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
PDF Code Poster


Robust Feature Inference: A Test-time Defense Strategy using Spectral Projections
Anurag Singh, Mahalakshmi Sabanayagam, Krikamol Muandet, Debarghya Ghoshdastidar
Transactions on Machine Learning Research (TMLR), 2024
PDF


Highly Parallel Optimisation of Nickel-Catalysed Suzuki Reactions through Automation and Machine Intelligence
Joshua W. Sin, Siu Lun Chau, Ryan P. Burwood, Kurt Püntener, Raphael Bigler, Philippe Schwaller
ChemRxiv, 2024
DOI PDF


Domain Generalisation via Imprecise Learning
Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet,
International Conference on Machine Learning (ICML), 2024 - Spotlight
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Learning Counterfactually Invariant Predictors
Francesco Quinzan, Cecilia Casolo, Krikamol Muandet, Yucen Luo, Niki Kilbertus
Transactions on Machine Learning Research (TMLR), 2024
PDF


Sufficient Invariant Learning for Distribution Shift
Taero Kim, Subeen Park, Sungjun Lim, Yonghan Jung, Krikamol Muandet, Kyungwoo Song
Preprint, 2024
DOI PDF


Credal Two-sample Tests of Epistemic Ignorance
Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet
Preprint, 2024
DOI PDF


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
DOI PDF Poster


Social Bayesian Optimization for Building Truthful Consensus
Masaki Adachi, Siu Lun Chau, Wenjie Xu, Anurag Singh, Michael A Osborne, Krikamol Muandet
Preprint, 2024


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


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


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


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


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
DOI PDF Poster


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
PDF Code


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
DOI PDF Code Poster