aaron sidford cv

Some I am still actively improving and all of them I am happy to continue polishing. 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. the Operations Research group. [pdf] to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration theses are protected by copyright. Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. Information about your use of this site is shared with Google. with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford The authors of most papers are ordered alphabetically. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). /Filter /FlateDecode Aaron Sidford - Stanford University /Creator (Apache FOP Version 1.0) pdf, Sequential Matrix Completion. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. [1811.10722] Solving Directed Laplacian Systems in Nearly-Linear Time Source: www.ebay.ie I was fortunate to work with Prof. Zhongzhi Zhang. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . Aviv Tamar - Reinforcement Learning Research Labs - Technion theory and graph applications. ", Applied Math at Fudan Parallelizing Stochastic Gradient Descent for Least Squares Regression Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. Associate Professor of . My research focuses on AI and machine learning, with an emphasis on robotics applications. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! I received a B.S. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. Journal of Machine Learning Research, 2017 (arXiv). [pdf] [poster] with Kevin Tian and Aaron Sidford Aaron Sidford - Selected Publications ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. 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. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. Yujia Jin - Stanford University Aaron Sidford's Homepage - Stanford University Stanford University. /Producer (Apache FOP Version 1.0) << I am Secured intranet portal for faculty, staff and students. O! Algorithms Optimization and Numerical Analysis. to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching 22nd Max Planck Advanced Course on the Foundations of Computer Science [last name]@stanford.edu where [last name]=sidford. Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. Prof. Erik Demaine TAs: Timothy Kaler, Aaron Sidford [Home] [Assignments] [Open Problems] [Accessibility] sample frame from lecture videos Data structures play a central role in modern computer science. In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) If you see any typos or issues, feel free to email me. Yujia Jin. Here are some lecture notes that I have written over the years. xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y In Sidford's dissertation, Iterative Methods, Combinatorial . My long term goal is to bring robots into human-centered domains such as homes and hospitals. Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in aaron sidford cvnatural fibrin removalnatural fibrin removal I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. I have the great privilege and good fortune of advising the following PhD students: I have also had the great privilege and good fortune of advising the following PhD students who have now graduated: Kirankumar Shiragur (co-advised with Moses Charikar) - PhD 2022, AmirMahdi Ahmadinejad (co-advised with Amin Saberi) - PhD 2020, Yair Carmon (co-advised with John Duchi) - PhD 2020. 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. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford I graduated with a PhD from Princeton University in 2018. dblp: Daogao Liu Yair Carmon. 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. A Faster Algorithm for Linear Programming and the Maximum Flow Problem II Google Scholar Digital Library; Russell Lyons and Yuval Peres. With Cameron Musco and Christopher Musco. Done under the mentorship of M. Malliaris. (, 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 (. Huang Engineering Center With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. However, even restarting can be a hard task here. aaron sidford cvis sea bass a bony fish to eat. 9-21. 2016. Source: appliancesonline.com.au. Management Science & Engineering Simple MAP inference via low-rank relaxations. Aaron Sidford | Stanford Online small tool to obtain upper bounds of such algebraic algorithms. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Applying this technique, we prove that any deterministic SFM algorithm . van vu professor, yale Verified email at yale.edu. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. Try again later. >> in Mathematics and B.A. SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. ?_l) with Aaron Sidford Anup B. Rao - Google Scholar he Complexity of Infinite-Horizon General-Sum Stochastic Games, Yujia Jin, Vidya Muthukumar, Aaron Sidford, Innovations in Theoretical Computer Science (ITCS 202, air Carmon, Danielle Hausler, Arun Jambulapati, and Yujia Jin, Advances in Neural Information Processing Systems (NeurIPS 2022), Moses Charikar, Zhihao Jiang, and Kirankumar Shiragur, Advances in Neural Information Processing Systems (NeurIPS 202, n Symposium on Foundations of Computer Science (FOCS 2022) (, International Conference on Machine Learning (ICML 2022) (, Conference on Learning Theory (COLT 2022) (, International Colloquium on Automata, Languages and Programming (ICALP 2022) (, In Symposium on Theory of Computing (STOC 2022) (, In Symposium on Discrete Algorithms (SODA 2022) (, In Advances in Neural Information Processing Systems (NeurIPS 2021) (, In Conference on Learning Theory (COLT 2021) (, In International Conference on Machine Learning (ICML 2021) (, In Symposium on Theory of Computing (STOC 2021) (, In Symposium on Discrete Algorithms (SODA 2021) (, In Innovations in Theoretical Computer Science (ITCS 2021) (, In Conference on Neural Information Processing Systems (NeurIPS 2020) (, In Symposium on Foundations of Computer Science (FOCS 2020) (, In International Conference on Artificial Intelligence and Statistics (AISTATS 2020) (, In International Conference on Machine Learning (ICML 2020) (, In Conference on Learning Theory (COLT 2020) (, In Symposium on Theory of Computing (STOC 2020) (, In International Conference on Algorithmic Learning Theory (ALT 2020) (, In Symposium on Discrete Algorithms (SODA 2020) (, In Conference on Neural Information Processing Systems (NeurIPS 2019) (, In Symposium on Foundations of Computer Science (FOCS 2019) (, In Conference on Learning Theory (COLT 2019) (, In Symposium on Theory of Computing (STOC 2019) (, In Symposium on Discrete Algorithms (SODA 2019) (, In Conference on Neural Information Processing Systems (NeurIPS 2018) (, In Symposium on Foundations of Computer Science (FOCS 2018) (, In Conference on Learning Theory (COLT 2018) (, In Symposium on Discrete Algorithms (SODA 2018) (, In Innovations in Theoretical Computer Science (ITCS 2018) (, In Symposium on Foundations of Computer Science (FOCS 2017) (, In International Conference on Machine Learning (ICML 2017) (, In Symposium on Theory of Computing (STOC 2017) (, In Symposium on Foundations of Computer Science (FOCS 2016) (, In Symposium on Theory of Computing (STOC 2016) (, In Conference on Learning Theory (COLT 2016) (, In International Conference on Machine Learning (ICML 2016) (, In International Conference on Machine Learning (ICML 2016). Unlike previous ADFOCS, this year the event will take place over the span of three weeks. Sivakanth Gopi at Microsoft Research Aaron's research interests lie in optimization, the theory of computation, and the . with Yair Carmon, Arun Jambulapati and Aaron Sidford "t a","H We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University 2021 - 2022 Postdoc, Simons Institute & UC . Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA 4026. NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games Articles Cited by Public access. I am broadly interested in mathematics and theoretical computer science. About - Annie Marsden Contact. Lower Bounds for Finding Stationary Points II: First-Order Methods Faster Matroid Intersection Princeton University ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. Roy Frostig, Sida Wang, Percy Liang, Chris Manning. MS&E welcomes new faculty member, Aaron Sidford ! This is the academic homepage of Yang Liu (I publish under Yang P. Liu). Google Scholar; Probability on trees and . Office: 380-T Alcatel One Touch Flip Phone - New Product Recommendations, Promotions Annie Marsden. F+s9H Main Menu. Allen Liu - GitHub Pages Aaron Sidford (sidford@stanford.edu) Welcome This page has informatoin and lecture notes from the course "Introduction to Optimization Theory" (MS&E213 / CS 269O) which I taught in Fall 2019. aaron sidford cv Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . By using this site, you agree to its use of cookies. 113 * 2016: The system can't perform the operation now. Personal Website. Selected recent papers . Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. with Yair Carmon, Kevin Tian and Aaron Sidford Research Institute for Interdisciplinary Sciences (RIIS) at The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games Before joining Stanford in Fall 2016, I was an NSF post-doctoral fellow at Carnegie Mellon University ; I received a Ph.D. in mathematics from the University of Michigan in 2014, and a B.A. 2023. . Yang P. Liu - GitHub Pages Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. SODA 2023: 5068-5089. I regularly advise Stanford students from a variety of departments. ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! 2013. 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. . Our algorithm combines the derandomized square graph operation (Rozenman and Vadhan, 2005), which we recently used for solving Laplacian systems in nearly logarithmic space (Murtagh, Reingold, Sidford, and Vadhan, 2017), with ideas from (Cheng, Cheng, Liu, Peng, and Teng, 2015), which gave an algorithm that is time-efficient (while ours is . Mary Wootters - Google by Aaron Sidford. /N 3 Here are some lecture notes that I have written over the years. Adam Bouland - Stanford University CSE 535: Theory of Optimization and Continuous Algorithms - Yin Tat Anup B. Rao. what is a blind trust for lottery winnings; ithaca college park school scholarships; >> With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. << } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ [pdf] She was 19 years old and looking forward to the start of classes and reuniting with her college pals. of practical importance. (arXiv), A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization, In Symposium on Foundations of Computer Science (FOCS 2015), Machtey Award for Best Student Paper (arXiv), Efficient Inverse Maintenance and Faster Algorithms for Linear Programming, In Symposium on Foundations of Computer Science (FOCS 2015) (arXiv), Competing with the Empirical Risk Minimizer in a Single Pass, With Roy Frostig, Rong Ge, and Sham Kakade, In Conference on Learning Theory (COLT 2015) (arXiv), Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization, In International Conference on Machine Learning (ICML 2015) (arXiv), Uniform Sampling for Matrix Approximation, With Michael B. Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, and Richard Peng, In Innovations in Theoretical Computer Science (ITCS 2015) (arXiv), Path-Finding Methods for Linear Programming : Solving Linear Programs in (rank) Iterations and Faster Algorithms for Maximum Flow, In Symposium on Foundations of Computer Science (FOCS 2014), Best Paper Award and Machtey Award for Best Student Paper (arXiv), Single Pass Spectral Sparsification in Dynamic Streams, With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco, An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations, With Jonathan A. Kelner, Yin Tat Lee, and Lorenzo Orecchia, In Symposium on Discrete Algorithms (SODA 2014), Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems, In Symposium on Fondations of Computer Science (FOCS 2013) (arXiv), A Simple, Combinatorial Algorithm for Solving SDD Systems in Nearly-Linear Time, With Jonathan A. Kelner, Lorenzo Orecchia, and Zeyuan Allen Zhu, In Symposium on the Theory of Computing (STOC 2013) (arXiv), SIAM Journal on Computing (arXiv before merge), Derandomization beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space, With Jack Murtagh, Omer Reingold, and Salil Vadhan, Book chapter in Building Bridges II: Mathematics of Laszlo Lovasz, 2020 (arXiv), Lower Bounds for Finding Stationary Points II: First-Order Methods. I am fortunate to be advised by Aaron Sidford . Gregory Valiant Homepage - Stanford University Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. Goethe University in Frankfurt, Germany. Sequential Matrix Completion. Before attending Stanford, I graduated from MIT in May 2018. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization .

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