Demo Data from EDBT 2026 / reviewers in the wild / expert
Deepak Mukunthu Iyappan Babu
dblp:273/0112
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2Databases, data management, data science and information retrieval · 2
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
2 papers |
Efficient and distributed learning · 62% Deep learning architectures and training · 19% Learning theory · 19% | |
| Software engineering, system software, and programming languages
1 paper |
Software maintenance and evolution · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Efficient and distributed learning
automated machine learning |
0.4 | 1 | 2020 | From Zero to AI Hero with Automated Machine Learning · KDD 2020 |
Software maintenance and evolution › release engineering
continuous integration |
0.4 | 1 | 2020 | Building Continuous Integration Services for Machine Learning · KDD 2020 |
Machine learning › Learning theory
model selection |
0.1 | 1 | 2020 | From Zero to AI Hero with Automated Machine Learning · KDD 2020 |
Methods — techniques the papers use, named apart from their topics
continuous integration · 0.9learning algorithm selection · 0.4hyperparameter optimization · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Building Continuous Integration Services for Machine LearningabstractContinuous integration (CI) has been a de facto standard for building industrial-strength software. Yet, there is little attention towards applying CI to the development of machine learning (ML) applications until the very recent effort on the theoretical side. In this paper, we take a step forward to bring the theory into practice. Bojan Karlas, Matteo Interlandi, Cédric Renggli, Wentao Wu 0001, Ce Zhang 0001, Deepak Mukunthu Iyappan Babu, Jordan Edwards, Chris Lauren, Andy Xu, Markus Weimer |
KDD | 6 |
| 2020 | From Zero to AI Hero with Automated Machine LearningabstractAutomated ML is an emerging field in Machine Learning that helps developers and new data scientists with little data science knowledge build Machine Learning models and solutions without understanding the complexity of Learning Algorithm selection, and Hyper parameter tuning. With Azure Machine Learning's automated machine learning capability, given a dataset and a few configuration parameters, you will get a trained high quality machine learning model for the dataset that you can use for predictions. In this session, you will learn how to use Automated ML for productivity gains, empowering domain experts to build ML based solutions and scale to build several models with Azure Machine Learning's Automated ML. Aniththa Umamahesan, Deepak Mukunthu Iyappan Babu |
KDD | 2 |