Second Edition:
"Lifelong Machine Learning."
by Z. Chen and B. Liu, Morgan & Claypool, August 2018 (1st edition, 2016)
- Three new chapters have been added and others have been updated and/or reorganized.
- One Chapter is dedicated to Open World Learning
- Any AI system (e.g., chatbot and self-driving car) that cannot learn in deployment (e.g., chatting and driving) in the real-world open envoronment is not truly intelligent.
Motivation for Autonomous Learning and Self-evolving Agents: It is known that about 70%
of our human knowledge comes
from "on-the-job" learning. Only about 10% is learned through formal
education and the rest 20% is learned through observation of others
(imitation). An autonomous AI agent must have this capability - its
machine learning algorithm must be able to learn autonomously on the job
or while working after model deployment so that it can
self-improve. As the real world is
too complex and constantly changing, it is impossible to learn everything
through offline training using manually labeled data. An autonomous learning
agent must explore and learn by itself in the real world, which is
open and constantly change - full of unknowns. AI agents must be able to
detect the unknowns and learn them in a self-supervised manner through
its interaction with humans, other agents, and the real-world environment.
It should not make the closed-world assumption any more.
Autonomous Learning: Like human on-the-job learning, it studies
learning after model deployment for self-evolution - after a
good model has been built and deployed in an application.
In classic machine learning, once a model is built, it is
deployed in an application. During application,
the model remains fixed or unchanged. Autonomous learning (or on-the-job learning) investigates continuous learning and self-improvement after model deployment, which involves the following steps
- continuously discover new tasks to learn by the agent itself. This is called Open World Learning or Out-of-Distribtuion Detection.
- gather "free" ground truth training data through the agent’s own active effort via interaction with humans, other agents and the environment.
- incrementally learn the new tasks without interrupting the application to become more and more knowledgeable. This is continual learning.
Publications
TextBook: Zhiyuan Chen and Bing Liu. Lifelong Machine Learning. Morgan & Claypool, 2018 (2nd edition), 2016 (1st edition).
- Saleh Momeni, Changnan Xiao, Bing Liu. Continual Out-of-Distribution Detection with Analytic Neural Collapse, Proceedings of AAAI-2026 (Oral), Jan. 20 - Jan. 27, 2026, Singapore.
- Alexander Politowicz, Sahisnu Mazumder, Bing Liu. Improving OOD Detection Using Segmented Images and Cross-View Attention Fusion, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision 2026 (WACV-2026), March 6 - 10, 2026, Tucson, Arizona.
- Derda Kaymak, Gyuhak Kim, Tomoya Kaichi, Tatsuya Konishi and Bing Liu.
Learning after Model Deployment. Proceedings of the European Conference on Artificial Intelligence (ECAI-2025), Oct 25-30, 2025, Bogona, Italy.
- Gyuhak Kim, Changnan Xiao, Tatsuya Konishi, Zixuan Ke, Bing Liu. Open-World Continual Learning: Unifying Novelty Detection and Continual Learning. Artificial Intelligence Journal, 2024.
- Bing Liu, Sahisnu Mazumder, Eric Robertson, and Scott Grigsby.
AI Autonomy: Self-Initiated Open-World Continual Learning and Adaptation. AI Magazine, May 21, 2023.
- Bing Liu, Sahisnu Mazumder, Eric Robertson, and Scott Grigsby.
AI Autonomy: Self-Initiation, Adaptation and Continual Learning. arXiv:2203.08994 [cs.AI], March 17, 2022.
- Bing Liu and Sahisnu Mazumder. Lifelong and Continual Learning Dialogue Systems: Learning during Conversation. Proceedings of AAAI-2021. 2021.
- Sahisnu Mazumder, Bing Liu, Shuai Wang, and Sepideh Esmaeilpour.
An Application-Independent Approach to Building Task-Oriented Chatbots with Interactive Continual Learning. NeurIPS-2020 Workshop on Human in the Loop Dialogue Systems (HLDS-2020). 2020.
- Sahisnu Mazumder, Bing Liu, Nianzu Ma, Shuai Wang. Continuous and Interactive Factual Knowledge Learning in Verification Dialogues. NeurIPS-2020 Workshop on Human And Machine in-the-Loop Evaluation and Learning Strategies (HAMLETS-2020). 2020.
- Bing Liu and Chuhe Mei. Lifelong Knowledge Learning in Rule-based Dialogue Systems. arXiv:2011.09811 [cs.AI], 2020.
- Bing Liu. Learning on the Job: Online Lifelong and Continual Learning. Proceedings of 34th AAAI Conference on Artifical Intelligence (AAAI-2020), Feb 7-12, 2020, New York City. (This work was done while I was on leave in Peking University).
- Sahisnu Mazumder, Bing Liu, ShuaiWang, Nianzu Ma. Lifelong and Interactive Learning of Factual Knowledge in Dialogues. Proceedings of Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL-2019), 11-13 September 2019, Stockholm, Sweden.
- Hu Xu, Bing Liu, Lei Shu and P. Yu. Open-world Learning and Application to Product Classification. Proceedings of the Web Conference (formerly known as the WWW conference), San Francisco, May 13-17, 2019.
- Lei Shu, Hu Xu, Bing Liu. Unseen Class Discovery in Open-world Classification. arXiv:1801.05609 [cs.LG], 2018.
- Lei Shu, Hu Xu, Bing Liu. DOC: Deep Open Classification of Text Documents. Proceedings of 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP-2017, oral presentation short paper), September 7–11, 2017, Copenhagen, Denmark.
- Geli Fei, Shuai Wang, and Bing Liu. 2016. Learning Cumulatively to Become More Knowledgeable. Proceedings of SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2016), August 13-17, San Francisco, USA.
- Geli Fei, and Bing Liu. 2016. Breaking the Closed World Assumption in Text Classification. Proceedings of NAACL-HLT 2016 , June 12-17, San Diego, USA.
Created on July 15, 2020 by Bing Liu.