With Cameron Musco and Christopher Musco. Anup B. Rao. There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. Some I am still actively improving and all of them I am happy to continue polishing. endobj 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. Page 1 of 5 Aaron Sidford Assistant Professor of Management Science and Engineering and of Computer Science CONTACT INFORMATION Administrative Contact Jackie Nguyen - Administrative Associate [name] = yangpliu, Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, Online Edge Coloring via Tree Recurrences and Correlation Decay, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, Discrepancy Minimization via a Self-Balancing Walk, Faster Divergence Maximization for Faster Maximum Flow. I graduated with a PhD from Princeton University in 2018. Enrichment of Network Diagrams for Potential Surfaces. NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. pdf, Sequential Matrix Completion. Np%p `a!2D4! Email / Prior to that, I received an MPhil in Scientific Computing at the University of Cambridge on a Churchill Scholarship where I was advised by Sergio Bacallado. Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang: Minimum Cost Flows, MDPs, and 1 -Regression in Nearly Linear Time for Dense Instances. AISTATS, 2021. Aleksander Mdry; Generalized preconditioning and network flow problems /CreationDate (D:20230304061109-08'00') STOC 2023. In this talk, I will present a new algorithm for solving linear programs. [pdf] I enjoy understanding the theoretical ground of many algorithms that are sidford@stanford.edu. "I am excited to push the theory of optimization and algorithm design to new heights!" Assistant Professor Aaron Sidford speaks at ICME's Xpo event. Google Scholar Digital Library; Russell Lyons and Yuval Peres. % Sequential Matrix Completion. D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford Their, This "Cited by" count includes citations to the following articles in Scholar. 2021. /N 3 Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . [pdf] how . O! Simple MAP inference via low-rank relaxations. Secured intranet portal for faculty, staff and students. Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. " 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. with Vidya Muthukumar and Aaron Sidford Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent BayLearn, 2019, "Computing stationary solution for multi-agent RL is hard: Indeed, CCE for simultaneous games and NE for turn-based games are both PPAD-hard. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. [pdf] [poster] 2017. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. Best Paper Award. 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. /Length 11 0 R Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. [pdf] [talk] [poster] stream [pdf] [talk] 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 ", "A short version of the conference publication under the same title. MI #~__ Q$.R$sg%f,a6GTLEQ!/B)EogEA?l kJ^- \?l{ P&d\EAt{6~/fJq2bFn6g0O"yD|TyED0Ok-\~[`|4P,w\A8vD$+)%@P4 0L ` ,\@2R 4f Computer Science. 9-21. %PDF-1.4 Conference Publications 2023 The Complexity of Infinite-Horizon General-Sum Stochastic Games With Yujia Jin, Vidya Muthukumar, Aaron Sidford To appear in Innovations in Theoretical Computer Science (ITCS 2023) (arXiv) 2022 Optimal and Adaptive Monteiro-Svaiter Acceleration With Yair Carmon, SODA 2023: 4667-4767. ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games Personal Website. ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. Aaron Sidford. Here are some lecture notes that I have written over the years. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods en_US: dc.format.extent: 266 pages: en_US: dc.language.iso: eng: en_US: dc.publisher: Massachusetts Institute of Technology: en_US: dc.rights: M.I.T. In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ 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 . AISTATS, 2021. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. 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. Articles 1-20. We forward in this generation, Triumphantly. 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. KTH in Stockholm, Sweden, and my BSc + MSc at the Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. Associate Professor of . Here is a slightly more formal third-person biography, and here is a recent-ish CV. Thesis, 2016. pdf. Towards this goal, some fundamental questions need to be solved, such as how can machines learn models of their environments that are useful for performing tasks . 4 0 obj 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. One research focus are dynamic algorithms (i.e. I am broadly interested in mathematics and theoretical computer science. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 [pdf] [talk] [poster] /Creator (Apache FOP Version 1.0) Source: www.ebay.ie In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& July 8, 2022. My research focuses on AI and machine learning, with an emphasis on robotics applications. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. Aaron's research interests lie in optimization, the theory of computation, and the . Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 Group Resources. Information about your use of this site is shared with Google. In International Conference on Machine Learning (ICML 2016). Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss Email: [name]@stanford.edu {{{;}#q8?\. Yang P. Liu, Aaron Sidford, Department of Mathematics "t a","H (, 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 (. View Full Stanford Profile. [pdf] [talk] 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 . 475 Via Ortega Slides from my talk at ITCS. In submission. with Aaron Sidford 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 . With Yair Carmon, John C. Duchi, and Oliver Hinder. In each setting we provide faster exact and approximate algorithms. >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. Alcatel flip phones are also ready to purchase with consumer cellular. ReSQueing Parallel and Private Stochastic Convex Optimization. F+s9H xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. Improved Lower Bounds for Submodular Function Minimization. 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. [pdf] [slides] ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. This site uses cookies from Google to deliver its services and to analyze traffic. Huang Engineering Center It was released on november 10, 2017. University of Cambridge MPhil. