Seminar on Machine Teaching
Saarland University — Winter Semester 2019
This course is offered as a block seminar. The course will provide an overview of machine teaching and cover the following three aspects: (i) how machine teaching formulation differs from machine learning, (ii) the problem space of machine teaching, and (iii) recent work on developing teaching algorithms for human learners.

Organizers

Schedule

  • Introductory lectures: We will have two introductory lectures on 4th and 5th November at 16:00 — 18:00 in MPI-SWS building E1.5, Room 029. Attendance is mandatory. [Slides#1] [Slides#2]
  • Research papers: We have assigned a total of 8 research papers to read; list is provided below. You will have to write a two-page report for each paper (one paper per week). Reports for the first 4 papers will be due by 10th December 2019, and reports for the remaining 4 papers will be due by 10th January 2020. A template will be provided in the introductory lecture.
  • Slides preparation: You will have to prepare a presentation of 15 mins for one of the 8 research papers. Each student will be assigned one of the papers to present and this assignment will be done on 11th January 2020. Your final slides will be due by 15th February 2020. You will have a possibility to get feedback on your slides before final submission.
  • Final presentations: At the end of the semester, you will give a final presentation. We will block about 6 hours for the presentations sometime between mid-February to mid-March 2020. The exact dates will be finalized in discussion with enrolled students. Attendance to the final presentations will be mandatory.

List of research papers

  1. On the Complexity of Teaching
    S. Goldman, M. Kearns
    In Proc. of the 4th Conference on Computational Learning Theory (COLT'91), 1991
  2. Deep Knowledge Tracing
    C. Piech, J. Bassen, J. Huang, S. Ganguli, M. Sahami, L. Guibas, J. Sohl-Dickstein
    In Proc. of the 29th Conference on Neural Information Processing Systems (NeurIPS'15), 2015
  3. Learning to Represent Student Knowledge on Programming Exercises Using Deep Learning
    L. Wang, A. Sy, L. Liu, C. Piech
    In Proc. of the 10th International Conference on Educational Data Mining (EDM'17), 2017
  4. Near-Optimally Teaching the Crowd to Classify
    A. Singla, I. Bogunovic, G. Bartok, A. Karbasi, A. Krause
    In Proc. of the 31st International Conference on Machine Learning (ICML), 2014
  5. Teaching Multiple Concepts to a Forgetful Learner
    A. Hunziker, Y. Chen, O. Mac Aodha, M. Gomez-Rodriguez, A. Krause, P. Perona, Y. Yue, A. Singla
    In Proc. of the 33rd Conference on Neural Information Processing Systems (NeurIPS'19), 2019
  6. Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
    S. Tschiatschek, A. Ghosh, L. Haug, R. Devidze, A. Singla
    In Proc. of the 33rd Conference on Neural Information Processing Systems (NeurIPS'19), 2019
  7. Learning to Teach in Cooperative Multiagent Reinforcement Learning
    S. Omidshafiei, D. Kim, M. Liu, G. Tesauro, M. Riemer, C. Amato, M. Campbell, J. How
    In Proc. of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), 2019
  8. Policy Poisoning in Batch Reinforcement Learning and Control
    Y. Ma, X. Zhang, W. Sun, X. Zhu
    In Proc. of the 33rd Conference on Neural Information Processing Systems (NeurIPS'19), 2019


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