If you are interested in Machine Learning Theory, Computational Complexity, Reinforcement Learning and other cool stuff, check out my Reading List below. I will be updating this list as I read more papers and books.
- Foret, Pierre, et al. “Sharpness-aware minimization for efficiently improving generalization.” arXiv preprint arXiv:2010.01412 (2020).
- Nakkiran, Preetum. 2021. Towards an Empirical Theory of Deep Learning. Doctoral dissertation, Harvard University Graduate School of Arts and Sciences.
- Salimans, Tim, et al. “Evolution strategies as a scalable alternative to reinforcement learning.” arXiv preprint arXiv:1703.03864 (2017).
- Sutton, R.S. & Barto, A.G., 2018. Reinforcement learning: An introduction, MIT press.
- Dean, Walter, “Computational Complexity Theory”, The Stanford Encyclopedia of Philosophy (Fall 2021 Edition), Edward N. Zalta (ed.), URL = https://plato.stanford.edu/archives/fall2021/entries/computational-complexity/.