Krikamol Muandet
36 papers · 2012–2026 · 8 conferences · across top CS/AI conferences
Achievements
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🐣 Hot Topic Early Bird 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🗺️ Taxonomy Completionist (12) 🌍 Conference Polyglot (8)
🧭
Keyword Pioneer
🐣
Hot Topic Early Bird
🏃
Academic Marathon
(13)
🤝
Dynamic Duo
(14)
🌱
Topic Pioneer
🔬
Deep Specialist
(10)
🧬
Topic Evolution
🏆
Keyword Champion
(5)
⚡
Prolific Year
(7)
🗃️
Keyword Collector
(143)
💎
Century Club
(35)
🔥
Unstoppable
(14)
🚀
Conference Pioneer
📈
Trend Setter
Conferences
ICML (10)
NIPS (9)
AISTATS (5)
JMLR (5)
AAAI (2)
CVPR (2)
UAI (2)
ALT (1)
Top co-authors
Research topics
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