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. /Filter /FlateDecode Management Science & Engineering ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). publications by categories in reversed chronological order. Contact. By using this site, you agree to its use of cookies. ?_l) ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. Google Scholar; Probability on trees and . Research Institute for Interdisciplinary Sciences (RIIS) at Verified email at stanford.edu - Homepage. A nearly matching upper and lower bound for constant error here! I am Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . 2013. rl1 Summer 2022: I am currently a research scientist intern at DeepMind in London. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. Annie Marsden. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. Before attending Stanford, I graduated from MIT in May 2018. SHUFE, where I was fortunate Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries The authors of most papers are ordered alphabetically. Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. 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 I am fortunate to be advised by Aaron Sidford. We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. COLT, 2022. what is a blind trust for lottery winnings; ithaca college park school scholarships; 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. Email: sidford@stanford.edu. The system can't perform the operation now. arXiv preprint arXiv:2301.00457, 2023 arXiv. [pdf] Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. of practical importance. I am an Assistant Professor in the School of Computer Science at Georgia Tech. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . 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. I am broadly interested in mathematics and theoretical computer science. Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions In Symposium on Discrete Algorithms (SODA 2018) (arXiv), Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes, Efficient (n/) Spectral Sketches for the Laplacian and its Pseudoinverse, Stability of the Lanczos Method for Matrix Function Approximation. Eigenvalues of the laplacian and their relationship to the connectedness of a graph. [pdf] [talk] [poster] [pdf] [poster] with Yair Carmon, Aaron Sidford and Kevin Tian 4026. Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper Algorithms Optimization and Numerical Analysis. Follow. ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! Yin Tat Lee and Aaron Sidford. ", Applied Math at Fudan Yujia Jin. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! Lower bounds for finding stationary points II: first-order methods. 113 * 2016: The system can't perform the operation now. with Yair Carmon, Kevin Tian and Aaron Sidford CoRR abs/2101.05719 ( 2021 ) Yair Carmon. My CV. << Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. Improves the stochas-tic convex optimization problem in parallel and DP setting. 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. Goethe University in Frankfurt, Germany. [pdf] aaron sidford cvnatural fibrin removalnatural fibrin removal Neural Information Processing Systems (NeurIPS), 2014. Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods [pdf] [poster] 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. Links. (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. I also completed my undergraduate degree (in mathematics) at MIT. IEEE, 147-156. 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. Applying this technique, we prove that any deterministic SFM algorithm . Aaron Sidford. 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. ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. Faculty Spotlight: Aaron Sidford. 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. Roy Frostig, Sida Wang, Percy Liang, Chris Manning. Faculty and Staff Intranet. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. Semantic parsing on Freebase from question-answer pairs. resume/cv; publications. in Chemistry at the University of Chicago. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. in Mathematics and B.A. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. van vu professor, yale Verified email at yale.edu. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). Yujia Jin. with Aaron Sidford [pdf] I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in Our method improves upon the convergence rate of previous state-of-the-art linear programming . Instructor: Aaron Sidford Winter 2018 Time: Tuesdays and Thursdays, 10:30 AM - 11:50 AM Room: Education Building, Room 128 Here is the course syllabus. Try again later. with Aaron Sidford With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Efficient Convex Optimization Requires Superlinear Memory. CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. 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. Title. International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. Allen Liu. with Yair Carmon, Arun Jambulapati and Aaron Sidford (ACM Doctoral Dissertation Award, Honorable Mention.) Intranet Web Portal. . February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs We prove that deterministic first-order methods, even applied to arbitrarily smooth functions, cannot achieve convergence rates in $$ better than $^{-8/5}$, which is within $^{-1/15}\\log\\frac{1}$ of the best known rate for such . Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization Try again later. Call (225) 687-7590 or park nicollet dermatology wayzata today! to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. I often do not respond to emails about applications. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). Aaron Sidford is an Assistant Professor of Management Science and Engineering at Stanford University, where he also has a courtesy appointment in Computer Science and an affiliation with the Institute for Computational and Mathematical Engineering (ICME). 2016. 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. Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). in math and computer science from Swarthmore College in 2008. 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. 2016. If you see any typos or issues, feel free to email me. The site facilitates research and collaboration in academic endeavors. [last name]@stanford.edu where [last name]=sidford. >> Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. Done under the mentorship of M. Malliaris. 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). I completed my PhD at data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. We also provide two . Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together.
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