I completed my PhD at
David P. Woodruff - Carnegie Mellon University Authors: Michael B. Cohen, Jonathan Kelner, Rasmus Kyng, John Peebles, Richard Peng, Anup B. Rao, Aaron Sidford Download PDF Abstract: We show how to solve directed Laplacian systems in nearly-linear time. Their, This "Cited by" count includes citations to the following articles in Scholar.
I am an Assistant Professor in the School of Computer Science at Georgia Tech. The design of algorithms is traditionally a discrete endeavor. Aaron Sidford | Stanford Online Source: www.ebay.ie Before attending Stanford, I graduated from MIT in May 2018. with Aaron Sidford
I often do not respond to emails about applications. Articles 1-20. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . Jan van den Brand 22nd Max Planck Advanced Course on the Foundations of Computer Science when do tulips bloom in maryland; indo pacific region upsc Applying this technique, we prove that any deterministic SFM algorithm . In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs
Another research focus are optimization algorithms. The authors of most papers are ordered alphabetically. " Geometric median in nearly linear time ." In Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2016, Cambridge, MA, USA, June 18-21, 2016, Pp. Parallelizing Stochastic Gradient Descent for Least Squares Regression 2021. Aaron Sidford's research works | Stanford University, CA (SU) and other This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). ", "Team-convex-optimization for solving discounted and average-reward MDPs! Gregory Valiant Homepage - Stanford University In Sidford's dissertation, Iterative Methods, Combinatorial .
Secured intranet portal for faculty, staff and students. With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli.
Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games
/Filter /FlateDecode Roy Frostig - Stanford University I am broadly interested in mathematics and theoretical computer science. I hope you enjoy the content as much as I enjoyed teaching the class and if you have questions or feedback on the note, feel free to email me. Yin Tat Lee and Aaron Sidford. to be advised by Prof. Dongdong Ge. Allen Liu.
>> In each setting we provide faster exact and approximate algorithms. with Yair Carmon, Kevin Tian and Aaron Sidford
Adam Bouland - Stanford University If you see any typos or issues, feel free to email me. Iterative methods, combinatorial optimization, and linear programming Here are some lecture notes that I have written over the years. Aaron Sidford - Stanford University Aaron Sidford's Profile | Stanford Profiles Cameron Musco - Manning College of Information & Computer Sciences [pdf]
I am theses are protected by copyright. Associate Professor of . By using this site, you agree to its use of cookies. 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. Sidford received his PhD from the department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology where he was advised by Professor Jonathan Kelner. Before Stanford, I worked with John Lafferty at the University of Chicago. /CreationDate (D:20230304061109-08'00') Allen Liu - GitHub Pages My research focuses on AI and machine learning, with an emphasis on robotics applications. Microsoft Research Faculty Fellowship 2020: Researchers in academia at
to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching
I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Selected recent papers . Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. ReSQueing Parallel and Private Stochastic Convex Optimization. %PDF-1.4 Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper The site facilitates research and collaboration in academic endeavors.
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Slides from my talk at ITCS.
Source: appliancesonline.com.au. [pdf] [talk] [poster]
[PDF] Faster Algorithms for Computing the Stationary Distribution "FV %H"Hr
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stream If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. Aaron Sidford's Homepage - Stanford University ", "Sample complexity for average-reward MDPs? 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). /N 3 Lower bounds for finding stationary points I, Accelerated Methods for NonConvex Optimization, SIAM Journal on Optimization, 2018 (arXiv), Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification. From 2016 to 2018, I also worked in
In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. Roy Frostig, Sida Wang, Percy Liang, Chris Manning. View Full Stanford Profile. % Try again later. 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. Contact. small tool to obtain upper bounds of such algebraic algorithms. aaron sidford cvnatural fibrin removalnatural fibrin removal Personal Website.
[pdf] [talk] [poster]
<< ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! Links. Many of my results use fast matrix multiplication
", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss
University of Cambridge MPhil. The following articles are merged in Scholar.
. [pdf]
dblp: Yin Tat Lee (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. CV (last updated 01-2022): PDF Contact. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020
how . Group Resources. publications | Daogao Liu dblp: Daogao Liu Efficient Convex Optimization Requires Superlinear Memory. (, 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 (. with Aaron Sidford
", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). /Creator (Apache FOP Version 1.0) endobj
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. Verified email at stanford.edu - Homepage. 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 . Aleksander Mdry; Generalized preconditioning and network flow problems 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! David P. Woodruff . Enrichment of Network Diagrams for Potential Surfaces. 113 * 2016: The system can't perform the operation now. Google Scholar, The Complexity of Infinite-Horizon General-Sum Stochastic Games, The Complexity of Optimizing Single and Multi-player Games, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions, On the Sample Complexity for Average-reward Markov Decision Processes, Stochastic Methods for Matrix Games and its Applications, Acceleration with a Ball Optimization Oracle, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG, The Complexity of Infinite-Horizon General-Sum Stochastic Games
Conference on Learning Theory (COLT), 2015. theory and graph applications. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. This improves upon previous best known running times of O (nr1.5T-ind) due to Cunningham in 1986 and (n2T-ind+n3) due to Lee, Sidford, and Wong in 2015.