Please note that lifelong learning has close ties to transfer learning, multi-task learning, curriculum learning, and many other areas.  Due to the extent of these other topics, we omit them from this listing, and instead focus solely on works related to lifelong learning.  For a survey of transfer learning methods, see:

• A Survey on Transfer Learning. Sinno Jialin Pan and Qiang Yang. 2010. 
• Transfer Learning for Reinforcement Learning Domains: A Survey. Matthew E. Taylor and Peter Stone. 2009.  

Books

• Lifelong Machine Learning by Zhiyuan Chen and Bing Liu, Morgan & Claypool Publishers, November 2016.
  • Related tutorial slides on lifelong machine learning [KDD-2016, IJCAI-2015]

Surveys

• Lifelong Machine Learning Systems: Beyond Learning Algorithms. Daniel L. Silver, Qiang Yang, and Lianghao Li, 2013.
  • Surveys some of the key issues in lifelong learning, covering techniques through early 2013.  For a more up-to-date treatment of the issues, see either Chen and Liu’s book and tutorials, or the papers listed below.

Dissertations and Theses

• Explanation-Based Neural Network Learning: A Lifelong Learning Approach by Sebastian Thrun. Kluwer Academic Publishers, Boston, MA, 1996.  
• Continual Learning in Reinforcement Environments by Mark Ring. The University of Texas at Austin,1994.

Lifelong Learning for Classification and Regression

• Is Learning The n-th Thing Any Easier Than Learning The First? Sebastian Thrun. NIPS 1996.
• Discovering structure in multiple learning tasks: The TC algorithm. Sebastian Thrun and Joseph O’Sullivan. ICML 1996.
• ELLA: An Efficient Lifelong Learning Algorithm. Paul Ruvolo & Eric Eaton. ICML 2013. 
• Active Task Selection for Lifelong Machine Learning. Paul Ruvolo & Eric Eaton.  AAAI 2013.
• A PAC-Bayesian Bound for Lifelong Learning. Anastasia Pentina & Christoph H. Lampert. ICML 2014
• Lifelong Learning with Non-i.i.d. Tasks. Anastasia Pentina & Christoph H. Lampert. NIPS 2015.
• Online Boosting Algorithms for Anytime Transfer and Multitask Learning. Boyu Wang & Joelle Pineau. AAAI 2015.
• Lifelong Learning for Sentiment Classification (Short Paper). Zhiyuan Chen, Nianzu Ma & Bing Liu. ACL 2015.
• Lifelong Learning with Weighted Majority Votes. Anastasia Pentina & Ruth Urner. NIPS 2016.
• Generalized Dictionary for Multitask Learning with Boosting. Boyu Wang & Joelle Pineau. IJCAI 2016.
• Learning Cumulatively to Become More Knowledgeable. Geli Fei, Shuai Wang & Bing Liu. KDD 2016.

Lifelong Reinforcement Learning

• CHILD: A First Step Towards Continual Learning. Mark Ring. Machine Learning Journal, volume 28, 1997.
• An Approach to Lifelong Reinforcement Learning through Multiple Environments. Fumihide Tanaka and Masayuki Yamamura. European Workshop on Learning Robots 1997.
• Multi-Task Reinforcement Learning: A Hierarchical Bayesian Approach. Aaron Wilson, Alan Fern, Soumya Ray, Prasad Tadepalli. ICML 2007.
• Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction. Richard S. Sutton, Joseph Modayil, Michael Delp, Thomas Degris, Patrick M. Pilarski, Adam White, & Doina Precup. AAMAS 2011.
• Multi-timescale Nexting in a Reinforcement Learning Robot. Josepth Modayil, Adam White, & Richard S. Sutton. Adaptive Behavior 22(2):146-160, 2014.
• Online Multi-Task Learning for Policy Gradient Methods. Haitham Bou Ammar, Eric Eaton, Paul Ruvolo, & Matthew E. Taylor. ICML 2014. 
• Safe policy search for lifelong reinforcement learning with sublinear regret. Haitham Bou Ammar, Rasul Tutunov, & Eric Eaton.  ICML 2015. 
• Autonomous cross-domain knowledge transfer in lifelong policy gradient reinforcement learning. Haitham Bou Ammar, Eric Eaton, Jose Marcio Luna, & Paul Ruvolo. IJCAI 2015. [Nominated for Distinguished Paper award] 

Few-Shot Transfer in Lifelong Learning

• Using task features for zero-shot knowledge transfer in lifelong learning. David Isele, Mohammad Rostami, & Eric Eaton. IJCAI 2016. [Best student paper award] 

Deep Lifelong Learning

• A Deep Hierarchical Approach to Lifelong Learning in Minecraft. Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J. Mankowitz, & Shie Mannor. arXiv 2016.
• Progressive Neural Networks. Andrei A. Rusu, Neil C. Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, Raia Hadsell. arXiv 2016.
• Overcoming catastrophic forgetting in neural networks. James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, Raia Hadsell. arXiv 2016.

Lifelong Learning for User Modeling and Crowdsourcing

• Principles of Lifelong Learning for Predictive User Modeling. Ashish Kapoor & Eric Horvitz. International Conference on User Modeling 2007.
• Lifelong Learning for Acquiring the Wisdom of the Crowd. Ece Kamar, Ashish Kapoor, & Eric Horvitz. IJCAI 2013.

Lifelong Robot Learning

• Lifelong robot learning. Sebastian Thrun and Tom M. Mitchell.  Robotics and Autonomous Systems, 15:25-46, 1995.
• A lifelong learning perspective for mobile robot control. Sebastian Thrun. In IEEE/RSJ/GI Conference on Intelligent Robots and Systems, pages 23-30, 1994.  
• Lifelong learning algorithms. Sebastian Thrun. In S. Thrun and L.Y. Pratt, editors, Learning To Learn. Kluwer Academic Publishers, 1998.
• Lifelong Learning for Disturbance Rejection on Mobile Robots. David Isele, Jose Marcio Luna, Eric Eaton, Gabriel V. de la Cruz, James Irwin, Brandon Kallaher, & Matthew E. Taylor.  IROS 2016.  

Lifelong Learning of Structured Information

• Toward an Architecture for Never-Ending Language Learning. A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E.R. Hruschka Jr. and T.M. Mitchell. AAAI 2010.
• Never-Ending Learning. T. Mitchell, W. Cohen, E. Hruschka, P. Talukdar, J. Betteridge, A. Carlson, B. Dalvi, M. Gardner, B. Kisiel, J. Krishnamurthy, N. Lao, K. Mazaitis, T. Mohamed, N. Nakashole, E. Platanios, A. Ritter, M. Samadi, B. Settles, R. Wang, D. Wijaya, A. Gupta, X. Chen, A. Saparov, M. Greaves, J. Welling. AAAI 2015. 
• Mining Topics in Documents: Standing on the Shoulders of Big Data. Zhiyuan Chen & Bing Liu.  KDD 2014.  
• Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data. Zhiyuan Chen & Bing Liu. ICML 2014.  
• Mining Aspect-Specific Opinion using a Holistic Lifelong Topic Model. Shuai Wang, Zhiyuan Chen, & Bing Liu. 2016. WWW 2016.

Testbeds and Data Sets

• DeepMind Lab.  Charles Beattie, Joel Z. Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, Julian Schrittwieser, Keith Anderson, Sarah York, Max Cant, Adam Cain, Adrian Bolton, Stephen Gaffney, Helen King, Demis Hassabis, Shane Legg, & Stig Petersen.  arXiv 2016. 
• OpenAI Gym