Marvels and pitfalls of the Langevin algorithm in noisy high-dimensional inference
Gradient-descent-based algorithms and their stochastic versions have widespread applications in machine learning and statistical inference. In this work, we carry out an analytic study of the performance of the algorithm most commonly considered in physics, the Langevin algorithm, in the context of...
| Auteurs principaux: | , , , , , |
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| Format: | Journal article |
| Langue: | English |
| Publié: |
American Physical Society
2020
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