Tupleware: Distributed Machine Learning on Small Clusters

By Andrew Crotty, Alex Galakatos, Tim Kraska. 2014

17 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Sanjay Krishnan
2. M. Franklin
3. Jiannan Wang
4. Eugene Wu
5. Ken Goldberg
6. Ihab F. Ilyas
7. Tova Milo
8. Tim Kraska

Citations in the corpus

• BoostClean: Automated Error Detection and Repair for Machine Learning (2017, methodology): “The database community has built systems to support almost every stage of the development process including featuriza-tion [1,53], distributed model training [2,17,19,24], and model deployment [16].”
• ActiveClean: An Interactive Data Cleaning Framework For Modern Machine Learning (2016, background): “Model training on large and growing datasets is a key data management challenge with significant interest in both industry and academia [1, 2, 5].”
• ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models (2016, background): “Machine Learning on large and growing datasets is a key data management challenge with significant interest in both industry and academia [1, 5, 10, 20].”
• KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics (2016, unknown): “and scalable implementations [17, 44] for model training, they do not capture the featurization process or the logical intent of the workflow.”
• PALM: Machine Learning Explanations For Iterative Debugging (2017, methodology): “While the data-base community has made substantial contributions in other areas of machine learning (e.g., training scalability and model specifica-tion [1, 2, 5, 8, 10]), the opportunity is ripe for developing scalable tools to debug modern machine learning models.”
• Robust Distributed Gradient Descent with Arbitrary Number of Byzantine Attackers (2018, background): “The design of distributed optimization algorithms has attracted significant recent research interests [1, 2, 3, 4, 5].”
• SampleClean: Fast and Reliable Analytics on Dirty Data (2015, background): “5 ActiveClean: Machine Learning on Dirty Data [31] Analytics is moving beyond SQL, and the growing popularity of predictive models [1, 7, 17, 28] leads to additional challenges in managing dirty data.”
• Distributed Gradient Descent Algorithm Robust to an Arbitrary Number of Byzantine Attackers (2019, background): “INTRODUCTION THE design of distributed optimization algorithms has attracted significant recent research interests [2]–[15].”