AML 2019: 1st IEEE ICDM Workshop on Autonomous Machine Learning (AML 2019) |
Website | https://sites.google.com/site/icdmaml2019/ |
Submission link | https://easychair.org/conferences/?conf=aml19 |
Submission deadline | August 7, 2019 |
1st IEEE ICDM Workshop on Autonomous Machine Learning (AML 2019)
In conjunction with the IEEE International Conference on Data Mining (ICDM 2019)
November 8-11, 2019
Beijing, China
Autonomous machine learning can simply be treated as progressive automation of data-driven learning that aims to be accomplished through purely self-governing process, without allowing any manual intervention or engineering knowledge during the course of learning. Due to its huge potential for extensive application in almost every span of real-life, autonomous machine learning has received notable attentions within just a few years. However, its current research progress is mainly confined to the domain of control and automation. Further, majority of these existing autonomous machine learning models possess limited capacity and ignore the resource constraints, demanding considerable computational time and memory, which is not at all suitable in the real-world scenario where the data arrive in streaming fashion. The prime objective of this workshop (AML 2019) is to provide an exclusive opportunity to the researchers and practitioners from machine learning, data mining, and other interdisciplinary communities to identify and discuss these emerging issues in autonomous machine learning. AML 2019 is not "yet another Auto-ML workshop" focusing only on developing quality machine learning models with little/no human engineering. Rather, this workshop will throw light on autonomous machine learning from the perspective of real-time analytics as well as the self-organizing capability to keep itself relevant to the most recent context without suffering from catastrophic forgetting. Thus, the primary scopes of this workshop include the following:
- Identifying challenges and opportunities for autonomous learning in the field of AI and machine intelligence, in view of wide-ranging application domains;
- Exploring ways to develop theoretical frameworks and to handle practical issues for advancements of autonomous learning with consideration to variants of computational framework and learning paradigm, including reinforcement learning, unsupervised learning, transfer learning, deep learning, big data analytics and distributed computing;
- Moving towards resource-efficient autonomous machine learning models, focusing on online single pass learning with dynamically evolving model capacity;
- Devising appropriate evaluation criteria to achieve improved comparability of the various autonomous machine learning models.
The AML 2019 workshop intends to bring together professionals, researchers, and practitioners from both industry and academia to promote awareness and collaboration regarding the advancements of autonomous machine learning. The goal is to provide them with a common platform for reviewing the state-of-the-arts and real-life scenarios, discussing on the open research challenges, sharing their ideas, experiences, and contributions on the recent progresses of autonomous machine learning, and finally, to set future directions for innovative research in this context.
Call for Papers
We welcome submission of full-length papers (up to 10 pages) or short-paper/extended-abstracts (2-4 pages) which are not published/communicated elsewhere.
Paper Guidelines:
All the papers submitted in AML2019 will be peer-reviewed. If accepted, the paper will be published in the IEEE ICDM 2019 workshop proceedings and will also be included in the IEEE explore digital library. For each accepted paper, at least one author must attend the workshop and present the paper. The papers should be prepared as per the IEEE conference proceedings template and to be submitted electronically in PDF format via the following link of EasyChair:
Submission Link: https://easychair.org/conferences/?conf=aml2019
Papers that do not meet the formatting requirements will be rejected without review.
Topics: The major topics of interest include but are not limited to the following.
- Autonomous feature selection
- Autonomous knowledge expansion and contraction
- Autonomous transfer learning
- Autonomous multi-task learning
- Autonomous unsupervised learning
- Autonomous reinforcement learning
- Autonomous deep learning
- Autonomous machine learning for big data analytics
- Real-world application of autonomous machine learning in
- Document processing, text mining
- Language processing, Speech recognition, music analysis
- Time series prediction
- Streaming data analysis, Multi-stream analysis
- Complex image classification, Object recognition
- Computer vision, Robotics, Control system
- Spatial/Spatio-temporal data mining
- Mobility analytics
Awards:
- Best Paper Award: The program committee will nominate a paper for the Best Paper award. The best paper will be selected based on the paper’s contribution, novelty, and presentation quality. Additionally, there will be a separate award for the paper whose primary author is a student.
Important Dates:
- Paper submission: August 7, 2019 (Anywhere on Earth)
- Author notification: September 4, 2019
- Camera-ready version: September 8, 2019
More information:
For more information about ICDM-AML 2019, please visit https://sites.google.com/site/icdmaml2019/
For any query, please email the organizing committee at Email: icdmaml12019@gmail.com
Organizers:
- Dr. Mahardhika Pratama [NTU, Singapore] (mpratama@ntu.edu.sg)
- Dr. Ong Yew Soon [NTU, Singapore] (asysong@ntu.edu.sg)
- Dr. Zhang Jie [NTU, Singapore] (ZhangJ@ntu.edu.sg)
- Dr. Edwin Lughofer [JKU, Softwarepark, Hagenberg] (edwin.lughofer@jku)
- Dr. Weiping Ding [Nantong University (NTU), China] (dwp9988@hotmail.com)
- Dr. Ms. Monidipa Das [NTU, Singapore] (monidipadas@ntu.edu.sg)