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Disambiguation of "Gal, Yarin"

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Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties.

, , , , , , and . AISTATS, volume 130 of Proceedings of Machine Learning Research, page 1756-1764. PMLR, (2021)

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Svetlana Yarina

Gal Schkolnik

Steffi Gal

Andreas Gál

Jan Gal

 

Other publications of authors with the same name

VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning., , , , , , and . ICLR, OpenReview.net, (2020)Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control., , , , , and . ICRA, page 7344-7350. IEEE, (2020)Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning, , and . (2019)cite arxiv:1907.00865.Active Learning under Pool Set Distribution Shift and Noisy Data., , and . CoRR, (2021)Quantifying Uncertainty for Machine Learning Based Diagnostic., , , and . CoRR, (2021)Dropout as a Bayesian Approximation: Appendix, and . (2015)cite arxiv:1506.02157Comment: 20 pages, 1 figure; ICML proceedings version.PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning., , , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 3305-3317. PMLR, (2021)Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models., , , and . NeurIPS, (2020)Model and Data Uncertainty for Satellite Time Series Forecasting with Deep Recurrent Models., , , , and . IGARSS, page 7025-7028. IEEE, (2020)On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes., , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 9148-9156. PMLR, (2021)