SHUFE, where I was fortunate ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. The system can't perform the operation now. [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent aaron sidford cvis sea bass a bony fish to eat. In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . 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. % 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 >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. 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 . ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. 9-21. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in Enrichment of Network Diagrams for Potential Surfaces. In each setting we provide faster exact and approximate algorithms. Navajo Math Circles Instructor. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! . Yujia Jin. F+s9H " 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. arXiv preprint arXiv:2301.00457, 2023 arXiv. 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. ", "A short version of the conference publication under the same title. when do tulips bloom in maryland; indo pacific region upsc Another research focus are optimization algorithms. Email: sidford@stanford.edu. rl1 of practical importance. with Aaron Sidford with Yair Carmon, Arun Jambulapati and Aaron Sidford 2016. She was 19 years old and looking forward to the start of classes and reuniting with her college pals. Adam Bouland - Stanford University Applying this technique, we prove that any deterministic SFM algorithm . Aaron Sidford | Stanford Online Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. I am ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. Best Paper Award. I regularly advise Stanford students from a variety of departments. IEEE, 147-156. /N 3 Np%p `a!2D4! with Yang P. Liu and Aaron Sidford. 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. /Length 11 0 R Faculty Spotlight: Aaron Sidford. 2017. ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). ?_l) About - Annie Marsden with Yair Carmon, 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. 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). Anup B. Rao - Google Scholar Email: [name]@stanford.edu Yang P. Liu, Aaron Sidford, Department of Mathematics Follow. The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. van vu professor, yale Verified email at yale.edu. 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. Google Scholar; Probability on trees and . 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. 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. Full CV is available here. We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). Nearly Optimal Communication and Query Complexity of Bipartite Matching . Many of my results use fast matrix multiplication I am a senior researcher in the Algorithms group at Microsoft Research Redmond. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. 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. resume/cv; publications. CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. Gregory Valiant Homepage - Stanford University Their, This "Cited by" count includes citations to the following articles in Scholar. Secured intranet portal for faculty, staff and students. Annie Marsden. Management Science & Engineering 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. My CV. Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA Articles 1-20. 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 . [pdf] [pdf] [talk] [poster] [pdf] I received a B.S. Aaron Sidford - Teaching 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 . CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. Iterative methods, combinatorial optimization, and linear programming Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games /CreationDate (D:20230304061109-08'00') I enjoy understanding the theoretical ground of many algorithms that are I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. Microsoft Research Faculty Fellowship 2020: Researchers in academia at Aaron Sidford receives best paper award at COLT 2022 My long term goal is to bring robots into human-centered domains such as homes and hospitals. . with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. to be advised by Prof. Dongdong Ge. Main Menu. with Aaron Sidford I also completed my undergraduate degree (in mathematics) at MIT. with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian 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. {{{;}#q8?\. 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 . sidford@stanford.edu. Assistant Professor of Management Science and Engineering and of Computer Science. A Faster Algorithm for Linear Programming and the Maximum Flow Problem II Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). Source: www.ebay.ie Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. Yair Carmon. . Fresh Faculty: Theoretical computer scientist Aaron Sidford joins MS&E Aaron Sidford's research works | Stanford University, CA (SU) and other (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. 2015 Doctoral Dissertation Award - Association for Computing Machinery ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). [pdf] [poster] Contact. Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. Journal of Machine Learning Research, 2017 (arXiv). CSE 535: Theory of Optimization and Continuous Algorithms - Yin Tat If you see any typos or issues, feel free to email me. how . Sampling random spanning trees faster than matrix multiplication Lower bounds for finding stationary points II: first-order methods. aaron sidford cvnatural fibrin removalnatural fibrin removal In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. 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. Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. 2023. . International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle [last name]@stanford.edu where [last name]=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. Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) Vatsal Sharan - GitHub Pages Mary Wootters - Google 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. One research focus are dynamic algorithms (i.e. "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. by Aaron Sidford. Yujia Jin. Parallelizing Stochastic Gradient Descent for Least Squares Regression Some I am still actively improving and all of them I am happy to continue polishing. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of 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 [pdf] [talk] (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. Done under the mentorship of M. Malliaris. Faster Matroid Intersection Princeton University A nearly matching upper and lower bound for constant error here! Aaron Sidford - All Publications >> which is why I created a International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods ", "Sample complexity for average-reward MDPs?
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