[CDG19] Yu Cheng, Ilias Diakonikolas, and Rong Ge. High-dimensional robust mean estimation in nearly-linear time. In Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2019, San Diego, California, USA, January 6-9, 2019, pages 2755-2771, 2019.
[CDST19] Yair Carmon, John C. Duchi, Aaron Sidford, and Kevin Tian. A rank-1 sketch for matrix multiplicative weights. In *Conference on Learning Theory*, COLT 2019, 25-28 June 2019, Phoenix, AZ, USA, pages 589-623, 2019.
[CFB19] Yeshwanth Cherapanamjeri, Nicolas Flammarion, and Peter L. Bartlett. Fast mean estimation with sub-gaussian rates. In Conference on Learning Theory, COLT 2019, 25-28 June 2019, Phoenix, AZ, USA, pages 786-806, 2019.
[CG18] Yu Cheng and Rong Ge. Non-convex matrix completion against a semi-random adversary. In Conference On Learning Theory, COLT 2018, Stockholm, Sweden, 6-9 July 2018, pages 1362-1394, 2018.
$\left[\mathrm{CKK}^{+}18\right]$  Michael B. Cohen, Jonathan A. Kelner, Rasmus Kyng, John Peebles, Richard Peng, Anup B. Rao, and Aaron Sidford. Solving directed laplacian systems in nearly-linear time through sparse LU factorizations. In 59th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2018, Paris, France, October 7-9, 2018, pages 898-909, 2018.
[CKM+11] Paul F. Christiano, Jonathan A. Kelner, Aleksander Madry, Daniel A. Spielman, and Shang-Hua Teng. Electrical flows, laplacian systems, and faster approximation of maximum flow in undirected graphs. In Proceedings of the 43rd ACM Symposium on Theory of Computing, STOC 2011, San Jose, CA, USA, 6-8 June 2011, pages 273-282, 2011.
$\left[\mathrm{CKM}^{+}14\right]$  Michael B. Cohen, Rasmus Kyng, Gary L. Miller, Jakub W. Pachocki, Richard Peng, Anup B. Rao, and Shen Chen Xu. Solving SDD linear systems in nearly  $m \log^{1/2} n$  time. In Symposium on Theory of Computing, STOC 2014, New York, NY, USA, May 31 - June 03, 2014, pages 343-352, 2014.
$\left[\mathrm{CKP}^{+}16\right]$  Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, Aaron Sidford, and Adrian Vladu. Faster algorithms for computing the stationary distribution, simulating random walks, and more. In IEEE 57th Annual Symposium on Foundations of Computer Science, FOCS 2016, 9-11 October 2016, Hyatt Regency, New Brunswick, New Jersey, USA, pages 583-592, 2016.
$\left[\mathrm{CKP}^{+}17\right]$  Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, Anup B. Rao, Aaron Sidford, and Adrian Vladu. Almost-linear-time algorithms for markov chains and new spectral primitives for directed graphs. In Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2017, Montreal, QC, Canada, June 19-23, 2017, pages 410-419, 2017.
[CMSV17] Michael B. Cohen, Aleksander Madry, Piotr Sankowski, and Adrian Vladu. Negative-weight shortest paths and unit capacity minimum cost flow in  $\tilde{\mathfrak{o}}$  ( $m^{10/7} \log W$ ) time (extended abstract). In Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2017, Barcelona, Spain, Hotel Porta Fira, January 16-19, pages 752-771, 2017.
[CMTV17a] 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 Proceedings of the 58th Annual IEEE Symposium on Foundations of Computer Science (FOCS), pages 902-913, 2017.
[CMTV17b] 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, Berkeley, CA, USA, October 15-17, 2017, pages 902-913, 2017.