← Authors

Krikamol Muandet

36 papers · 2012–2026 · 8 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (8) 🌈 Renaissance Researcher (7) 🐝 Cross-Pollinator (15) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (12) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (13) 🀝 Dynamic Duo (14) 🌱 Topic Pioneer πŸ”¬ Deep Specialist (10) 🧬 Topic Evolution πŸ† Keyword Champion (5) ⚑ Prolific Year (7) πŸ—ƒοΈ Keyword Collector (143) πŸ’Ž Century Club (35) πŸ”₯ Unstoppable (14) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter

Conferences

ICML (10) NIPS (9) AISTATS (5) JMLR (5) AAAI (2) CVPR (2) UAI (2) ALT (1)

Top co-authors

Bernhard SchΓΆlkopf (14) Siu Lun Chau (8) Arthur Gretton (4) Junhyung Park (4) Bharath Sriperumbudur (3) Kenji Fukumizu (3) Wittawat Jitkrittum (3) Siyu Tang (2) Jonas KΓΌbler (2) Anurag Singh (2)

Research topics

Statistics (2) Probability (2)

Keywords

reproducing kernel hilbert space (10) kernel methods (10) causal inference (7) kernel mean embedding (5) shrinkage estimator (3) maximum mean discrepancy (3) hypothesis testing (3) kernel mean estimation (2) statistical learning theory (2) observational datum (2) feature learning (2) explainable ai (2) domain adaptation (2) probability distribution (2) model interpretability (2) probabilistic modeling (2) domain generalization (2) gaussian process (2) two-sample test (2) distribution shift (2)

Papers

Exact Shapley Attributions in Quadratic-time for FANOVA Gaussian Processes AAAI 2026
Kernel Quantile Embeddings and Associated Probability Metrics ICML 2025
Sufficient Invariant Learning for Distribution Shift CVPR 2025
Truthful Elicitation of Imprecise Forecasts UAI 2025
Credal Two-Sample Tests of Epistemic Uncertainty AISTATS 2025
Causal Strategic Learning with Competitive Selection AAAI 2024
Domain Generalisation via Imprecise Learning ICML 2024
Looping in the Human: Collaborative and Explainable Bayesian Optimization AISTATS 2024
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models NIPS 2023
Towards Empirical Process Theory for Vector-Valued Functions: Metric Entropy of Smooth Function Classes ALT 2023
On the Relationship Between Explanation and Prediction: A Causal View ICML 2023
A Measure-Theoretic Axiomatisation of Causality NIPS 2023
A Witness Two-Sample Test AISTATS 2022
AutoML Two-Sample Test NIPS 2022
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions ICML 2022
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction ICML 2021
Counterfactual Mean Embeddings JMLR 2021
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression ICML 2021
Kernel Conditional Moment Test via Maximum Moment Restriction UAI 2020
Dual Instrumental Variable Regression NIPS 2020
Learning Kernel Tests Without Data Splitting NIPS 2020
MATE: Plugging in Model Awareness to Task Embedding for Meta Learning NIPS 2020
A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings NIPS 2020
Fair Decisions Despite Imperfect Predictions AISTATS 2020
Kernel Conditional Density Operators AISTATS 2020
Local Temporal Bilinear Pooling for Fine-Grained Action Parsing CVPR 2019
Design and Analysis of the NIPS 2016 Review Process JMLR 2018
Minimax Estimation of Kernel Mean Embeddings JMLR 2017
Kernel Mean Shrinkage Estimators JMLR 2016
Towards a Learning Theory of Cause-Effect Inference ICML 2015
The Randomized Causation Coefficient JMLR 2015
Kernel Mean Estimation via Spectral Filtering NIPS 2014
Kernel Mean Estimation and Stein Effect ICML 2014
Domain Generalization via Invariant Feature Representation ICML 2013
Domain Adaptation under Target and Conditional Shift ICML 2013
Learning from Distributions via Support Measure Machines NIPS 2012