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Langevin diffusions are rapidly convergent under appropriate functional inequality assumptions.
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2022
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Krishnakumar Balasubramanian, Sinho Chewi, Murat A Erdogdu, Adil Salim, and Shunshi Zhang, Towards a theory of non-log-concave sampling: First-order stationarity guarantees for Langevin monte carlo , Conference on Learning Theory, PMLR, 2022, pp. 2896–2923
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2022
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2022
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Murat A Erdogdu, Rasa Hosseinzadeh, and Shunshi Zhang, Convergence of Langevin Monte Carlo in chi-squared and Rényi divergence , International Conference on Artificial Intelligence and Statistics, PMLR, 2022, pp. 8151–8175
2022
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Ruilin Li, Hongyuan Zha, and Molei Tao, Sqrt(d) Dimension Dependence of Langevin Monte Carlo , The International Conference on Learning Representations, 2022
2022
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2022
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Keru Wu, Scott Schmidler, and Yuansi Chen, Minimax Mixing Time of the Metropolis-Adjusted Langevin Algorithm for Log-Concave Sampling , Journal of Machine Learning Research 23
2022
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2022
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2022
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Sinho Chewi, Log-concave sampling , 2023, Book draft available at https://chewisinho.github.io/
2023
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Sinho Chewi, Patrik R Gerber, Chen Lu, Thibaut Le Gouic, and Philippe Rigollet, The query complexity of sampling from strongly log-concave distributions in one dimension , Proceedings of Thirty Fifth Conference on Learning Theory (Po-Ling Loh and Maxim Raginsky, eds.), Proceedings of Machine Learning Research, vol. 178, PMLR, 2022, pp. 2041–2059
2059
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Alain Durmus, Arnaud Guillin, and Pierre Monmarché, Geometric ergodicity of the bouncy particle sampler , Annals of applied probability 30
2098
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