Research Seminar "Machine Learning Theory"
This is the research seminar by Ulrike's group.When and where
Each thursday 14:00 - 15:00. Until further notice, all meetings take place virtually!What
Most sessions take place in form of a reading group: everybody reads the assigned paper before the meeting. Then we jointly discuss the paper in the meeting. Sometimes we also have talks by guests or members of the group.Who
Everybody who is interested in machine learning theory. Students, PhD students and researchers alike. We do not mind people dropping in and out depending on whether they find the current session interesting or not.Upcoming meetings
- 30.7.2020 Master Defense by Solveig: Tangles in Machine Learning.
- 7.8. No meeting
- 13.8.2020 BSc Defense by Margaretha: Is ordinal embedding NP hard?
- August / September: depending on who is there, we might or might not meet, we will decide spontaneously. Resuming regular schedule by the end of September.
Past meetings
Listed here.Suggested papers for future meetings
Feel free to make suggestions!If you do, please (i) try to select short conference papers rather than 40-page-journal papers; (ii) please put your name when entering suggestions; it does not mean that you need to present it, but then we can judge where it comes from;
- On the Utility of Learning about Humans for Human-AI Coordination, NeurIPS 2019.
- Paper discussion (David): Landmark Ordinal Embedding, NeurIPS 2019.
- Paper discussion (Sascha): The Power of Batching in Multiple Hypothesis Testing, Tijana Zrnic, Daniel Jiang, Aaditya Ramdas, Michael Jordan; PLMR, 2020.
- Auditing ML Models for Individual Bias and Unfairness Songkai Xue, Mikhail Yurochkin, Yuekai Sun ; PMLR 108:4552-4562 http://proceedings.mlr.press/v108/xue20a/xue20a.pdf
- Fast and Bayes-consistent nearest neighbors Klim Efremenko, Aryeh Kontorovich, Moshe Noivirt ; Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:1276-1286, 2020. http://proceedings.mlr.press/v108/efremenko20a/efremenko20a.pdf
- Envy-free classification, NeurIPS 2019.
- Random deep neural networks are biased towards simple functions, NeurIPS 2019.
- On the Fairness of Disentangled Representations, NeurIPS 2019.
- (Leena)To Understand Deep Learning We Need to Understand Kernel Learning Mikhail Belkin, Siyuan Ma, Soumik Mandal, ICML 2018
- (Leena) Neural Tangent Kernel:Convergence and Generalization in Neural Networks Arthur Jacot, Franck Gabriel, Clement Hongler, NeurIPS 2018
- Rademacher Complexity for Adversarially Robust Generalization ICML 2019
- (Luca) Relational inductive biases, deep learning, and graph networks arXiv 2018
- (Michael) Two-Player Games for Efficient Non-Convex Constrained Optimization ALT 2019
- (Michael) Balancing Competing Objectives with Noisy Data - Score-Based Classifier for Welfare-Aware Machine Learning ICML 2020
- (Michael) Minimax Pareto Fairness: A Multi Objective Perspective ICML 2020
