Muandet, Krikamol

44 publications

NeurIPS 2025 An Analysis of Causal Effect Estimation Using Outcome Invariant Data Augmentation Uzair Akbar, Niki Kilbertus, Hao Shen, Krikamol Muandet, Bo Dai

AISTATS 2025 Credal Two-Sample Tests of Epistemic Uncertainty Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet

NeurIPS 2025 Integral Imprecise Probability Metrics Siu Lun Chau, Michele Caprio, Krikamol Muandet

ICML 2025 Kernel Quantile Embeddings and Associated Probability Metrics Masha Naslidnyk, Siu Lun Chau, Francois-Xavier Briol, Krikamol Muandet

CVPR 2025 Sufficient Invariant Learning for Distribution Shift Taero Kim, Subeen Park, Sungjun Lim, Yonghan Jung, Krikamol Muandet, Kyungwoo Song

UAI 2025 Truthful Elicitation of Imprecise Forecasts Anurag Singh, Siu Lun Chau, Krikamol Muandet

AAAI 2024 Causal Strategic Learning with Competitive Selection Kiet Q. H. Vo, Muneeb Aadil, Siu Lun Chau, Krikamol Muandet

ICML 2024 Domain Generalisation via Imprecise Learning Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet

TMLR 2024 Learning Counterfactually Invariant Predictors Francesco Quinzan, Cecilia Casolo, Krikamol Muandet, Yucen Luo, Niki Kilbertus

AISTATS 2024 Looping in the Human: Collaborative and Explainable Bayesian Optimization Masaki Adachi, Brady Planden, David Howey, Michael A. Osborne, Sebastian Orbell, Natalia Ares, Krikamol Muandet, Siu Lun Chau

TMLR 2024 Robust Feature Inference: A Test-Time Defense Strategy Using Spectral Projections Anurag Singh, Mahalakshmi Sabanayagam, Krikamol Muandet, Debarghya Ghoshdastidar

NeurIPS 2023 A Measure-Theoretic Axiomatisation of Causality Junhyung Park, Simon Buchholz, Bernhard Schölkopf, Krikamol Muandet

NeurIPS 2023 Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic

TMLR 2023 Gated Domain Units for Multi-Source Domain Generalization Simon Föll, Alina Dubatovka, Eugen Ernst, Siu Lun Chau, Martin Maritsch, Patrik Okanovic, Gudrun Thaeter, Joachim M. Buhmann, Felix Wortmann, Krikamol Muandet

ICLRW 2023 Impossibility of Collective Intelligence Krikamol Muandet

ICML 2023 On the Relationship Between Explanation and Prediction: A Causal View Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim

NeurIPSW 2023 On the Relationship Between Explanation and Prediction: A Causal View Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim

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

AISTATS 2022 A Witness Two-Sample Test Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet

NeurIPS 2022 AutoML Two-Sample Test Jonas M. Kübler, Vincent Stimper, Simon Buchholz, Krikamol Muandet, Bernhard Schölkopf

ICML 2022 Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf

ICML 2021 Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet

JMLR 2021 Counterfactual Mean Embeddings Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, Sanparith Marukatat

ICML 2021 Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt Kusner, Arthur Gretton, Krikamol Muandet

NeurIPS 2020 A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings Junhyung Park, Krikamol Muandet

NeurIPS 2020 Dual Instrumental Variable Regression Krikamol Muandet, Arash Mehrjou, Si Kai Lee, Anant Raj

AISTATS 2020 Fair Decisions Despite Imperfect Predictions Niki Kilbertus, Manuel Gomez Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera

AISTATS 2020 Kernel Conditional Density Operators Ingmar Schuster, Mattes Mollenhauer, Stefan Klus, Krikamol Muandet

UAI 2020 Kernel Conditional Moment Test via Maximum Moment Restriction Krikamol Muandet, Wittawat Jitkrittum, Jonas Kübler

NeurIPS 2020 Learning Kernel Tests Without Data Splitting Jonas Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet

NeurIPS 2020 MATE: Plugging in Model Awareness to Task Embedding for Meta Learning Xiaohan Chen, Zhangyang Wang, Siyu Tang, Krikamol Muandet

JMLR 2018 Design and Analysis of the NIPS 2016 Review Process Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike von Luxburg

FnTML 2017 Kernel Mean Embedding of Distributions: A Review and Beyond Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Bernhard Schölkopf

JMLR 2017 Minimax Estimation of Kernel Mean Embeddings Ilya Tolstikhin, Bharath K. Sriperumbudur, Krikamol Muandet

JMLR 2016 Kernel Mean Shrinkage Estimators Krikamol Muandet, Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf

JMLR 2015 The Randomized Causation Coefficient David Lopez-Paz, Krikamol Muandet, Benjamin Recht

ICML 2015 Towards a Learning Theory of Cause-Effect Inference David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Iliya Tolstikhin

UAI 2014 A Permutation-Based Kernel Conditional Independence Test Gary Doran, Krikamol Muandet, Kun Zhang, Bernhard Schölkopf

ICML 2014 Kernel Mean Estimation and Stein Effect Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur, Arthur Gretton, Bernhard Schoelkopf

NeurIPS 2014 Kernel Mean Estimation via Spectral Filtering Krikamol Muandet, Bharath Sriperumbudur, Bernhard Schölkopf

ICML 2013 Domain Adaptation Under Target and Conditional Shift Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, Zhikun Wang

ICML 2013 Domain Generalization via Invariant Feature Representation Krikamol Muandet, David Balduzzi, Bernhard Schölkopf

UAI 2013 One-Class Support Measure Machines for Group Anomaly Detection Krikamol Muandet, Bernhard Schölkopf

NeurIPS 2012 Learning from Distributions via Support Measure Machines Krikamol Muandet, Kenji Fukumizu, Francesco Dinuzzo, Bernhard Schölkopf