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
- 7.5.2020 Paper discussion (who?) A Meta-Analysis of Overfitting in Machine Learning, NeurIPS 2019.
- 14.5.2020 Paper discusssion (who?) Model Similarity Mitigates Test Set Overuse, NeurIPS 2019.
- 21.5.2020 Public holiday
- 28.5.2020 tba
- 4.6.2020 No meeting because of Neurips deadline
- 11.6.2020 Public holiday
- 18.6.2020 (Sebastian) Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles Dylan J. Foster, Alexander Rakhlin
- 25.6.2020 tba
- In the queue: ? On the Utility of Learning about Humans for Human-AI Coordination, NeurIPS 2019.
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;
- Envy-free classification, NeurIPS 2019.
- Random deep neural networks are biased towards simple functions, NeurIPS 2019.
- Making AI Forget You: Data Deletion in Machine Learning, NeurIPS 2019.
- Landmark Ordinal Embedding, 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
