The Machine Learning Reading Group (MLRG) aims at enhancing our understanding in broad areas of machine learning (ML), incubating innovative ideas, and fostering cross-domain research collaborations. The topics of interest include, but are not limited to, learning theory, out-of-distribution (OOD) generalization, robustness, causal inference, game-theoretic learning, collaborative/cooperative learning, economics, etc.
Update: From 01 November 2023 onwards, the MLRG will be cohosted with the Machine Learning Group from University of Saarland. Further details can be found here.
- Date/Time: Bi-weekly on Wednesday, 14:30pm-16:00pm
- Location: Alternating between CISPA building and MPI building. You can also join virtually.
- Organiser: Siu Lun Chau (siu-lun.chau-at-cispa.de), Huynh Quang Kiet Vo ([email protected])
Each session is led by a presenter(s) who presents a paper of their choice and leads the discussion. The participants are encouraged to read the paper beforehand. To stay informed, please sign up to our mailing list.
|31/10/2023||Masha Naslidnyk||MONK – Outlier-Robust Mean Embedding Estimation by Median-of-Means|
|10/10/2023||Muneeb Aadil||Model-based Causal Bayesian Optimization|
|13/09/2023||Siu Lun Chau||Imprecise Bayesian Neural Networks|
|11/07/2023||Advait Gadhikar||Why Random Pruning Is All We Need to Start Sparse|
|27/06/2023||Anurag Singh||Probable Domain Generalization via Quantile Risk Minimization|
|13/06/2023||Huynh Quang Kiet Vo||Information Discrepancy in Strategic Learning|
|25/04/2023||Siu Lun Chau||Learning Choice Functions with Gaussian Processes|
|11/04/2023||Krikamol Muandet||Multi-Task Learning as a Bargaining Game|
|28/03/2023||Simon Föll||You Only Train Once: Loss-conditional Training On Deep Networks|
|14/03/2023||Siu Lun Chau||Fairness Risk Measures|