", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. Some I am still actively improving and all of them I am happy to continue polishing. Follow. Try again later. Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. In International Conference on Machine Learning (ICML 2016). In Sidford's dissertation, Iterative Methods, Combinatorial . stream Personal Website. With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. Enrichment of Network Diagrams for Potential Surfaces. Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. 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. << in Chemistry at the University of Chicago. Source: appliancesonline.com.au. Faculty and Staff Intranet. xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games With Cameron Musco and Christopher Musco. If you see any typos or issues, feel free to email me. My long term goal is to bring robots into human-centered domains such as homes and hospitals. with Vidya Muthukumar and Aaron Sidford With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. ICML, 2016. 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. SODA 2023: 4667-4767. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! 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 . I am an Assistant Professor in the School of Computer Science at Georgia Tech. [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. Yang P. Liu, Aaron Sidford, Department of Mathematics sidford@stanford.edu. ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). From 2016 to 2018, I also worked in (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. 475 Via Ortega with Yair Carmon, Aaron Sidford and Kevin Tian In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Before attending Stanford, I graduated from MIT in May 2018. /Length 11 0 R I am broadly interested in optimization problems, sometimes in the intersection with machine learning [pdf] [slides] Title. SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. 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 research focuses on AI and machine learning, with an emphasis on robotics applications. . ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. CV (last updated 01-2022): PDF Contact. 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. >> This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space 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 The design of algorithms is traditionally a discrete endeavor. [pdf] [poster] when do tulips bloom in maryland; indo pacific region upsc with Yair Carmon, Arun Jambulapati and Aaron Sidford I am a senior researcher in the Algorithms group at Microsoft Research Redmond. NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games [pdf] University, Research Institute for Interdisciplinary Sciences (RIIS) at ", "Team-convex-optimization for solving discounted and average-reward MDPs! Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford Allen Liu. Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper ! It was released on november 10, 2017. "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. Sequential Matrix Completion. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. 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 with Yair Carmon, Kevin Tian and Aaron Sidford Intranet Web Portal. KTH in Stockholm, Sweden, and my BSc + MSc at the by Aaron Sidford. 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. Here is a slightly more formal third-person biography, and here is a recent-ish CV. Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. Yujia Jin. with Kevin Tian and Aaron Sidford Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 F+s9H Some I am still actively improving and all of them I am happy to continue polishing. Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. 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. 4026. 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. University of Cambridge MPhil. February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. Nearly Optimal Communication and Query Complexity of Bipartite Matching . International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods of practical importance. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization Selected recent papers . ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). Alcatel flip phones are also ready to purchase with consumer cellular. 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. [pdf] With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. 2013. [pdf] [talk] [poster] << 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. /CreationDate (D:20230304061109-08'00') 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. Annie Marsden. 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. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created.