Time-independent generalization bounds for SGLD in non-convex settings
We establish generalization error bounds for stochastic gradient Langevin dynamics (SGLD) with constant learning rate under the assumptions of dissipativity and smoothness, a setting that has received increased attention in the sampling/optimization literature. Unlike existing bounds for SGLD in non...
| Main Authors: | , |
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| Format: | Conference item |
| Language: | English |
| Published: |
Neural Information Processing Systems Foundation
2021
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