The Machine Learning Reading Group (MLRG) aims to enhance 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 the 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 (, 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.


Date Presenter Paper
24/01/2024 Siu Lun Chau Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations
29/11/2023 Jake Fawkes Why does Throwing Away Data Improve Worst-Group Error?
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