Krikamol Muandet

108 papers A* 19A 10B 2C 1Journal 70Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	Exact Shapley Attributions in Quadratic-time for FANOVA Gaussian Processes. AAAI Majid Mohammadi, Krikamol Muandet, Ilaria Tiddi, Annette ten Teije, Siu Lun Chau
2026	J	`jnl`	Incentive Aware AI Regulations: A Credal Characterisation. CoRR Anurag Singh, Julian Rodemann, Rajeev Verma, Siu Lun Chau, Krikamol Muandet
2026	J	`jnl`	Instrumental and Proximal Causal Inference with Gaussian Processes. CoRR Yuqi Zhang, Krikamol Muandet, Dino Sejdinovic, Edwin Fong, Siu Lun Chau
2026	J	`jnl`	Performative Learning Theory. CoRR Julian Rodemann, Unai Fischer Abaigar, James Bailie, Krikamol Muandet
2026	J	`jnl`	Quantification of Credal Uncertainty: A Distance-Based Approach. CoRR Xabier Gonzalez-Garcia, Siu Lun Chau, Julian Rodemann, Michele Caprio, Krikamol Muandet, Humberto Bustince, Sébastien Destercke, Eyke Hüllermeier, Yusuf Sale
2026	J	`jnl`	Verbalizing LLM's Higher-order Uncertainty via Imprecise Probabilities. CoRR Anita Yang, Krikamol Muandet, Michele Caprio, Siu Lun Chau, Masaki Adachi
2025	J	`jnl`	An Analysis of Causal Effect Estimation using Outcome Invariant Data Augmentation. CoRR Uzair Akbar, Niki Kilbertus, Hao Shen, Krikamol Muandet, Bo Dai
2025	J	`jnl`	Bayesian Optimization for Building Social-Influence-Free Consensus. CoRR Masaki Adachi, Siu Lun Chau, Wenjie Xu, Anurag Singh, Michael A. Osborne, Krikamol Muandet
2025	J	`jnl`	Computing Exact Shapley Values in Polynomial Time for Product-Kernel Methods. CoRR Majid Mohammadi, Siu Lun Chau, Krikamol Muandet
2025	A	`conf`	Credal Two-Sample Tests of Epistemic Uncertainty. AISTATS Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet
2025	J	`jnl`	Exact Shapley Attributions in Quadratic-time for FANOVA Gaussian Processes. CoRR Majid Mohammadi, Krikamol Muandet, Ilaria Tiddi, Annette ten Teije, Siu Lun Chau
2025	J	`jnl`	Integral Imprecise Probability Metrics. CoRR Siu Lun Chau, Michele Caprio, Krikamol Muandet
2025	A*	`conf`	Kernel Quantile Embeddings and Associated Probability Metrics. ICML Masha Naslidnyk, Siu Lun Chau, François-Xavier Briol, Krikamol Muandet
2025	J	`jnl`	Kernel Quantile Embeddings and Associated Probability Metrics. CoRR Masha Naslidnyk, Siu Lun Chau, François-Xavier Briol, Krikamol Muandet
2025	J	`jnl`	Strategic Learning with Local Explanations as Feedback. CoRR Kiet Q. H. Vo, Siu Lun Chau, Masahiro Kato, Yixin Wang, Krikamol Muandet
2025	A*	`conf`	Sufficient Invariant Learning for Distribution Shift. CVPR Taero Kim, Subeen Park, Sungjun Lim, Yonghan Jung, Krikamol Muandet, Kyungwoo Song
2025	A	`conf`	Truthful Elicitation of Imprecise Forecasts. UAI Anurag Singh, Siu Lun Chau, Krikamol Muandet
2025	J	`jnl`	Truthful Elicitation of Imprecise Forecasts. CoRR Anurag Singh, Siu Lun Chau, Krikamol Muandet
2025	J	`jnl`	When Do Credal Sets Stabilize? Fixed-Point Theorems for Credal Set Updates. CoRR Michele Caprio, Siu Lun Chau, Krikamol Muandet
2025	J	`jnl`	When Shift Happens - Confounding Is to Blame. CoRR Abbavaram Gowtham Reddy, Celia Rubio-Madrigal, Rebekka Burkholz, Krikamol Muandet
2024	A*	`conf`	Causal Strategic Learning with Competitive Selection. AAAI Kiet Q. H. Vo, Muneeb Aadil, Siu Lun Chau, Krikamol Muandet
2024	J	`jnl`	Credal Two-Sample Tests of Epistemic Ignorance. CoRR Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet
2024	A*	`conf`	Domain Generalisation via Imprecise Learning. ICML Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet
2024	J	`jnl`	Domain Generalisation via Imprecise Learning. CoRR Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet
2024	J	`jnl`	Learning Counterfactually Invariant Predictors. Trans. Mach. Learn. Res. Francesco Quinzan, Cecilia Casolo, Krikamol Muandet, Yucen Luo, Niki Kilbertus
2024	A	`conf`	Looping in the Human: Collaborative and Explainable Bayesian Optimization. AISTATS Masaki Adachi, Brady Planden, David A. Howey, Michael A. Osborne, Sebastian Orbell, Natalia Ares, Krikamol Muandet, Siu Lun Chau
