EECS260 Optimization (Fall semester 2023): project 3

Project 3 consists of participating in class discussions of several papers that apply optimisation algorithms to specific problems. You should read each paper ahead of time and be prepared to ask questions (or to answer them). The first thing to understand is the optimisation problem, i.e., what the variables are and what the objective function is (and why it makes sense to formulate the problem in that way). Example questions (not all may apply): is the problem constrained/unconstrained/smooth/line-search/trust-region...? If it uses a line search, does it satisfy the Wolfe (or other) conditions? Does the paper use a standard optimisation algorithm, or are there ad-hoc modifications, and if so what are the consequences? Are there better choices of algorithm (take into account ease of implementation, scalability wrt problem size, etc.)? Is the empirical evaluation fair and conclusive (competing methods are often presented at a disadvantage)? What is the stopping condition or convergence criterion? Does the problem have a unique optimum? How important is the optimisation part in the paper's main objectives? Be critical. 🤮

• Oct. 23. Presenter: Magzhan Gabidolla.

  • Gabidolla, M. and Carreira-Perpiñán, M. Á.: "Optimal interpretable clustering using oblique decision trees". KDD 2022.

• Nov. 2. Presenter: Arman Zharmagambetov.

  • Zharmagambetov, A. and Carreira-Perpiñán, M. Á.: "Semi-supervised learning with decision trees: graph Laplacian tree alternating optimization". NeurIPS 2022.
  • Zharmagambetov, A. and Carreira-Perpiñán, M. Á.: "Learning interpretable, tree-based projection mappings for nonlinear embeddings". AISTATS 2022.

• Nov. 9. Presenter: Suryabhan Singh Hada.

  • Carreira-Perpiñán, M. Á. and Hada, S. S.: "Inverse classification with logistic and softmax classifiers: efficient optimization". arXiv:2309.08945.
  • Carreira-Perpiñán, M. Á. and Hada, S. S.: "Counterfactual explanations for oblique decision trees: exact, efficient algorithms". AAAI 2021 and arXiv:2103.01096.
  • Carreira-Perpiñán, M. Á. and Hada, S. S.: "Very fast, approximate counterfactual explanations for decision forests". AAAI 2023 and arXiv:2303.02883.

• Nov. 16. Presenters: Rasul Kairgeldin, Kuat Gazizov

  • Schölkopf, B. et al.: "Estimating the support of a high-dimensional distribution". Neural Computation 2001 and MSR-TR-99-87.
  • Bottou, L. Curtis, F. E. and Nocedal, J.: "Optimization methods for large-scale machine learning" (sections 3-4). SIAM Review 2018 and arXiv:1606.04838.

• Nov. 30. Presenters: Kyle Wright, Jieke Wang, Fang Chen

  • Defazio, A. and Mishchenko, K.: "Learning-rate-free learning by D-adaptation". arXiv:2301.07733
  • Khan, M. E. and Rue, H.: "The Bayesian Learning Rule". JMLR 2023.

• Dec. 6. Presenters: Irabiel Romero Ruiz, Zhiyu An

  • Watkins, C.J.C.H. and Dayan, P.: Q-learning. Machine Learning 1992.

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