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Recurrent machines for likelihood-free inference

• Authors : Pesah, Arthur; Wehenkel, Antoine; Louppe, Gilles

Subjects: Deep Learning; Machine Learning; Meta Learning

• Source: Workshop of Meta-Learning at Thirty-second Conference on Neural Information Processing Systems 2018, Montreal, Canada [CA], December 2018

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Likelihood-free inference with an improved cross-entropy estimator

• Authors : Stoye, Markus; Brehmer, Johann; Louppe, Gilles

Subjects: Statistics - Machine Learning; Computer Science - Machine Learning; High Energy Physics - Phenomenology

• Source: Machine Learning and the Physical Sciences, NeurIPS 2019, Vancouver, Canada [CA], December 14, 2019

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Mining gold from implicit models to improve likelihood-free inference

• Authors : Brehmer, Johann; Louppe, Gilles; Pavez, Juan

Subjects: Statistics - Machine Learning; Computer Science - Learning; High Energy Physics - Phenomenology

• Source: Proceedings of the National Academy of Sciences of the United States of America (2020-02-20)

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Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization

• Authors : Delaunoy, Arnaud; Wehenkel, Antoine; Hinderer, Tanja

Subjects: Gravitational wave; Likelihood-free inference; Simulation-based inference

• Source: Machine Learning and the Physical Sciences. Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS), December 11, 2020

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A Crisis In Simulation-Based Inference? Beware, Your Posterior Approximations Can Be Unfaithful

• Authors : Hermans, Joeri; Delaunoy, Arnaud; Rozet, François

Subjects: Simulation-based inference; Reliable inference; Likelihood-free inference

• Source: Transactions on Machine Learning Research (2022-11)

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The frontier of simulation-based inference

• Authors : Cranmer, Kyle; Brehmer, Johann; Louppe, Gilles

Subjects: Statistical inference; Implicit models; Likelihood-free inference

• Source: Proceedings of the National Academy of Sciences of the United States of America (2020-05-29)

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Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference

• Authors : Vandegar, Maxime; Kagan, Michael; Wehenkel, Antoine

Subjects: Engineering, computing & technology; Computer science; Physical, chemical, mathematical & earth Sciences

• Source: Proceedings of AISTATS 2021 (2021-04-13); The 24th International Conference on Artificial Intelligence and Statistics, April 2021

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Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time

• Authors : Kurt Miller, Benjamin; Cole, Alex; Louppe, Gilles

Subjects: Engineering, computing & technology; Computer science; Ingénierie, informatique & technologie

• Source: Machine Learning and the Physical Sciences. Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS), December 11, 2020

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Adversarial Variational Optimization of Non-Differentiable Simulators

• Authors : Louppe, Gilles; Hermans, Joeri; Cranmer, Kyle

Subjects: Statistics - Machine Learning; Computer Science - Learning; Engineering, computing & technology

• Source: Proceedings of Machine Learning Research (2019-04); AISTATS 2019, Okinawa, Japan [JP], April 16-18, 2019

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Approximating Likelihood Ratios with Calibrated Discriminative Classifiers

• Authors : Cranmer, Kyle; Pavez, Juan; Louppe, Gilles

Subjects: Statistics - Applications; Physics - Data Analysis; Statistics and Probability

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