aaron sidford cv

Some I am still actively improving and all of them I am happy to continue polishing. Office: 380-T Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Improves the stochas-tic convex optimization problem in parallel and DP setting. missouri noodling association president cnn. . In Sidford's dissertation, Iterative Methods, Combinatorial . Aaron Sidford - Google Scholar Group Resources. I received my PhD from the department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology where I was advised by Professor Jonathan Kelner. University of Cambridge MPhil. Their, This "Cited by" count includes citations to the following articles in Scholar. arXiv | conference pdf (alphabetical authorship), Jonathan Kelner, Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Honglin Yuan, Big-Step-Little-Step: Gradient Methods for Objectives with Multiple Scales. Allen Liu - GitHub Pages My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). [pdf] [talk] Winter 2020 Teaching assistant for EE364a: Convex Optimization I taught by John Duchi, Fall 2018 Teaching assitant for CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019 taught by Greg Valiant. Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. [pdf] I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. Mail Code. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. By using this site, you agree to its use of cookies. Cameron Musco - Manning College of Information & Computer Sciences [1811.10722] Solving Directed Laplacian Systems in Nearly-Linear Time Yujia Jin. My CV. He received his PhD from the Electrical Engineering and Computer Science Department at the Massachusetts Institute of Technology, where he was advised by Jonathan Kelner. United States. In International Conference on Machine Learning (ICML 2016). The authors of most papers are ordered alphabetically. We forward in this generation, Triumphantly. aaron sidford cv with Aaron Sidford Aaron Sidford. Semantic parsing on Freebase from question-answer pairs. Before Stanford, I worked with John Lafferty at the University of Chicago. [pdf] 113 * 2016: The system can't perform the operation now. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. 2016. theses are protected by copyright. [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. She was 19 years old and looking forward to the start of classes and reuniting with her college pals. Aaron Sidford - Stanford University Journal of Machine Learning Research, 2017 (arXiv). to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching It was released on november 10, 2017. 2013. The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. with Yair Carmon, Aaron Sidford and Kevin Tian [pdf] Aaron's research interests lie in optimization, the theory of computation, and the . I am broadly interested in mathematics and theoretical computer science. (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. Thesis, 2016. pdf. Publications | Jakub Pachocki - Harvard University 4026. I maintain a mailing list for my graduate students and the broader Stanford community that it is interested in the work of my research group. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). F+s9H Sivakanth Gopi at Microsoft Research Anup B. Rao - Google Scholar Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. . /N 3 Honorable Mention for the 2015 ACM Doctoral Dissertation Award went to Aaron Sidford of the Massachusetts Institute of Technology, and Siavash Mirarab of the University of Texas at Austin. In Symposium on Foundations of Computer Science (FOCS 2017) (arXiv), "Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions, With Yair Carmon, John C. Duchi, and Oliver Hinder, In International Conference on Machine Learning (ICML 2017) (arXiv), Almost-Linear-Time Algorithms for Markov Chains and New Spectral Primitives for Directed Graphs, With Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, Anup B. Rao, and, Adrian Vladu, In Symposium on Theory of Computing (STOC 2017), Subquadratic Submodular Function Minimization, With Deeparnab Chakrabarty, Yin Tat Lee, and Sam Chiu-wai Wong, In Symposium on Theory of Computing (STOC 2017) (arXiv), Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More, With Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, and Adrian Vladu, In Symposium on Foundations of Computer Science (FOCS 2016) (arXiv), With Michael B. Cohen, Yin Tat Lee, Gary L. Miller, and Jakub Pachocki, In Symposium on Theory of Computing (STOC 2016) (arXiv), With Alina Ene, Gary L. Miller, and Jakub Pachocki, Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm, With Prateek Jain, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli, In Conference on Learning Theory (COLT 2016) (arXiv), Principal Component Projection Without Principal Component Analysis, With Roy Frostig, Cameron Musco, and Christopher Musco, In International Conference on Machine Learning (ICML 2016) (arXiv), Faster Eigenvector Computation via Shift-and-Invert Preconditioning, With Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, and Praneeth Netrapalli, Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. Annie Marsden. dblp: Yin Tat Lee Optimization and Algorithmic Paradigms (CS 261): Winter '23, Optimization Algorithms (CS 369O / CME 334 / MS&E 312): Fall '22, Discrete Mathematics and Algorithms (CME 305 / MS&E 315): Winter '22, '21, '20, '19, '18, Introduction to Optimization Theory (CS 269O / MS&E 213): Fall '20, '19, Spring '19, '18, '17, Almost Linear Time Graph Algorithms (CS 269G / MS&E 313): Fall '18, Winter '17. