With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. Applying this technique, we prove that any deterministic SFM algorithm . One research focus are dynamic algorithms (i.e. Aleksander Mdry; Generalized preconditioning and network flow problems Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Best Paper Award. in Mathematics and B.A. aaron sidford cvnatural fibrin removalnatural fibrin removal The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. I regularly advise Stanford students from a variety of departments. Full CV is available here. February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. [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. with Yair Carmon, Aaron Sidford and Kevin Tian Google Scholar Digital Library; Russell Lyons and Yuval Peres. Here is a slightly more formal third-person biography, and here is a recent-ish CV. [pdf] Efficient Convex Optimization Requires Superlinear Memory. SODA 2023: 4667-4767. 4026. arXiv preprint arXiv:2301.00457, 2023 arXiv. 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. Aaron Sidford. theory and graph applications. Stanford University. 2013. The authors of most papers are ordered alphabetically. Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford. Aaron Sidford. Sequential Matrix Completion. We forward in this generation, Triumphantly. ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. Assistant Professor of Management Science and Engineering and of Computer Science. Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. I also completed my undergraduate degree (in mathematics) at MIT. 9-21. Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . CV (last updated 01-2022): PDF Contact. Many of my results use fast matrix multiplication Selected recent papers . publications by categories in reversed chronological order. ", "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 %PDF-1.4 Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. with Yair Carmon, Kevin Tian and Aaron Sidford (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. KTH in Stockholm, Sweden, and my BSc + MSc at the Some I am still actively improving and all of them I am happy to continue polishing. 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. ICML, 2016. ReSQueing Parallel and Private Stochastic Convex Optimization. The design of algorithms is traditionally a discrete endeavor. 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. with Aaron Sidford 2017. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration with Aaron Sidford If you see any typos or issues, feel free to email me. Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). with Yair Carmon, Arun Jambulapati and Aaron Sidford [pdf] [talk] [poster] Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space Information about your use of this site is shared with Google. Publications and Preprints. (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. Roy Frostig, Sida Wang, Percy Liang, Chris Manning. [last name]@stanford.edu where [last name]=sidford. 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), Optimal and Adaptive Monteiro-Svaiter Acceleration, With Yair Carmon, Danielle Hausler, Arun Jambulapati, and Yujia Jin, To appear in Advances in Neural Information Processing Systems (NeurIPS 2022) (arXiv), On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood, With Moses Charikar, Zhihao Jiang, and Kirankumar Shiragur, Improved Lower Bounds for Submodular Function Minimization, With Deeparnab Chakrabarty, Andrei Graur, and Haotian Jiang, In Symposium on Foundations of Computer Science (FOCS 2022) (arXiv), RECAPP: Crafting a More Efficient Catalyst for Convex Optimization, With Yair Carmon, Arun Jambulapati, and Yujia Jin, International Conference on Machine Learning (ICML 2022) (arXiv), Efficient Convex Optimization Requires Superlinear Memory, With Annie Marsden, Vatsal Sharan, and Gregory Valiant, Conference on Learning Theory (COLT 2022), Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Method, Conference on Learning Theory (COLT 2022) (arXiv), Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales, With Jonathan A. Kelner, Annie Marsden, Vatsal Sharan, Gregory Valiant, and Honglin Yuan, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching, With Arun Jambulapati, Yujia Jin, and Kevin Tian, International Colloquium on Automata, Languages and Programming (ICALP 2022) (arXiv), Fully-Dynamic Graph Sparsifiers Against an Adaptive Adversary, With Aaron Bernstein, Jan van den Brand, Maximilian Probst, Danupon Nanongkai, Thatchaphol Saranurak, and He Sun, Faster Maxflow via Improved Dynamic Spectral Vertex Sparsifiers, With Jan van den Brand, Yu Gao, Arun Jambulapati, Yin Tat Lee, Yang P. Liu, and Richard Peng, In Symposium on Theory of Computing (STOC 2022) (arXiv), Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space, With Sepehr Assadi, Arun Jambulapati, Yujia Jin, and Kevin Tian, In Symposium on Discrete Algorithms (SODA 2022) (arXiv), Algorithmic trade-offs for girth approximation in undirected graphs, With Avi Kadria, Liam Roditty, Virginia Vassilevska Williams, and Uri Zwick, In Symposium on Discrete Algorithms (SODA 2022), Computing Lewis Weights to High Precision, With Maryam Fazel, Yin Tat Lee, and Swati Padmanabhan, With Hilal Asi, Yair Carmon, Arun