Epsilon's Machine Learning Notes
Prelogue
1
Computational and Statistical Learning Theory
1.1
PAC Framework
2
Topics in Deep Learning
3
Interpretability
3.1
Attribution
3.1.1
Saliency with Guarantees
3.2
Knowledge Tracing
3.2.1
Literature Survey
3.3
Compositionality
3.3.1
Related Works in ICLR 2020
3.4
Understanding internal representation
4
Robustness, Adversary and Causality
4.1
Applications
4.1.1
Certified Robustness in Text Classification
4.1.2
Adversarial Examples for Natural Language
5
Out-of-Distribution Learning
References
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Epsilon’s Machine Learning Notes
Chapter 1
Computational and Statistical Learning Theory
This chapter covers the following topics:
PAC and PAC Bayes framework.