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AlphaD3M: An Open-Source AutoML Library for Multiple ML Tasks
DE PAULA LOURENCO, Raoni; Rampin, Remi; Castelo, Sonia et al.
2023AutoML Conference 2023 (ABCD Track)
Peer reviewed
 

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Keywords :
AutoML
Abstract :
[en] We present AlphaD3M, an open-source Python library that supports a wide range of machine learning tasks over different data types. We discuss the challenges involved in supporting multiple tasks and how AlphaD3M addresses them by combining deep reinforcement learning and meta-learning to construct pipelines over a large collection of primitives effectively. To better integrate the use of AutoML within the data science lifecycle, we have built an ecosystem of tools around AlphaD3M that support user-in-the-loop tasks, including selecting suitable pipelines and developing custom solutions for complex problems. We present use cases that demonstrate some of these features. We report the results of a detailed experimental evaluation showing that AlphaD3M is effective and derives highquality pipelines for a diverse set of problems with performance comparable or superior to state-of-the-art AutoML systems.
Disciplines :
Computer science
Author, co-author :
DE PAULA LOURENCO, Raoni  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal
Rampin, Remi;  New York University
Castelo, Sonia;  New York University
Santos, Aécio;  New York University
Ono, Jorge;  New York University
Silva, Claudio;  New York University
Freire, Juliana;  New York University
Speaker :
Lopez, Roque;  New York University
External co-authors :
yes
Language :
English
Title :
AlphaD3M: An Open-Source AutoML Library for Multiple ML Tasks
Publication date :
12 September 2023
Event name :
AutoML Conference 2023 (ABCD Track)
Event place :
Berlin, Germany
Event date :
Sep 12 2023
Audience :
International
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 22 November 2023

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