Advanced Topics in Machine Learning, Caltech: http://www.yisongyue.com/courses/cs159/, taught by Yisong Yue
Mining from Large Data Sets, ETHZ: https://las.inf.ethz.ch/teaching/dm-f17, taught by Andreas Krause
(Course Notes on Online Learning) Online Learning, by Gabor Bartok, David Pal, Csaba Szepesvari, and Istvan Szita.
Bandit algorithms, by Tor Lattimore and Csaba Szepesvari, 2020: https://tor-lattimore.com/downloads/book/book.pdf
Active Learning, (accessible via UChicago IPs), by Burr Settles, 2012
Reinforcement Learning: An Introduction (Barto & Sutton): http://incompleteideas.net/book/bookdraft2018jan1.pdf
Algorithms for Reinforcement Learning, by Csaba Szepesvári, 2010
CMSC 25300/35300: Mathematical Foundations of Machine Learning, taught by Rebecca Willett
CMSC 35300: Machine Learning, UChicago: https://voices.uchicago.edu/machinelearning/stats37710-cmsc35400-s20/, co-taught by Rebecca Willett and Yuxin Chen
More lists of resources (RL):
Online learning: Online Learning and Online Convex Optimization, by Shai Shalev-Shwartz. Foundations and Trends in Machine Learning, 4(11), 107-194, 2011.
Bandits: Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems, by Sébastien Bubeck, Nicolò Cesa-Bianchi.
Bayesian optimization: Taking the Human Out of the Loop: A Review of Bayesian Optimization, by Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan Adams, and Nando de Freitas. Proceedings of the IEEE, 104(1), 2016.
Active learning: Active Learning Literature Survey, by Burr Settles.
Reinforcement learning: Bayesian Reinforcement Learning: A Survey, by Mohammad Ghavamzadeh, Shie Mannor, Joelle Pineau, and Aviv Tamar. Foundations and Trends in Machine Learning, 8(5-6), 359-483, 2015.
Machine teaching: An Overview of Machine Teaching, by Xiaojin Zhu, Adish Singla, Sandra Zilles, Anna N. Rafferty. ArXiv 1801.05927, 2018.
Active Learning for Level Set Estimation. A Gotovos, N Casati, G Hitz, A Krause - ijcai.org
Bayesian Optimal Active Search and Surveying. R Garnett, Y Krishnamurthy, X Xiong, J Schneider et al. ICML 2012
Efficient Nonmyopic Active Search. Shali Jiang, Gustavo Malkomes, Geoff Converse, Alyssa Shofner, Benjamin Moseley, Roman Garnett ; Proceedings of the 34th International Conference on Machine Learning, PMLR 70:1714-1723, 2017.
Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments. Yi Sun, Faustino Gomez, Jürgen Schmidhuber. International Conference on Artificial General Intelligence (AGI) 2011.
Latent Structured Active Learning, by Wenjie Luo Alex Schwing Raquel Urtasun. Advances in Neural Information Processing Systems, 2013.
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks, by Julius von Kügelgen, Paul K Rubenstein, Bernhard Schölkopf, Adrian Weller. NeurIPS 2019 Workshop “Do the right thing”: machine learning and causal inference for improved decision making, December 2019
Active Imitation Learning: Formal and Practical Reductions to I.I.D. Learning, by Kshitij Judah, Alan Fern, Tom Dietterich, Prasad Tadepalli. Journal of Machine Learning Research, 15, 4105-4143, 2015.
Interactive Clustering: A Comprehensive Review. Bae et al., ACM Computing Surveys, 2020.
Interactive Bayesian hierarchical clustering. S Vikram, S Dasgupta. International Conference on Machine Learning, 2016
(Streaming algorithms) Do Less, Get More: Streaming Submodular Maximization with Subsampling, by Moran Feldman, Amin Karbasi, Ehsan Kazemi. Advances in Neural Information Processing Systems 31 (NIPS 2018)
Streaming Submodular Maximization: Massive Data Summarization on the Fly, by Ashwinkumar Badanidiyuru, Baharan Mirzasoleiman, Amin Karbasi, Andreas Krause. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, August 2014.
Practical Bayesian Optimization of Machine Learning Algorithms, by Jasper Snoek, Hugo Larochelle, and Ryan Adams. Neural Information Processing Systems, 2012.
