[CLS21] Michael B Cohen, Yin Tat Lee, and Zhao Song. Solving linear programs in the current matrix multiplication time. Journal of the ACM (JACM), 68(1):1-39, 2021.
[CMTV17] Michael B. Cohen, Aleksander Madry, Dimitris Tsipras, and Adrian Vladu. Matrix scaling and balancing via box constrained newton's method and interior point methods. In 58th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2017, pages 902-913. IEEE Computer Society, 2017.
[CMY20] Yeshwanth Cherapanamjeri, Sidhanth Mohanty, and Morris Yau. List decodable mean estimation in nearly linear time. In 61st IEEE Annual Symposium on Foundations of Computer Science, FOCS 2020, pages 141-148. IEEE, 2020.
[CST21] Michael B. Cohen, Aaron Sidford, and Kevin Tian. Relative lipschitzness in extragradient methods and a direct recipe for acceleration. In 12th Innovations in Theoretical Computer Science Conference, ITCS 2021, volume 185 of LIPIcs, pages 62:1-62:18. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021.
[DGK18] Pavel E. Dvurechensky, Alexander V. Gasnikov, and Alexey Kroshnin. Computational optimal transport: Complexity by accelerated gradient descent is better than by sinkhorn's algorithm. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, volume 80 of Proceedings of Machine Learning Research, pages 1366–1375. PMLR, 2018.
[DWZ22] Ran Duan, Hongxun Wu, and Renfei Zhou. Faster matrix multiplication via asymmetric hashing. CoRR, abs/2210.10173, 2022.
[HJS+22] Baihe Huang, Shunhua Jiang, Zhao Song, Runzhou Tao, and Ruizhe Zhang. Solving SDP faster: A robust IPM framework and efficient implementation. In 63rd IEEE Annual Symposium on Foundations of Computer Science, FOCS 2022, pages 233-244. IEEE, 2022.
[JJST22] Arun Jambulapati, Yujia Jin, Aaron Sidford, and Kevin Tian. Regularized box-simplex games and dynamic decremental bipartite matching. In 49th International Colloquium on Automata, Languages, and Programming, ICALP 2022, volume 229 of LIPics, pages 77:1-77:20. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022.
$\left[\mathrm{JKL}^{+}20\right]$  Haotian Jiang, Tarun Kathuria, Yin Tat Lee, Swati Padmanabhan, and Zhao Song. A faster interior point method for semidefinite programming. In 61st IEEE Annual Symposium on Foundations of Computer Science, FOCS 2020, pages 910-918, 2020.
$\left[\mathrm{JLM}^{+}21\right]$  Arun Jambulapati, Jerry Li, Christopher Musco, Aaron Sidford, and Kevin Tian. Fast and near-optimal diagonal preconditioning. CoRR, abs/2008.01722, 2021.
[JLT20] Arun Jambulapati, Jerry Li, and Kevin Tian. Robust sub-gaussian principal component analysis and width-independent schatten packing. In Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020.
[JRT23] Arun Jambulapati, Victor Reis, and Kevin Tian. Linear-sized sparsifiers via near-linear time discrepancy theory. CoRR, abs/2305.08434, 2023.
[JST19] Arun Jambulapati, Aaron Sidford, and Kevin Tian. A direct  $\tilde{O}(1/\epsilon)$  iteration parallel algorithm for optimal transport. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, pages 11355-11366, 2019.
[KLOS14] Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford. An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. In Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2014, pages 217-226. SIAM, 2014.