enabling and Optimizing Non-linear Feature Interactions in Factorized Linear Algebra
Side Li, Lingjiao Chen, and Arun Kumar
ACM SIGMOD 2019 | Paper PDF and BibTeX | Code and Data on Github
Tuple-oriented Compression for Large-scale Mini-batch Stochastic Gradient Descent
Fengan Li, Lingjiao Chen, Yijing Zeng, Arun Kumar, Jeffrey Naughton, Jignesh Patel, and Xi Wu
ACM SIGMOD 2019 | Paper PDF | TechReport | Code on GitHub
Model-based Pricing for Machine Learning in a Data Marketplace
Lingjiao Chen, Paraschos Koutris, and Arun Kumar
ACM SIGMOD 2019 | Paper PDF | TechReport | Code and Data coming soon
Cerebro: Efficient and Reproducible Model Selection on Deep Learning Systems
Supun Nakandala, Yuhao Zhang, and Arun Kumar
ACM SIGMOD 2019 DEEM Workshop | Paper PDF and BibTeX | TechReport | Blog post
The ML Data Prep Zoo: Towards Semi-Automatic Data Preparation for ML
Vraj Shah and Arun Kumar
ACM SIGMOD 2019 DEEM Workshop | Paper PDF and BibTeX | TechReport | Blog post | Data Prep Zoo Repository on GitHub
Demonstration of Nimbus: Model-based Pricing for Machine Learning in a Data Marketplace
Lingjiao Chen, Hongyi Wang, Leshang Chen, Paraschos Koutris, and Arun Kumar
ACM SIGMOD 2019 Demo | Paper PDF | Video coming soon
A Comparative Evaluation of Systems for Scalable Linear Algebra-based Analytics
Anthony Thomas and Arun Kumar
VLDB 2018/2019 | Paper PDF | TechReport | Code and Data
Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers?
Vraj Shah, Arun Kumar, and Xiaojin Zhu.
VLDB 2018 | Paper PDF and BibTeX| TechReport | Code and Data
Towards Linear Algebra over Normalized Data
Lingjiao Chen, Arun Kumar, Jeffrey Naughton, and Jignesh Patel
VLDB 2017 | Paper PDF | TechReport | Code and Data
Model-based Pricing: Do Not Pay for More than What You Learn!
Lingjiao Chen, Paraschos Koutris, and Arun Kumar
ACM SIGMOD 2017 DEEM Workshop | Paper PDF
Cerebro: A System to Manage Deep Learning for Relational Data Analytics
Arun Kumar
CIDR 2017 Abstract | Paper PDF
To Join or Not to Join? Thinking Twice about Joins before Feature Selection
Arun Kumar, Jeffrey Naughton, Jignesh M. Patel, and Xiaojin Zhu
ACM SIGMOD 2016 | Paper PDF and BibTeX | TechReport | Code and Data
Model Selection Management Systems: The Next Frontier of Advanced Analytics
Arun Kumar, Robert McCann, Jeffrey Naughton, and Jignesh M. Patel
ACM SIGMOD Record Dec 2015 Vision Track | Paper PDF
Technical Reports
Nautilus: An Optimized System for Deep Transfer Learning over Evolving Training Datasets
Supun Nakandala and Arun Kumar
Under submission | TechReport
SystemX: A Scalable and Optimized Data System for Large Multi-Model Deep Learning
Kabir Nagrecha and Arun Kumar
Under submission | TechReport
Improving Feature Type Inference Accuracy of TFDV with SortingHat
Vraj Shah, Kevin Yang, and Arun Kumar
TechReport
Past Projects
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Hamlet |
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Nimbus |
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SLAB |