Jambulapati, and Yujia Jin, In Advances in Neural Information Processing Systems (NeurIPS 2021) (arXiv), Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss, In Conference on Learning Theory (COLT 2021) (arXiv), The Bethe and Sinkhorn Permanents of Low Rank Matrices and Implications for Profile Maximum Likelihood, With Nima Anari, Moses Charikar, and Kirankumar Shiragur, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs, In International Conference on Machine Learning (ICML 2021) (arXiv), Minimum cost flows, MDPs, and 1-regression in nearly linear time for dense instances, With Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, and Zhao Song, Di Wang, In Symposium on Theory of Computing (STOC 2021) (arXiv), Ultrasparse Ultrasparsifiers and Faster Laplacian System Solvers, In Symposium on Discrete Algorithms (SODA 2021) (arXiv), Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration, In Innovations in Theoretical Computer Science (ITCS 2021) (arXiv), Acceleration with a Ball Optimization Oracle, With Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, and Kevin Tian, In Conference on Neural Information Processing Systems (NeurIPS 2020), Instance Based Approximations to Profile Maximum Likelihood, In Conference on Neural Information Processing Systems (NeurIPS 2020) (arXiv), Large-Scale Methods for Distributionally Robust Optimization, With Daniel Levy*, Yair Carmon*, and John C. Duch (* denotes equal contribution), High-precision Estimation of Random Walks in Small Space, With AmirMahdi Ahmadinejad, Jonathan A. Kelner, Jack Murtagh, John Peebles, and Salil P. Vadhan, In Symposium on Foundations of Computer Science (FOCS 2020) (arXiv), Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs, With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang, In Symposium on Foundations of Computer Science (FOCS 2020), With Yair Carmon, Yujia Jin, and Kevin Tian, Unit Capacity Maxflow in Almost $O(m^{4/3})$ Time, Invited to the special issue (arXiv before merge)), Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity, In International Conference on Artificial Intelligence and Statistics (AISTATS 2020) (arXiv), Efficiently Solving MDPs with Stochastic Mirror Descent, In International Conference on Machine Learning (ICML 2020) (arXiv), Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond, With Oliver Hinder and Nimit Sharad Sohoni, In Conference on Learning Theory (COLT 2020) (arXiv), Solving Tall Dense Linear Programs in Nearly Linear Time, With Jan van den Brand, Yin Tat Lee, and Zhao Song, In Symposium on Theory of Computing (STOC 2020). David P. Woodruff . << 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. I am fortunate to be advised by Aaron Sidford. 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 Our method improves upon the convergence rate of previous state-of-the-art linear programming . Email: [name]@stanford.edu Np%p `a!2D4! Improves the stochas-tic convex optimization problem in parallel and DP setting. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries I enjoy understanding the theoretical ground of many algorithms that are Secured intranet portal for faculty, staff and students. ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. 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, Title. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . I often do not respond to emails about applications. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. The following articles are merged in Scholar. Faculty Spotlight: Aaron Sidford. Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. 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 Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y Their, This "Cited by" count includes citations to the following articles in Scholar. [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. " 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. . 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). 113 * 2016: The system can't perform the operation now. Follow. 2021. /Producer (Apache FOP Version 1.0) ", "Sample complexity for average-reward MDPs? Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. Alcatel flip phones are also ready to purchase with consumer cellular. [pdf] [talk] 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 Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. Google Scholar; Probability on trees and . I am an Assistant Professor in the School of Computer Science at Georgia Tech. with Aaron Sidford Unlike previous ADFOCS, this year the event will take place over the span of three weeks. 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 . We also provide two . >> A nearly matching upper and lower bound for constant error here! Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. In this talk, I will present a new algorithm for solving linear programs. 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 . ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! The ones marked, 2014 IEEE 55th Annual Symposium on Foundations of Computer Science, 424-433, SIAM Journal on Optimization 28 (2), 1751-1772, Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 1049-1065, 2013 ieee 54th annual symposium on foundations of computer science, 147-156, Proceedings of the forty-fifth annual ACM symposium on Theory of computing, MB Cohen, YT Lee, C Musco, C Musco, R Peng, A Sidford, Proceedings of the 2015 Conference on Innovations in Theoretical Computer, Advances in Neural Information Processing Systems 31, M Kapralov, YT Lee, CN Musco, CP Musco, A Sidford, SIAM Journal on Computing 46 (1), 456-477, P Jain, S Kakade, R Kidambi, P Netrapalli, A Sidford, MB Cohen, YT Lee, G Miller, J Pachocki, A Sidford, Proceedings of the forty-eighth annual ACM symposium on Theory of Computing, International Conference on Machine Learning, 2540-2548, P Jain, SM Kakade, R Kidambi, P Netrapalli, A Sidford, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 230-249, Mathematical Programming 184 (1-2), 71-120, P Jain, C Jin, SM Kakade, P Netrapalli, A Sidford, International conference on machine learning, 654-663, Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete, D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford, New articles related to this author's research, Path finding methods for linear programming: Solving linear programs in o (vrank) iterations and faster algorithms for maximum flow, Accelerated methods for nonconvex optimization, An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations, A faster cutting plane method and its implications for combinatorial and convex optimization, Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems, A simple, combinatorial algorithm for solving SDD systems in nearly-linear time, Uniform sampling for matrix approximation, Near-optimal time and sample complexities for solving Markov decision processes with a generative model, Single pass spectral sparsification in dynamic streams, Parallelizing stochastic gradient descent for least squares regression: mini-batching, averaging, and model misspecification, Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization, Accelerating stochastic gradient descent for least squares regression, Efficient inverse maintenance and faster algorithms for linear programming, Lower bounds for finding stationary points I, Streaming pca: Matching matrix bernstein and near-optimal finite sample guarantees for ojas algorithm, Convex Until Proven Guilty: Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions, Competing with the empirical risk minimizer in a single pass, Variance reduced value iteration and faster algorithms for solving Markov decision processes, Robust shift-and-invert preconditioning: Faster and more sample efficient algorithms for eigenvector computation. ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . resume/cv; publications. This site uses cookies from Google to deliver its services and to analyze traffic. 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! I graduated with a PhD from Princeton University in 2018. [pdf] [poster] I am a senior researcher in the Algorithms group at Microsoft Research Redmond. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 Slides from my talk at ITCS. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! 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. Faster energy maximization for faster maximum flow. what is a blind trust for lottery winnings; ithaca college park school scholarships; Research Institute for Interdisciplinary Sciences (RIIS) at [pdf] [poster] Stanford, CA 94305 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. 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 Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford Student Intranet. which is why I created a Huang Engineering Center 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 was fortunate to work with Prof. Zhongzhi Zhang. ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). 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. 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods 2023. . With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. Yang P. Liu, Aaron Sidford, Department of Mathematics Stanford University Enrichment of Network Diagrams for Potential Surfaces. Source: appliancesonline.com.au. Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." Before attending Stanford, I graduated from MIT in May 2018. With Yair Carmon, John C. Duchi, and Oliver Hinder. with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford However, even restarting can be a hard task here. I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. My research focuses on AI and machine learning, with an emphasis on robotics applications. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA STOC 2023. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Email / CoRR abs/2101.05719 ( 2021 ) aaron sidford cvis sea bass a bony fish to eat. CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. Another research focus are optimization algorithms. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG with Arun Jambulapati, Aaron Sidford and Kevin Tian 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. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). [pdf] [slides] /CreationDate (D:20230304061109-08'00') ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. {{{;}#q8?\. Personal Website. In submission. Try again later. 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. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 [pdf] [pdf]
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