They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . 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. The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. Email: sidford@stanford.edu. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Faculty Spotlight: Aaron Sidford. what is a blind trust for lottery winnings; ithaca college park school scholarships; In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! Alcatel flip phones are also ready to purchase with consumer cellular. theses are protected by copyright. ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). Huang Engineering Center There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. Yin Tat Lee and Aaron Sidford. Google Scholar; Probability on trees and . 9-21. February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. Another research focus are optimization algorithms. I am broadly interested in optimization problems, sometimes in the intersection with machine learning Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan 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 SODA 2023: 5068-5089. 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 . In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. with Vidya Muthukumar and Aaron Sidford United States. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG Enrichment of Network Diagrams for Potential Surfaces. SODA 2023: 4667-4767. University, where My CV. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. with Yair Carmon, Kevin Tian and Aaron Sidford I am broadly interested in mathematics and theoretical computer science. [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. The authors of most papers are ordered alphabetically. University of Cambridge MPhil. Full CV is available here. Source: appliancesonline.com.au. 4026. with Aaron Sidford Outdated CV [as of Dec'19] Students I am very lucky to advise the following Ph.D. students: Siddartha Devic (co-advised with Aleksandra Korolova . with Yair Carmon, Aaron Sidford and Kevin Tian Np%p `a!2D4! 5 0 obj Goethe University in Frankfurt, Germany. << STOC 2023. in math and computer science from Swarthmore College in 2008. Algorithms Optimization and Numerical Analysis. 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. The design of algorithms is traditionally a discrete endeavor. " 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. Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. . Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. Applying this technique, we prove that any deterministic SFM algorithm . 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. with Kevin Tian and Aaron Sidford Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. ", "Sample complexity for average-reward MDPs? 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 pdf, Sequential Matrix Completion. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. Their, This "Cited by" count includes citations to the following articles in Scholar. with Aaron Sidford sidford@stanford.edu. with Arun Jambulapati, Aaron Sidford and Kevin Tian 2021 - 2022 Postdoc, Simons Institute & UC . Stanford, CA 94305 Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. Improves the stochas-tic convex optimization problem in parallel and DP setting. 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 . how . Yair Carmon. 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 establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. 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. ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. Group Resources. Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. Yang P. Liu, Aaron Sidford, Department of Mathematics A nearly matching upper and lower bound for constant error here! [pdf] [poster] Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper Roy Frostig, Sida Wang, Percy Liang, Chris Manning. [pdf] ICML, 2016. [pdf] [talk] Publications and Preprints. Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 It was released on november 10, 2017. 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 . Faculty and Staff Intranet. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). . Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space The following articles are merged in Scholar. van vu professor, yale Verified email at yale.edu. International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle Google Scholar Digital Library; Russell Lyons and Yuval Peres. Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). O! I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. 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. AISTATS, 2021. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. I enjoy understanding the theoretical ground of many algorithms that are [pdf] with Aaron Sidford Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. COLT, 2022. 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. CV (last updated 01-2022): PDF Contact. /N 3 You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). My broad research interest is in theoretical computer science and my focus is on fundamental mathematical problems in data science at the intersection of computer science, statistics, optimization, biology and economics. 2023. . Email / Office: 380-T /CreationDate (D:20230304061109-08'00') with Yair Carmon, Aaron Sidford and Kevin Tian ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. COLT, 2022. arXiv | code | conference pdf (alphabetical authorship), Annie Marsden, John Duchi and Gregory Valiant, Misspecification in Prediction Problems and Robustness via Improper Learning. [pdf] [slides] We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). "t a","H Try again later. Annie Marsden. 2021. [pdf] [poster] The system can't perform the operation now. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. 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. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods resume/cv; publications. Semantic parsing on Freebase from question-answer pairs. Research Institute for Interdisciplinary Sciences (RIIS) at I received a B.S. % 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. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods If you see any typos or issues, feel free to email me. I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. Lower bounds for finding stationary points II: first-order methods. SHUFE, where I was fortunate I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. Before attending Stanford, I graduated from MIT in May 2018. ReSQueing Parallel and Private Stochastic Convex Optimization. Best Paper Award. I am Personal Website. 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University One research focus are dynamic algorithms (i.e. [pdf] [poster] 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).