Scalable Bayesian Optimization Using Deep Neural Networks, by Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostafa Ali Patwary, Prabhat, Ryan Adams. International Conference on Machine Learning, 2015.
Bayesian Multi-Scale Optimistic Optimization, by Ziyu Wang, Babak Shakibi, Lin Jin, Nando de Freitas. International Conference on Artificial Intelligence and Statistics, 2014.
Learning to search in branch and bound algorithms, He, H., Daume III, H., & Eisner, J. M. (2014). In Advances in neural information processing systems (pp. 3293-3301).
Co-training for Policy Learning. Song, J., Lanka, R., Yue, Y., & Ono, M. (2019). In UAI 2019.
Reinforcement learning for integer programming: Learning to cut. Tang, Y., Agrawal, S., & Faenza, Y. (2019). arXiv preprint arXiv:1906.04859.
Learning combinatorial optimization algorithms over graphs. Khalil, E., Dai, H., Zhang, Y., Dilkina, B., & Song, L. (2017). In Advances in Neural Information Processing Systems (pp. 6348-6358).
Neural Architecture Search with Reinforcement Learning. Li & Le. (2017). In ICLR 2017.
Neural Architecture Search: A Survey. Elsken, Metzen, Hutter. (2018).
DARTS: Differentiable Architecture Search. Liu, Simonyan, Yang. (2019). In ICLR 2019.
Safe Exploration for Optimization with Gaussian Processes. Yanan Sui, Alkis Gotovos, Joel Burdick, Andreas Krause. International Conference on Machine Learning (ICML), 2015
Safe Exploration for Interactive Machine Learning. Matteo Turchetta, Felix Berkenkamp, Andreas Krause. In Neural Information Processing Systems (NeurIPS), 2019
Safe Reinforcement Learning in Constrained Markov Decision Processes. Akifumi Wachi, Yanan Sui. International Conference on Machine Learning (ICML), 2020
Teaching with Commentaries. Aniruddh Raghu, Maithra Raghu, Simon Kornblith, David Duvenaud, Geoffrey Hinton. arXiv:2011.03037 [cs.LG].
Learning to Read through Machine Teaching. Ayon Sen, Christopher R. Cox, Matthew Cooper Borkenhagen, Mark S. Seidenberg, Xiaojin Zhu. arXiv:2006.16470 [cs.LG]
Towards a rigorous science of interpretable machine learning. F Doshi-Velez, B Kim - arXiv preprint arXiv:1702.08608, 2017.
Human evaluation of models built for interpretability. I Lage, E Chen, J He, M Narayanan, B Kim, et al. HCOMP, 2019.
The Mythos of Model Interpretability. ZC Lipton - Queue, 2018.
A survey of methods for explaining black box models. R Guidotti, A Monreale, S Ruggieri et al. ACM computing surveys, 2018.
Multi-agent Reinforcement Learning in Sequential Social Dilemmas, by J.Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel, at AAMAS 2017.
Teaching on a Budget: Agents Advising Agents in Reinforcement Learning, by Torrey and Taylor, at AAMAS 2013.
Learning to Teach in Cooperative Multiagent Reinforcement Learning, by S. Omidshafiei et al., at AAAI 2019.
Machine Theory of Mind, by N. C. Rabinowitz et al., at ICML 2018.
Multi-view Decision Processes: The Helper-AI Problem, by C. Dimitrakakis, D.C. Parkes, G. Radanovic, and P. Tylkin, at NeurIPS 2017.
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints, by S. Tschiatschek, A. Ghosh, L. Haug, R. Devidze, and A. Singla, at NeurIPS 2019.
Reactive Learning: Actively Trading Off Larger Noisier Training Sets Against Smaller Cleaner Ones. C.H. Lin, Mausam, D.S. Weld, ICML Workshop on Crowdsourcing and Machine Learning and ICML Active Learning Workshop, July 2015.
Learning on the Job: Optimal Instruction for Crowdsourcing. J. Bragg, Mausam, D.S. Weld, ICML ’15 Workshop on Crowdsourcing and Machine Learning, July 2015.
Beyond accuracy: The role of mental models in human-AI team performance, G Bansal, B Nushi, E Kamar, et al. HCOMP 2019
Motivating Novice Crowd Workers through Goal Setting: An Investigation into the Effects on Complex Crowdsourcing Task Training. Amy Rechkemmer and Ming Yin. The 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), Hilversum, Netherlands, October 2020.