2024	J	`jnl`	Robust Feature Inference: A Test-time Defense Strategy using Spectral Projections. Trans. Mach. Learn. Res. Anurag Singh, Mahalakshmi Sabanayagam, Krikamol Muandet, Debarghya Ghoshdastidar
2023	A*	`conf`	A Measure-Theoretic Axiomatisation of Causality. NeurIPS Junhyung Park, Simon Buchholz, Bernhard Schölkopf, Krikamol Muandet
2023	J	`jnl`	A Measure-Theoretic Axiomatisation of Causality. CoRR Junhyung Park, Simon Buchholz, Bernhard Schölkopf, Krikamol Muandet
2023	J	`jnl`	Causal Strategic Learning with Competitive Selection. CoRR Kiet Q. H. Vo, Muneeb Aadil, Siu Lun Chau, Krikamol Muandet
2023	A*	`conf`	Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models. NeurIPS Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic
2023	J	`jnl`	Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models. CoRR Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic
2023	J	`jnl`	Fast Adaptive Test-Time Defense with Robust Features. CoRR Anurag Singh, Mahalakshmi Sabanayagam, Krikamol Muandet, Debarghya Ghoshdastidar
2023	J	`jnl`	Gated Domain Units for Multi-source Domain Generalization. Trans. Mach. Learn. Res. Simon Föll, Alina Dubatovka, Eugen Ernst, Siu Lun Chau, Martin Maritsch, Patrik Okanovic, Gudrun Thäter, Joachim M. Buhmann, Felix Wortmann, Krikamol Muandet
2023	J	`jnl`	Looping in the Human: Collaborative and Explainable Bayesian Optimization. CoRR Masaki Adachi, Brady Planden, David A. Howey, Krikamol Muandet, Michael A. Osborne, Siu Lun Chau
2023	A*	`conf`	On the Relationship Between Explanation and Prediction: A Causal View. ICML Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim
2023	B	`conf`	Towards Empirical Process Theory for Vector-Valued Functions: Metric Entropy of Smooth Function Classes. ALT Junhyung Park, Krikamol Muandet
2022	A	`conf`	A Witness Two-Sample Test. AISTATS Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet
2022	A*	`conf`	AutoML Two-Sample Test. NeurIPS Jonas M. Kübler, Vincent Stimper, Simon Buchholz, Krikamol Muandet, Bernhard Schölkopf
2022	J	`jnl`	AutoML Two-Sample Test. CoRR Jonas M. Kübler, Vincent Stimper, Simon Buchholz, Krikamol Muandet, Bernhard Schölkopf
2022	A*	`conf`	Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions. ICML Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf
2022	J	`jnl`	Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions. CoRR Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf
2022	J	`jnl`	Gated Domain Units for Multi-source Domain Generalization. CoRR Simon Föll, Alina Dubatovka, Eugen Ernst, Martin Maritsch, Patrik Okanovic, Gudrun Thäter, Joachim M. Buhmann, Felix Wortmann, Krikamol Muandet
2022	J	`jnl`	Impossibility of Collective Intelligence. CoRR Krikamol Muandet
2022	J	`jnl`	Learning Counterfactually Invariant Predictors. CoRR Francesco Quinzan, Cecilia Casolo, Krikamol Muandet, Niki Kilbertus, Yucen Luo
2022	J	`jnl`	On the Relationship Between Explanation and Prediction: A Causal View. CoRR Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim
2021	J	`jnl`	An Optimal Witness Function for Two-Sample Testing. CoRR Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet
2021	A*	`conf`	Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression. ICML Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet
2021	J	`jnl`	Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression. CoRR Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet
2021	J	`jnl`	Counterfactual Mean Embeddings. J. Mach. Learn. Res. Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, Sanparith Marukatat
2021	J	`jnl`	Instrument Space Selection for Kernel Maximum Moment Restriction. CoRR Rui Zhang, Krikamol Muandet, Bernhard Schölkopf, Masaaki Imaizumi
2021	A*	`conf`	Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. ICML Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet
2021	J	`jnl`	Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. CoRR Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet
2020	A*	`conf`	A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings. NeurIPS Junhyung Park, Krikamol Muandet
2020	J	`jnl`	A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings. CoRR Junhyung Park, Krikamol Muandet
2020	A*	`conf`	Dual Instrumental Variable Regression. NeurIPS Krikamol Muandet, Arash Mehrjou, Si Kai Lee, Anant Raj
2020	J	`jnl`	Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces. J. Nonlinear Sci. Stefan Klus, Ingmar Schuster, Krikamol Muandet
2020	A	`conf`	Fair Decisions Despite Imperfect Predictions. AISTATS Niki Kilbertus, Manuel Gomez Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera
2020	—	`conf`	Grasping Field: Learning Implicit Representations for Human Grasps. 3DV Korrawe Karunratanakul, Jinlong Yang, Yan Zhang, Michael J. Black, Krikamol Muandet, Siyu Tang
2020	J	`jnl`	Grasping Field: Learning Implicit Representations for Human Grasps. CoRR Korrawe Karunratanakul, Jinlong Yang, Yan Zhang, Michael J. Black, Krikamol Muandet, Siyu Tang
2020	A	`conf`	Kernel Conditional Density Operators. AISTATS Ingmar Schuster, Mattes Mollenhauer, Stefan Klus, Krikamol Muandet
2020	A	`conf`	Kernel Conditional Moment Test via Maximum Moment Restriction. UAI Krikamol Muandet, Wittawat Jitkrittum, Jonas M. Kübler
2020	J	`jnl`	Kernel Conditional Moment Test via Maximum Moment Restriction. CoRR Krikamol Muandet, Wittawat Jitkrittum, Jonas M. Kübler
2020	A*	`conf`	Learning Kernel Tests Without Data Splitting. NeurIPS Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet
2020	J	`jnl`	Learning Kernel Tests Without Data Splitting. CoRR Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet
2020	A*	`conf`	MATE: Plugging in Model Awareness to Task Embedding for Meta Learning. NeurIPS Xiaohan Chen, Zhangyang Wang, Siyu Tang, Krikamol Muandet
2020	J	`jnl`	Maximum Moment Restriction for Instrumental Variable Regression. CoRR Rui Zhang, Masaaki Imaizumi, Bernhard Schölkopf, Krikamol Muandet
2020	J	`jnl`	Regularised Least-Squares Regression with Infinite-Dimensional Output Space. CoRR Junhyung Park, Krikamol Muandet
2019	J	`jnl`	A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming. CoRR Jia-Jie Zhu, Krikamol Muandet, Moritz Diehl, Bernhard Schölkopf
2019	J	`jnl`	Dual IV: A Single Stage Instrumental Variable Regression. CoRR Krikamol Muandet, Arash Mehrjou, Si Kai Lee, Anant Raj
2019	J	`jnl`	Improving Consequential Decision Making under Imperfect Predictions. CoRR Niki Kilbertus, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera
2019	J	`jnl`	Kernel Conditional Density Operators. CoRR Ingmar Schuster, Mattes Mollenhauer, Stefan Klus, Krikamol Muandet
2019	A*	`conf`	Local Temporal Bilinear Pooling for Fine-Grained Action Parsing. CVPR Yan Zhang, Siyu Tang, Krikamol Muandet, Christian Jarvers, Heiko Neumann
2019	J	`jnl`	Low-rank Random Tensor for Bilinear Pooling. CoRR Yan Zhang, Krikamol Muandet, Qianli Ma, Heiko Neumann, Siyu Tang
2019	J	`jnl`	Private Causal Inference using Propensity Scores. CoRR Si Kai Lee, Luigi Gresele, Mijung Park, Krikamol Muandet
2019	J	`jnl`	Quantum Mean Embedding of Probability Distributions. CoRR Jonas M. Kübler, Krikamol Muandet, Bernhard Schölkopf
2019	J	`jnl`	Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces. CoRR Arash Mehrjou, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet
2018	J	`jnl`	Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference. CoRR Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, Sanparith Marukatat
2018	J	`jnl`	Design and Analysis of the NIPS 2016 Review Process. J. Mach. Learn. Res. Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike von Luxburg
2018	J	`jnl`	Local Temporal Bilinear Pooling for Fine-grained Action Parsing. CoRR Yan Zhang, Siyu Tang, Krikamol Muandet, Christian Jarvers, Heiko Neumann
