Query Selection via Weighted Entropy in Graph-Based Semi-supervised Classification. - Krikamol Muandet; Sanparith Marukatat; Cholwich Nattee (ACML, 2009)

Robust Graph Hyperparameter Learning for Graph Based Semi-supervised Classification. - Krikamol Muandet; Sanparith Marukatat; Cholwich Nattee (PAKDD, 2009)

Learning from Distributions via Support Measure Machines. - Krikamol Muandet; Kenji Fukumizu; Francesco Dinuzzo; Bernhard Schölkopf (NIPS, 2012)

Hilbert Space Embedding for Dirichlet Process Mixtures - Krikamol Muandet (CoRR, 2012)

Domain Generalization via Invariant Feature Representation. - Krikamol Muandet; David Balduzzi; Bernhard Schölkopf (ICML (1), 2013)

Domain Adaptation under Target and Conditional Shift. - Kun Zhang 001; Bernhard Schölkopf; Krikamol Muandet; Zhikun Wang (ICML (3), 2013)

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

Kernel Mean Estimation and Stein's Effect. - Krikamol Muandet; Kenji Fukumizu; Bharath K. Sriperumbudur; Arthur Gretton; Bernhard Schölkopf (CoRR, 2013)

Kernel Mean Estimation and Stein Effect. - Krikamol Muandet; Kenji Fukumizu; Bharath K. Sriperumbudur; Arthur Gretton; Bernhard Schölkopf (ICML, 2014)

Kernel Mean Estimation via Spectral Filtering. - Krikamol Muandet; Bharath K. Sriperumbudur; Bernhard Schölkopf (NIPS, 2014)

A Permutation-Based Kernel Conditional Independence Test. - Gary Doran; Krikamol Muandet; Kun Zhang 001; Bernhard Schölkopf (UAI, 2014)

Towards a Learning Theory of Cause-Effect Inference. - David Lopez-Paz; Krikamol Muandet; Bernhard Schölkopf; Ilya O. Tolstikhin (ICML, 2015)

Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations. - Bernhard Schölkopf; Krikamol Muandet; Kenji Fukumizu; Jonas Peters (CoRR, 2015)

The Randomized Causation Coefficient. - David Lopez-Paz; Krikamol Muandet; Benjamin Recht (J. Mach. Learn. Res., 2015)

Computing functions of random variables via reproducing kernel Hilbert space representations. - Bernhard Schölkopf; Krikamol Muandet; Kenji Fukumizu; Stefan Harmeling; Jonas Peters (Stat. Comput., 2015)

From Points to Probability Measures: Statistical Learning on Distributions with Kernel Mean Embedding. - Krikamol Muandet (2015)

TerseSVM : A Scalable Approach for Learning Compact Models in Large-scale Classification. - Rohit Babbar; Krikamol Muandet; Bernhard Schölkopf (SDM, 2016)

Kernel Mean Embedding of Distributions: A Review and Beyonds. - Krikamol Muandet; Kenji Fukumizu; Bharath K. Sriperumbudur; Bernhard Schölkopf (CoRR, 2016)

Kernel Mean Shrinkage Estimators. - Krikamol Muandet; Bharath K. Sriperumbudur; Kenji Fukumizu; Arthur Gretton; Bernhard Schölkopf (J. Mach. Learn. Res., 2016)

Kernel Mean Embedding of Distributions: A Review and Beyond. - Krikamol Muandet; Kenji Fukumizu; Bharath K. Sriperumbudur; Bernhard Schölkopf (Found. Trends Mach. Learn., 2017)

Minimax Estimation of Kernel Mean Embeddings. - Ilya O. Tolstikhin; Bharath K. Sriperumbudur; Krikamol Muandet (J. Mach. Learn. Res., 2017)

Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference. - Krikamol Muandet; Motonobu Kanagawa; Sorawit Saengkyongam; Sanparith Marukatat (CoRR, 2018)

Design and Analysis of the NIPS 2016 Review Process. - Nihar B. Shah; Behzad Tabibian; Krikamol Muandet; Isabelle Guyon; Ulrike von Luxburg (J. Mach. Learn. Res., 2018)

Local Temporal Bilinear Pooling for Fine-Grained Action Parsing. - Yan Zhang 054; Siyu Tang 001; Krikamol Muandet; Christian Jarvers; Heiko Neumann (CVPR, 2019)

Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces. - Arash Mehrjou; Wittawat Jitkrittum; Bernhard Schölkopf; Krikamol Muandet (CoRR, 2019)

Improving Consequential Decision Making under Imperfect Predictions. - Niki Kilbertus; Manuel Gomez-Rodriguez; Bernhard Schölkopf; Krikamol Muandet; Isabel Valera (CoRR, 2019)

Private Causal Inference using Propensity Scores. - Si Kai Lee; Luigi Gresele; Mijung Park; Krikamol Muandet (CoRR, 2019)

Quantum Mean Embedding of Probability Distributions. - Jonas M. Kübler; Krikamol Muandet; Bernhard Schölkopf (CoRR, 2019)

Low-rank Random Tensor for Bilinear Pooling. - Yan Zhang 054; Krikamol Muandet; Qianli Ma 007; Heiko Neumann; Siyu Tang 001 (CoRR, 2019)

Dual IV: A Single Stage Instrumental Variable Regression. - Krikamol Muandet; Arash Mehrjou; Si Kai Lee; Anant Raj (CoRR, 2019)

A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming. - Jia-Jie Zhu; Krikamol Muandet; Moritz Diehl; Bernhard Schölkopf (CoRR, 2019)

Grasping Field: Learning Implicit Representations for Human Grasps. - Korrawe Karunratanakul; Jinlong Yang; Yan Zhang 054; Michael J. Black; Krikamol Muandet; Siyu Tang 001 (3DV, 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 (AISTATS, 2020)

