Reading.

If you are interested in Physics Informed Machine Learning, Neural Operators, Machine Learning Theory, Computational Complexity, Reinforcement Learning, Formal Methods, and other cool stuff, check out my Reading List below. I will be updating this list as I read more papers and books.

Neural Operators

  • Kovachki, Nikola, et al. "Neural operator: Learning maps between function spaces." arXiv preprint arXiv:2108.08481 (2021).
  • Li, Zongyi, et al. "Fourier neural operator for parametric partial differential equations." arXiv preprint arXiv:2010.08895 (2020).
  • Li, Zongyi, et al. "Physics-informed neural operator for learning partial differential equations." arXiv preprint arXiv:2111.03794 (2021).
  • Müller, Thomas, et al. "Instant neural graphics primitives with a multiresolution hash encoding." ACM Transactions on Graphics (ToG) 41.4 (2022): 1-15.
  • Lu, Lu, et al. "DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators." Nature Machine Intelligence 3.3 (2021): 218-229.
  • Brandstetter, Johannes, et al. "Message passing neural PDE solvers." ICLR (2022).
  • Li, Zongyi, et al. "Multipole graph neural operator for parametric partial differential equations." NeurIPS (2022).

Machine Learning Theory

  • 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.
  • Zhang, Chiyuan, et al. "Understanding deep learning requires rethinking generalization." ICLR (2017).
  • Belkin, Mikhail, et al. "Reconciling modern machine-learning practice and the classical bias–variance trade-off." PNAS 116.32 (2019): 15849-15854.
  • Arora, Sanjeev, et al. "A compression approach to generalization bounds for neural networks." JMLR 21.63 (2020): 1-34.
  • Bartlett, Peter L., et al. "Spectrally-normalized margin bounds for neural networks." NeurIPS (2017).
  • Allen-Zhu, Zeyuan, et al. "Learning and generalization in overparameterized neural networks, going beyond two layers." NeurIPS (2019).

Reinforcement Learning

  • 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.
  • Lattimore, Tor, Szepesvári, Csaba. Bandit Algorithms. Cambridge University Press, 2020.
  • Mnih, Volodymyr, et al. "Human-level control through deep reinforcement learning." Nature 518.7540 (2015): 529-533.
  • Schulman, John, et al. "Proximal policy optimization algorithms." arXiv preprint arXiv:1707.06347 (2017).
  • Silver, David, et al. "Mastering the game of Go with deep neural networks and tree search." Nature 529.7587 (2016): 484-489.
  • Coursera - Reinforcement Learning Specialization

Computational Complexity

  • Dean, Walter, "Computational Complexity Theory", The Stanford Encyclopedia of Philosophy (Fall 2021 Edition), Edward N. Zalta (ed.), URL = link.
  • Arora, Sanjeev, and Boaz Barak. Computational Complexity: A Modern Approach. Cambridge University Press, 2009.
  • Fortnow, Lance. The Status of the P Versus NP Problem. Communications of the ACM, 52(9), 2009.
  • Valiant, Leslie G. "The complexity of computing the permanent." Theoretical Computer Science 8.2 (1979): 189-201.
  • Cook, Stephen. "The complexity of theorem-proving procedures." STOC (1971).

Lean & Formalization / Interactive Theorem Provers

  • de Moura, Leonardo, et al. "The Lean theorem prover (system description)." CADE (2015).
  • Han, X., et al. "ProofNet: Autoformalizing and Formally Verifying Mathematics with Language Models and Proof Assistants." arXiv preprint arXiv:2305.20050 (2023).
  • Polu, S., et al. "Formalizing Mathematics with Large Language Models." arXiv preprint arXiv:2205.12615 (2022).
  • Yang, Kaiyu, et al. "LeanDojo: Theorem proving with retrieval-augmented language models." NeurIPS (2023).
  • Kumarappan, Adarsh, George, Robert Joseph, Anandkumar, Anima, et al. "LeanAgent: Lifelong Learning for Formal Theorem Proving." ICLR (2025).
  • Suozhi Huang, George, Robert Joseph, Anandkumar, Anima, et al. "LeanProgress: Guiding Search for Neural Theorem Proving via Proof Progress Prediction." arXiv preprint arXiv:2502.17925 (2025).
  • Wang, Yuhuai, et al. "Codex: Generating code from natural language in OpenAI Codex." arXiv preprint arXiv:2107.03374 (2021).
  • Gauthier, Thomas, et al. "TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning." arXiv preprint arXiv:2203.11876 (2022).
  • Blanchette, Jasmin Christian, et al. "Hammering towards QED." Journal of Formalized Reasoning 9.1 (2016): 101-148. (Isabelle/HOL)