2017	J	`jnl`	Design and Analysis of the NIPS 2016 Review Process. CoRR Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike von Luxburg
2017	J	`jnl`	Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces. CoRR Stefan Klus, Ingmar Schuster, Krikamol Muandet
2017	J	`jnl`	Kernel Mean Embedding of Distributions: A Review and Beyond. Found. Trends Mach. Learn. Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Bernhard Schölkopf
2017	J	`jnl`	Minimax Estimation of Kernel Mean Embeddings. J. Mach. Learn. Res. Ilya O. Tolstikhin, Bharath K. Sriperumbudur, Krikamol Muandet
2016	J	`jnl`	Kernel Mean Embedding of Distributions: A Review and Beyonds. CoRR Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Bernhard Schölkopf
2016	J	`jnl`	Kernel Mean Shrinkage Estimators. J. Mach. Learn. Res. Krikamol Muandet, Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf
2016	A	`conf`	TerseSVM : A Scalable Approach for Learning Compact Models in Large-scale Classification. SDM Rohit Babbar, Krikamol Muandet, Bernhard Schölkopf
2015	J	`jnl`	Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations. CoRR Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Jonas Peters
2015	J	`jnl`	Computing functions of random variables via reproducing kernel Hilbert space representations. Stat. Comput. Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Stefan Harmeling, Jonas Peters
2015	—	`—`	From Points to Probability Measures: Statistical Learning on Distributions with Kernel Mean Embedding. Krikamol Muandet
2015	J	`jnl`	The Randomized Causation Coefficient. J. Mach. Learn. Res. David Lopez-Paz, Krikamol Muandet, Benjamin Recht
2015	A*	`conf`	Towards a Learning Theory of Cause-Effect Inference. ICML David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Ilya O. Tolstikhin
2014	A	`conf`	A Permutation-Based Kernel Conditional Independence Test. UAI Gary Doran, Krikamol Muandet, Kun Zhang, Bernhard Schölkopf
2014	A*	`conf`	Kernel Mean Estimation and Stein Effect. ICML Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf
2014	—	`conf`	Kernel Mean Estimation via Spectral Filtering. NIPS Krikamol Muandet, Bharath K. Sriperumbudur, Bernhard Schölkopf
2014	J	`jnl`	Kernel Mean Shrinkage Estimators. CoRR Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf
2014	J	`jnl`	One-Class Support Measure Machines for Group Anomaly Detection. CoRR Krikamol Muandet, Bernhard Schölkopf
2013	—	`conf`	Domain Adaptation under Target and Conditional Shift. ICML (3) Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, Zhikun Wang
2013	J	`jnl`	Domain Generalization via Invariant Feature Representation CoRR Krikamol Muandet, David Balduzzi, Bernhard Schölkopf
2013	—	`conf`	Domain Generalization via Invariant Feature Representation. ICML (1) Krikamol Muandet, David Balduzzi, Bernhard Schölkopf
2013	J	`jnl`	Kernel Mean Estimation and Stein's Effect. CoRR Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf
2013	J	`jnl`	One-Class Support Measure Machines for Group Anomaly Detection CoRR Krikamol Muandet, Bernhard Schölkopf
2013	A	`conf`	One-Class Support Measure Machines for Group Anomaly Detection. UAI Krikamol Muandet, Bernhard Schölkopf
2012	J	`jnl`	Hilbert Space Embedding for Dirichlet Process Mixtures CoRR Krikamol Muandet
2012	J	`jnl`	Learning from Distributions via Support Measure Machines CoRR Krikamol Muandet, Bernhard Schölkopf, Kenji Fukumizu, Francesco Dinuzzo
2012	—	`conf`	Learning from Distributions via Support Measure Machines. NIPS Krikamol Muandet, Kenji Fukumizu, Francesco Dinuzzo, Bernhard Schölkopf
2009	C	`conf`	Query Selection via Weighted Entropy in Graph-Based Semi-supervised Classification. ACML Krikamol Muandet, Sanparith Marukatat, Cholwich Nattee
2009	B	`conf`	Robust Graph Hyperparameter Learning for Graph Based Semi-supervised Classification. PAKDD Krikamol Muandet, Sanparith Marukatat, Cholwich Nattee