implicit regularization

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1. 1▶Stanford CS229M - Lecture 16: Implicit regularization in classification problemsStanford CS229M: Machine Learning Theory - Fall 2021Notes
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2. 2▶Stanford CS229M - Lecture 15: Implicit regularization effect of initializationStanford CS229M: Machine Learning Theory - Fall 2021Notes
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3. 3▶Stanford CS229M - Lecture 14: Neural Tangent Kernel, Implicit regularization of gradient descentStanford CS229M: Machine Learning Theory - Fall 2021Notes
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4. 4▶Stanford CS229M - Lecture 13: Neural Tangent KernelStanford CS229M: Machine Learning Theory - Fall 2021Notes
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5. 5▶Stanford CS229M - Lecture 11: All-layer marginStanford CS229M: Machine Learning Theory - Fall 2021Notes
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6. 6▶Stanford CS229M - Lecture 10: Generalization bounds for deep netsStanford CS229M: Machine Learning Theory - Fall 2021Notes
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7. 7▶Stanford CS229M - Lecture 9: Covering number approach, Dudley TheoremStanford CS229M: Machine Learning Theory - Fall 2021Notes
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8. 8▶Stanford CS229M - Lecture 8: Refined generalization bounds for neural nets, Kernel methodsStanford CS229M: Machine Learning Theory - Fall 2021Notes
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9. 9▶Stanford CS229M - Lecture 7: Challenges in DL theory, generalization bounds for neural netsStanford CS229M: Machine Learning Theory - Fall 2021Notes
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10. 10▶Stanford CS229M - Lecture 6: Margin theory and Rademacher complexity for linear modelsStanford CS229M: Machine Learning Theory - Fall 2021Notes
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11. 11▶Stanford CS229M - Lecture 5: Rademacher complexity, empirical Rademacher complexityStanford CS229M: Machine Learning Theory - Fall 2021Notes
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12. 12▶Stanford CS229M - Lecture 4: Advanced concentration inequalitiesStanford CS229M: Machine Learning Theory - Fall 2021Notes
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13. 13▶Stanford CS229M - Lecture 3: Finite hypothesis class, discretizing infinite hypothesis spaceStanford CS229M: Machine Learning Theory - Fall 2021Notes
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14. 14▶Stanford CS229M - Lecture 2: Asymptotic analysis, uniform convergence, Hoeffding inequalityStanford CS229M: Machine Learning Theory - Fall 2021Notes
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15. 15▶Stanford CS229M - Lecture 1: Overview, supervised learning, empirical risk minimizationStanford CS229M: Machine Learning Theory - Fall 2021Notes
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16. 16▶Nearest Neighbors IIDeep Learning Boot CampNotes
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17. 17▶Generative ModelsDeep Learning Boot CampNotes
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18. 18▶Implicit Regularization IIDeep Learning Boot CampNotes
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19. 19▶Implicit Regularization IDeep Learning Boot CampNotes
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20. 20▶Nearest Neighbors IDeep Learning Boot CampNotes
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21. 21▶Recent Developments in Over-parametrized Neural Networks, Part IIDeep Learning Boot CampNotes
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22. 22▶Adversarial ExamplesDeep Learning Boot CampNotes
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23. 23▶Generalization IVDeep Learning Boot CampNotes
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24. 24▶Recent Developments in Over-parametrized Neural Networks, Part IDeep Learning Boot CampNotes
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25. 25▶Generalization IIIDeep Learning Boot CampNotes
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26. 26▶Optimization's Untold Gift to Learning: Implicit RegularizationOptimization, Statistics and UncertaintyNotes
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27. 27▶Fast Rates for Bandit Optimization with Upper-Confidence Frank-WolfeOptimization, Statistics and UncertaintyNotes
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28. 28▶On Approximation Guarantees for Greedy Low Rank OptimizationOptimization, Statistics and UncertaintyNotes
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29. 29▶Graph PoweringOptimization, Statistics and UncertaintyNotes
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30. 30▶Low-rank Matrix Completion: Adaptive Sampling Can Help When, How?Optimization, Statistics and UncertaintyNotes
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31. 31▶Implicit Regularization in Nonconvex Statistical EstimationOptimization, Statistics and UncertaintyNotes
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32. 32▶A Few Connections Between Optimization and ProbabilityOptimization, Statistics and UncertaintyNotes
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33. 33▶An Instability in Variational Methods for Learning Topic ModelsOptimization, Statistics and UncertaintyNotes
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34. 34▶Distributional Robustness, Learning, and Empirical LikelihoodOptimization, Statistics and UncertaintyNotes
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35. 35▶The Convergence of Hamiltonian Monte CarloOptimization, Statistics and UncertaintyNotes
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