Personal Development

• The God Delusion by Richard Dawkins
• The Subtle Art of Not Giving a F*ck by Mark Manson
• Atomic Habits by James Clear
• Deep Work by Cal Newport
• The Power of Small by Aisling Leonard-Curtin and Dr. Trish Leonard-Curtin
• Godel, Escher, Bach by Douglas R. Hofstadter

Accessibility

• Interaction Design Foundation - Accessibility
• W3C - Web Standards

Museum Studies

• Museum Learning Hub
• Annual Meeting & MuseumEXPO
• Digital Museums Canadas

ML Theory

• The Nature of Statistical Learning Theory
• An Introduction to Computational Learning Theory
• The Elements of Statistical Learning
• Understanding Machine Learning: From Theory to Algorithms

General ML

• Data imputation using autoencoders
• Model Reduction for Nonlinear Systems by Balanced Truncation of State and Gradient Covariance
• Liquid State Machine
• Model-Free prediction of large spatiotemporally chaotic systems from data: A reservoir approach
• Principal component analysis in linear systems: Controllability, observability, and model reduction
• Statistical inference and machine learning
• A Theoretically Grounded Question Answering Data Set for Evaluating Machine Common Sense

ACO/Swarm Intelligence

• ACO-Pants

AML Papers

• S. Caton, S. Malisetty, and C. Haas. Impact of Imputation Strategies on Fairness in Machine Learning. Journal of Artificial Intelligence Research 74 (2022) 1011–1035.
• J. Wang, L. Hopkins, T. Hallman, W. Robinson, and R. Hutchinson. Cross-validation for Geospatial Data: Estimating Generalization Performance in Geostatistical Problems. Transactions on Machine Learning Re- search, 09/2023, 1–40.
• A. Turner and A. Kabán. PAC-Learning with Approximate Predictors. Machine Learning (2023) 112:1693–1732.
• S. Chen, C. Gong, X. Li, J. Yang, G. Niu, and M. Sugiyama. Boundary-Restricted Metric Learning. Machine Learning (2023) 112:4723–4762.
• N. Grüttemeier and C. Komusiewicz. Learning Bayesian Networks Under Sparsity Constraints: A Parameterized Complexity Analysis. Journal of Artificial Intelligence Research 74 (2022) 1225–1267.
• N. Durrani, F. Dalvi, and H. Sajjad. Discovering Salient Neurons in Deep NLP Models. Journal of Machine Learning Research 24 (2023) 1–40.
• M. Agarwal and V. Aggarwal. Reinforcement Learning for Joint Optimization of Multiple Rewards. Journal of Machine Learning Research 24 (2023) 1–41.
• T. Nguyen, W. Browne, and M. Zhang. ConCS: A Continual Classifier System for Continual Learning of Multiple Boolean Problems. IEEE Transactions on Evolutionary Computation, Vol. 27. No. 4, August 2023, 1057–1071.
• D. Aineto, S. Jiménez, and E. Onainda. A Comprehensive Framework for Learning Declarative Action Models. Journal of Artificial Intelligence Research 74 (2022) 1091–1123.
• J. Raymaekers and R. Zamar. Regularized K-means Through Hard-Thresholding. Journal of Machine Learning Research 23 (2022) 1–48.
• S. Zhou. Sparse SVM for Sufficient Data Reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, No. 9, September 2022, 5560–5571.
• Y. Zhou and G. Hooker. Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution. Journal of Machine Learning Research 23 (2022) 1–44.