MATE: Plugging in Model Awareness to Task Embedding for Meta Learning. - Xiaohan Chen; Zhangyang Wang; Siyu Tang 001; Krikamol Muandet (NeurIPS, 2020)

Learning Kernel Tests Without Data Splitting. - Jonas M. Kübler; Wittawat Jitkrittum; Bernhard Schölkopf; Krikamol Muandet (NeurIPS, 2020)

Dual Instrumental Variable Regression. - Krikamol Muandet; Arash Mehrjou; Si Kai Lee; Anant Raj (NeurIPS, 2020)

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

Kernel Conditional Moment Test via Maximum Moment Restriction. - Krikamol Muandet; Wittawat Jitkrittum; Jonas M. Kübler (UAI, 2020)

Maximum Moment Restriction for Instrumental Variable Regression. - Rui Zhang; Masaaki Imaizumi; Bernhard Schölkopf; Krikamol Muandet (CoRR, 2020)

Regularised Least-Squares Regression with Infinite-Dimensional Output Space. - Junhyung Park; Krikamol Muandet (CoRR, 2020)

Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces. - Stefan Klus; Ingmar Schuster; Krikamol Muandet (J. Nonlinear Sci., 2020)

Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. - Afsaneh Mastouri; Yuchen Zhu; Limor Gultchin; Anna Korba; Ricardo Silva 001; Matt J. Kusner; Arthur Gretton; Krikamol Muandet (ICML, 2021)

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

An Optimal Witness Function for Two-Sample Testing. - Jonas M. Kübler; Wittawat Jitkrittum; Bernhard Schölkopf; Krikamol Muandet (CoRR, 2021)

Instrument Space Selection for Kernel Maximum Moment Restriction. - Rui Zhang; Krikamol Muandet; Bernhard Schölkopf; Masaaki Imaizumi (CoRR, 2021)

Counterfactual Mean Embeddings. - Krikamol Muandet; Motonobu Kanagawa; Sorawit Saengkyongam; Sanparith Marukatat (J. Mach. Learn. Res., 2021)

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

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

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

Impossibility of Collective Intelligence. - Krikamol Muandet (CoRR, 2022)

Gated Domain Units for Multi-source Domain Generalization. - Simon Föll; Alina Dubatovka; Eugen Ernst; Martin Maritsch; Patrik Okanovic; Gudrun Thäter; Joachim M. Buhmann; Felix Wortmann; Krikamol Muandet (CoRR, 2022)

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

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

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

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

Fast Adaptive Test-Time Defense with Robust Features. - Anurag Singh 006; Mahalakshmi Sabanayagam; Krikamol Muandet; Debarghya Ghoshdastidar (CoRR, 2023)

Looping in the Human: Collaborative and Explainable Bayesian Optimization. - Masaki Adachi; Brady Planden; David A. Howey; Krikamol Muandet; Michael A. Osborne; Siu Lun Chau (CoRR, 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 (Trans. Mach. Learn. Res., 2023)

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

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

Domain Generalisation via Imprecise Learning. - Anurag Singh 006; Siu Lun Chau; Shahine Bouabid; Krikamol Muandet (ICML, 2024)

Credal Two-Sample Tests of Epistemic Ignorance. - Siu Lun Chau; Antonin Schrab; Arthur Gretton; Dino Sejdinovic; Krikamol Muandet (CoRR, 2024)

Robust Feature Inference: A Test-time Defense Strategy using Spectral Projections. - Anurag Singh 006; Mahalakshmi Sabanayagam; Krikamol Muandet; Debarghya Ghoshdastidar (Trans. Mach. Learn. Res., 2024)

Learning Counterfactually Invariant Predictors. - Francesco Quinzan; Cecilia Casolo; Krikamol Muandet; Yucen Luo; Niki Kilbertus (Trans. Mach. Learn. Res., 2024)

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

Sufficient Invariant Learning for Distribution Shift. - Taero Kim; Subeen Park; Sungjun Lim 002; Yonghan Jung; Krikamol Muandet; Kyungwoo Song (CVPR, 2025)

Truthful Elicitation of Imprecise Forecasts. - Anurag Singh 006; Siu Lun Chau; Krikamol Muandet (UAI, 2025)

Strategic Learning with Local Explanations as Feedback. - Kiet Q. H. Vo; Siu Lun Chau; Masahiro Kato; Yixin Wang; Krikamol Muandet (CoRR, 2025)

Bayesian Optimization for Building Social-Influence-Free Consensus. - Masaki Adachi; Siu Lun Chau; Wenjie Xu; Anurag Singh 006; Michael A. Osborne; Krikamol Muandet (CoRR, 2025)

Integral Imprecise Probability Metrics. - Siu Lun Chau; Michele Caprio; Krikamol Muandet (CoRR, 2025)

Computing Exact Shapley Values in Polynomial Time for Product-Kernel Methods. - Majid Mohammadi; Siu Lun Chau; Krikamol Muandet (CoRR, 2025)

Kernel Quantile Embeddings and Associated Probability Metrics. - Masha Naslidnyk; Siu Lun Chau; François-Xavier Briol; Krikamol Muandet (CoRR, 2025)

When Shift Happens - Confounding Is to Blame. - Abbavaram Gowtham Reddy; Celia Rubio-Madrigal; Rebekka Burkholz; Krikamol Muandet (CoRR, 2025)

Exact Shapley Attributions in Quadratic-time for FANOVA Gaussian Processes. - Majid Mohammadi; Krikamol Muandet; Ilaria Tiddi; Annette ten Teije; Siu Lun Chau (CoRR, 2025)