Krikamol Muandet

Exact Shapley Attributions in Quadratic-time for FANOVA Gaussian Processes AAAI 2026 Instrumental and Proximal Causal Inference with Gaussian Processes UAI 2026 Quantification of Credal Uncertainty: A Distance-Based Approach UAI 2026 Verbalizing LLM’s Higher-order Uncertainty via Imprecise Probabilities UAI 2026 Credal Two-Sample Tests of Epistemic Uncertainty AISTATS 2025 Kernel Quantile Embeddings and Associated Probability Metrics ICML 2025 Sufficient Invariant Learning for Distribution Shift CVPR 2025 Truthful Elicitation of Imprecise Forecasts UAI 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 A Measure-Theoretic Axiomatisation of Causality NeurIPS 2023 Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models NeurIPS 2023 On the Relationship Between Explanation and Prediction: A Causal View ICML 2023 Towards Empirical Process Theory for Vector-Valued Functions: Metric Entropy of Smooth Function Classes ALT 2023 AutoML Two-Sample Test NeurIPS 2022 A Witness Two-Sample Test AISTATS 2022 Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions ICML 2022 Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression ICML 2021 Counterfactual Mean Embeddings JMLR 2021 Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction ICML 2021 A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings NeurIPS 2020 Dual Instrumental Variable Regression NeurIPS 2020 Fair Decisions Despite Imperfect Predictions AISTATS 2020 Kernel Conditional Density Operators AISTATS 2020 Kernel Conditional Moment Test via Maximum Moment Restriction UAI 2020 Learning Kernel Tests Without Data Splitting NeurIPS 2020 MATE: Plugging in Model Awareness to Task Embedding for Meta Learning NeurIPS 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 The Randomized Causation Coefficient JMLR 2015 Towards a Learning Theory of Cause-Effect Inference ICML 2015 Kernel Mean Estimation and Stein Effect ICML 2014 Kernel Mean Estimation via Spectral Filtering NeurIPS 2014 Domain Adaptation under Target and Conditional Shift ICML 2013 Domain Generalization via Invariant Feature Representation ICML 2013 Learning from Distributions via Support Measure Machines NeurIPS 2012