This page lists publications that appeared after September 2022. Prior publications can be found at https://www.krikamol.org/publication/.


Causal Strategic Learning with Competitive Selection
Kiet Q. H. Vo, Muneeb Aadil, Siu Lun Chau, Krikamol Muandet
The AAAI Conference on Artificial Intelligence (AAAI), 2024 - Oral Presentation


Domain Generalisation via Imprecise Learning
Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet,
International Conference on Machine Learning (ICML), 2024 - Spotlight


Looping in the Human: Collaborative and Explainable Bayesian Optimization
Masaki Adachi, Brady Planden, David A. Howey, Michael A. Osborne, Sebastian Orbell, Natalia Ares, Krikamol Maundet, Siu Lun Chau
The International Conference on Artificial Intelligence and Statistics (AISTATS), 2024


Instrumental Variable Regression via Kernel Maximum Moment Loss
Rui Zhang, Masaaki Imaizumi, Bernhard Schölkopf and Krikamol Muandet
Journal of Causal Inference, 2023


Fast Adaptive Test-Time Defense with Robust Features
Anurag Singh, Mahalakshmi Sabanayagam, Krikamol Muandet, Debarghya Ghoshdastidar
Preprint, 2023


Towards Empirical Process Theory for Vector-Valued Functions: Metric Entropy of Smooth Function Classes
Junhyung Park and Krikamol Muandet
Algorithmic Learning Theory (ALT), 2023


A Measure-Theoretic Axiomatisation of Causality
Junhyung Park, Simon Buchholz, Bernhard Schölkopf, Krikamol Muandet
Neural Information Processing Systems (NeurIPS), 2023 - Oral Presentation


(Im)possibility of Collective Intelligence
Krikamol Muandet
Preprint, 2023


On the Relationship Between Explanation and Prediction: A Causal View
Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim
International Conference on Machine Learning (ICML), 2023


Gated Domain Units for Multi-source Domain Generalization
Simon Föll*, Alina Dubatovka*, Eugen Ernst†, Siu Lun Chau†, Martin Maritsch, Patrik Okanovic, Gudrun Thäter, Joachim M Buhmann, Felix Wortmann, Krikamol Muandet (*,† equal contributions)
Transactions on Machine Learning Research (TMLR), 2023


Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models
Siu Lun Chau, Krikamol Muandet*, Dino Sejdinovic* (* equal contribution)
Neural Information Processing Systems (NeurIPS), 2023 - Spotlight


Learning Counterfactually Invariant Predictors
Francesco Quinzan, Cecilia Casolo, Krikamol Muandet, Yucen Luo, Niki Kilbertus
Preprint, 2022