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . AISTATS, 2021. Yang P. Liu - GitHub Pages Unlike previous ADFOCS, this year the event will take place over the span of three weeks. Np%p `a!2D4! Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, Sushant Sachdeva, Online Edge Coloring via Tree Recurrences and Correlation Decay, STOC 2022 Secured intranet portal for faculty, staff and students. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). Source: www.ebay.ie David P. Woodruff - Carnegie Mellon University Neural Information Processing Systems (NeurIPS), 2014. MS&E welcomes new faculty member, Aaron Sidford ! Here are some lecture notes that I have written over the years. Jan van den Brand SHUFE, where I was fortunate MS&E213 / CS 269O - Introduction to Optimization Theory I graduated with a PhD from Princeton University in 2018. Aaron Sidford's 143 research works with 2,861 citations and 1,915 reads, including: Singular Value Approximation and Reducing Directed to Undirected Graph Sparsification 2023. . I am fortunate to be advised by Aaron Sidford . The paper, Efficient Convex Optimization Requires Superlinear Memory, was co-authored with Stanford professor Gregory Valiant as well as current Stanford student Annie Marsden and alumnus Vatsal Sharan. Yujia Jin. how . We will start with a primer week to learn the very basics of continuous optimization (July 26 - July 30), followed by two weeks of talks by the speakers on more advanced . We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. (, In Symposium on Foundations of Computer Science (FOCS 2015) (, In Conference on Learning Theory (COLT 2015) (, In International Conference on Machine Learning (ICML 2015) (, In Innovations in Theoretical Computer Science (ITCS 2015) (, In Symposium on Fondations of Computer Science (FOCS 2013) (, In Symposium on the Theory of Computing (STOC 2013) (, Book chapter in Building Bridges II: Mathematics of Laszlo Lovasz, 2020 (, Journal of Machine Learning Research, 2017 (. Yujia Jin - Stanford University We also provide two . I am an Assistant Professor in the School of Computer Science at Georgia Tech. in Mathematics and B.A. Publications | Salil Vadhan About Me. I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. I am fortunate to be advised by Aaron Sidford. With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. In submission. However, even restarting can be a hard task here. Applying this technique, we prove that any deterministic SFM algorithm . In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . rl1 Accelerated Methods for NonConvex Optimization | Semantic Scholar Personal Website. Aaron Sidford - My Group in math and computer science from Swarthmore College in 2008. Contact. My broad research interest is in theoretical computer science and my focus is on fundamental mathematical problems in data science at the intersection of computer science, statistics, optimization, biology and economics. Goethe University in Frankfurt, Germany. Roy Frostig - Stanford University Lower bounds for finding stationary points II: first-order methods. ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. Roy Frostig, Sida Wang, Percy Liang, Chris Manning. aaron sidford cv CV (last updated 01-2022): PDF Contact. ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. Full CV is available here. (ACM Doctoral Dissertation Award, Honorable Mention.) Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan Parallelizing Stochastic Gradient Descent for Least Squares Regression This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). I regularly advise Stanford students from a variety of departments. IEEE, 147-156. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. van vu professor, yale Verified email at yale.edu. publications by categories in reversed chronological order. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. with Yair Carmon, Aaron Sidford and Kevin Tian Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. [pdf] [talk] [poster] SODA 2023: 5068-5089. Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. with Yang P. Liu and Aaron Sidford. Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. Try again later. We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). Selected recent papers . Conference Publications 2023 The Complexity of Infinite-Horizon General-Sum Stochastic Games With Yujia Jin, Vidya Muthukumar, Aaron Sidford To appear in Innovations in Theoretical Computer Science (ITCS 2023) (arXiv) 2022 Optimal and Adaptive Monteiro-Svaiter Acceleration With Yair Carmon,

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