aaron sidford cvaaron sidford cv

Allen Liu - GitHub Pages Fresh Faculty: Theoretical computer scientist Aaron Sidford joins MS&E 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. "t a","H July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. University, Research Institute for Interdisciplinary Sciences (RIIS) at SODA 2023: 4667-4767. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). van vu professor, yale Verified email at yale.edu. Iterative methods, combinatorial optimization, and linear programming 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. Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." The design of algorithms is traditionally a discrete endeavor. Stanford University. Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space [pdf] [pdf] [poster] Aaron Sidford - All Publications 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. Navajo Math Circles Instructor. With Yair Carmon, John C. Duchi, and Oliver Hinder. Management Science & Engineering I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. (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. (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. Aaron Sidford. In September 2018, I started a PhD at Stanford University in mathematics, and am 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. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods 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. stream My research was supported by the National Defense Science and Engineering Graduate (NDSEG) Fellowship from 2018-2021, and by a Google PhD Fellowship from 2022-2023. >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. My research focuses on AI and machine learning, with an emphasis on robotics applications. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! Aaron Sidford ([email protected]) 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. SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. with Aaron Sidford We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). I received a B.S. Sequential Matrix Completion. Publications | Salil Vadhan ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. Lower bounds for finding stationary points II: first-order methods. Aaron Sidford's Homepage - Stanford University the Operations Research group. David P. Woodruff . I am a senior researcher in the Algorithms group at Microsoft Research Redmond. Yujia Jin. [pdf] [poster] Google Scholar; Probability on trees and . By using this site, you agree to its use of cookies. Aleksander Mdry; Generalized preconditioning and network flow problems Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) Selected recent papers . which is why I created a Aaron Sidford - Teaching with Aaron Sidford 4 0 obj data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). 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 . This work presents an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second derivatives that is Hessian free, i.e., it only requires gradient computations, and is therefore suitable for large-scale applications. We also provide two . [1811.10722] Solving Directed Laplacian Systems in Nearly-Linear Time ICML, 2016. Articles Cited by Public access. It was released on november 10, 2017. Mail Code. publications | Daogao Liu Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. ReSQueing Parallel and Private Stochastic Convex Optimization. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration Roy Frostig, Sida Wang, Percy Liang, Chris Manning. . A Faster Algorithm for Linear Programming and the Maximum Flow Problem II If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& << by Aaron Sidford. DOI: 10.1109/FOCS.2016.69 Corpus ID: 3311; Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More @article{Cohen2016FasterAF, title={Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More}, author={Michael B. Cohen and Jonathan A. Kelner and John Peebles and Richard Peng and Aaron Sidford and Adrian Vladu}, journal . To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. Adam Bouland - Stanford University About Me. She was 19 years old and looking - freewareppc.com With Cameron Musco and Christopher Musco. F+s9H aaron sidford cvis sea bass a bony fish to eat. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. Selected for oral presentation. The site facilitates research and collaboration in academic endeavors. Faculty Spotlight: Aaron Sidford - Management Science and Engineering I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. The authors of most papers are ordered alphabetically. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. Links. 2023. . 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 with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian Email: [name]@stanford.edu From 2016 to 2018, I also worked in Intranet Web Portal. Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA I am broadly interested in mathematics and theoretical computer science. Here are some lecture notes that I have written over the years. 9-21. aaron sidford cv 5 0 obj Another research focus are optimization algorithms. [pdf] what is a blind trust for lottery winnings; ithaca college park school scholarships; Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent Algorithms Optimization and Numerical Analysis. D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. 2021. theory and graph applications. dblp: Yin Tat Lee [PDF] Faster Algorithms for Computing the Stationary Distribution Yair Carmon. Aaron's research interests lie in optimization, the theory of computation, and the . AISTATS, 2021. We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). I am Verified email at stanford.edu - Homepage. 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 Abstract. Enrichment of Network Diagrams for Potential Surfaces. 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. Alcatel flip phones are also ready to purchase with consumer cellular. << MS&E213 / CS 269O - Introduction to Optimization Theory with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. Faculty and Staff Intranet. I am fortunate to be advised by Aaron Sidford . ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! resume/cv; publications. CV (last updated 01-2022): PDF Contact. She was 19 years old and looking forward to the start of classes and reuniting with her college pals. I also completed my undergraduate degree (in mathematics) at MIT. [pdf] [talk] [poster] Here are some lecture notes that I have written over the years. United States. Publications and Preprints. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford {{{;}#q8?\. Huang Engineering Center Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). aaron sidford cvnatural fibrin removalnatural fibrin removal Neural Information Processing Systems (NeurIPS), 2014. International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) Cameron Musco - Manning College of Information & Computer Sciences ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. Given a linear program with n variables, m > n constraints, and bit complexity L, our algorithm runs in (sqrt(n) L) iterations each consisting of solving (1) linear systems and additional nearly linear time computation. IEEE, 147-156. Yujia Jin. O! Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. In this talk, I will present a new algorithm for solving linear programs. Their, This "Cited by" count includes citations to the following articles in Scholar. Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. in math and computer science from Swarthmore College in 2008. /Creator (Apache FOP Version 1.0) [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. 22nd Max Planck Advanced Course on the Foundations of Computer Science ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian Efficient Convex Optimization Requires Superlinear Memory. However, many advances have come from a continuous viewpoint. Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. [pdf] [poster] Improves the stochas-tic convex optimization problem in parallel and DP setting. [pdf] 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. with Yair Carmon, Aaron Sidford and Kevin Tian ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). when do tulips bloom in maryland; indo pacific region upsc with Vidya Muthukumar and Aaron Sidford [pdf] Google Scholar Digital Library; Russell Lyons and Yuval Peres. aaron sidford cv natural fibrin removal - libiot.kku.ac.th Aaron Sidford | Management Science and Engineering with Yang P. Liu and Aaron Sidford. Kirankumar Shiragur | Data Science Aaron Sidford - live-simons-institute.pantheon.berkeley.edu Unlike previous ADFOCS, this year the event will take place over the span of three weeks. My CV. Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. endobj /Length 11 0 R 2022 - Learning and Games Program, Simons Institute, Sept. 2021 - Young Researcher Workshop, Cornell ORIE, Sept. 2021 - ACO Student Seminar, Georgia Tech, Dec. 2019 - NeurIPS Spotlight presentation. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . . In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . Yang P. Liu - GitHub Pages 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. with Arun Jambulapati, Aaron Sidford and Kevin Tian Applying this technique, we prove that any deterministic SFM algorithm . I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. Contact. ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. [last name]@stanford.edu where [last name]=sidford. Advanced Data Structures (6.851) - Massachusetts Institute of Technology The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. with Aaron Sidford One research focus are dynamic algorithms (i.e. Etude for the Park City Math Institute Undergraduate Summer School. Conference on Learning Theory (COLT), 2015. KTH in Stockholm, Sweden, and my BSc + MSc at the Accelerated Methods for NonConvex Optimization | Semantic Scholar Gregory Valiant Homepage - Stanford University Annie Marsden. [pdf] [poster] Aaron Sidford. Email: [email protected]. Publications | Jakub Pachocki - Harvard University Faculty Spotlight: Aaron Sidford. to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching
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