aaron sidford cv

Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Iterative methods, combinatorial optimization, and linear programming Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. ReSQueing Parallel and Private Stochastic Convex Optimization. CoRR abs/2101.05719 ( 2021 ) My long term goal is to bring robots into human-centered domains such as homes and hospitals. University, where IEEE, 147-156. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Aleksander Mdry; Generalized preconditioning and network flow problems 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University Faculty Spotlight: Aaron Sidford - Management Science and Engineering 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. "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. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. I am fortunate to be advised by Aaron Sidford . Improved Lower Bounds for Submodular Function Minimization Aaron Sidford (sidford@stanford.edu) Welcome This page has informatoin and lecture notes from the course "Introduction to Optimization Theory" (MS&E213 / CS 269O) which I taught in Fall 2019. Algorithms Optimization and Numerical Analysis. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs Some I am still actively improving and all of them I am happy to continue polishing. (ACM Doctoral Dissertation Award, Honorable Mention.) 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. Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. Improves the stochas-tic convex optimization problem in parallel and DP setting. . [pdf] 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 SODA 2023: 5068-5089. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . However, even restarting can be a hard task here. In this talk, I will present a new algorithm for solving linear programs. (, 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 (. Yujia Jin. 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. [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. Aaron Sidford - Teaching 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. Selected for oral presentation. van vu professor, yale Verified email at yale.edu. 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. Aaron Sidford - All Publications July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. 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. {{{;}#q8?\. Sivakanth Gopi at Microsoft Research Best Paper Award. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. KTH in Stockholm, Sweden, and my BSc + MSc at the Eigenvalues of the laplacian and their relationship to the connectedness of a graph. CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. 4026. Anup B. Rao. CME 305/MS&E 316: Discrete Mathematics and Algorithms ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). Secured intranet portal for faculty, staff and students. Group Resources. with Yair Carmon, Aaron Sidford and Kevin Tian I regularly advise Stanford students from a variety of departments. 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 - Home - Author DO Series Source: www.ebay.ie I also completed my undergraduate degree (in mathematics) at MIT. aaron sidford cvis sea bass a bony fish to eat. Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. My research is on the design and theoretical analysis of efficient algorithms and data structures. " 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. Roy Frostig - Stanford University 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. 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. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. with Aaron Sidford International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle [pdf] [poster] 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. February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. which is why I created a 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. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. aaron sidford cv I often do not respond to emails about applications. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. I am broadly interested in mathematics and theoretical computer science. 9-21. Computer Science. 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. with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian If you see any typos or issues, feel free to email me. Improved Lower Bounds for Submodular Function Minimization. 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. Here are some lecture notes that I have written over the years. We also provide two . Before Stanford, I worked with John Lafferty at the University of Chicago. stream 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 . in Chemistry at the University of Chicago. I am broadly interested in optimization problems, sometimes in the intersection with machine learning I was fortunate to work with Prof. Zhongzhi Zhang. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). 4 0 obj 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. /Creator (Apache FOP Version 1.0) 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. Yair Carmon. 2016. Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford. Interior Point Methods for Nearly Linear Time Algorithms | ISL when do tulips bloom in maryland; indo pacific region upsc [last name]@stanford.edu where [last name]=sidford. [pdf] Our method improves upon the convergence rate of previous state-of-the-art linear programming . with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford Call (225) 687-7590 or park nicollet dermatology wayzata today! Kirankumar Shiragur | Data Science I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. /Producer (Apache FOP Version 1.0) to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration Try again later. 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. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& with Kevin Tian and Aaron Sidford Here are some lecture notes that I have written over the years. The site facilitates research and collaboration in academic endeavors. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. with Arun Jambulapati, Aaron Sidford and Kevin Tian In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. Conference on Learning Theory (COLT), 2015. aaron sidford cvnatural fibrin removalnatural fibrin removal I am an Assistant Professor in the School of Computer Science at Georgia Tech. In International Conference on Machine Learning (ICML 2016). Adam Bouland - Stanford University Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! Applying this technique, we prove that any deterministic SFM algorithm . Nearly Optimal Communication and Query Complexity of Bipartite Matching . 2017. Stanford University Faculty and Staff Intranet. [PDF] Faster Algorithms for Computing the Stationary Distribution We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. Sequential Matrix Completion. From 2016 to 2018, I also worked in With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). In submission. With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. Faster energy maximization for faster maximum flow. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. with Aaron Sidford Email: [name]@stanford.edu Aaron's research interests lie in optimization, the theory of computation, and the . Authors: Michael B. Cohen, Jonathan Kelner, Rasmus Kyng, John Peebles, Richard Peng, Anup B. Rao, Aaron Sidford Download PDF Abstract: We show how to solve directed Laplacian systems in nearly-linear time. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. 2015 Doctoral Dissertation Award - Association for Computing Machinery 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. Mary Wootters - Google Advanced Data Structures (6.851) - Massachusetts Institute of Technology [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. Many of my results use fast matrix multiplication In Sidford's dissertation, Iterative Methods, Combinatorial . Roy Frostig, Sida Wang, Percy Liang, Chris Manning. Yang P. Liu, Aaron Sidford, Department of Mathematics [pdf] [poster] Aaron Sidford - My Group 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. In Symposium on Theory of Computing (STOC 2020) (arXiv), Constant Girth Approximation for Directed Graphs in Subquadratic Time, With Shiri Chechik, Yang P. Liu, and Omer Rotem, Leverage Score Sampling for Faster Accelerated Regression and ERM, With Naman Agarwal, Sham Kakade, Rahul Kidambi, Yin Tat Lee, and Praneeth Netrapalli, In International Conference on Algorithmic Learning Theory (ALT 2020) (arXiv), Near-optimal Approximate Discrete and Continuous Submodular Function Minimization, In Symposium on Discrete Algorithms (SODA 2020) (arXiv), Fast and Space Efficient Spectral Sparsification in Dynamic Streams, With Michael Kapralov, Aida Mousavifar, Cameron Musco, Christopher Musco, Navid Nouri, and Jakab Tardos, In Conference on Neural Information Processing Systems (NeurIPS 2019), Complexity of Highly Parallel Non-Smooth Convex Optimization, With Sbastien Bubeck, Qijia Jiang, Yin Tat Lee, and Yuanzhi Li, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG, A Direct (1/) Iteration Parallel Algorithm for Optimal Transport, In Conference on Neural Information Processing Systems (NeurIPS 2019) (arXiv), A General Framework for Efficient Symmetric Property Estimation, With Moses Charikar and Kirankumar Shiragur, Parallel Reachability in Almost Linear Work and Square Root Depth, In Symposium on Foundations of Computer Science (FOCS 2019) (arXiv), With Deeparnab Chakrabarty, Yin Tat Lee, Sahil Singla, and Sam Chiu-wai Wong, Deterministic Approximation of Random Walks in Small Space, With Jack Murtagh, Omer Reingold, and Salil P. Vadhan, In International Workshop on Randomization and Computation (RANDOM 2019), A Rank-1 Sketch for Matrix Multiplicative Weights, With Yair Carmon, John C. Duchi, and Kevin Tian, In Conference on Learning Theory (COLT 2019) (arXiv), Near-optimal method for highly smooth convex optimization, Efficient profile maximum likelihood for universal symmetric property estimation, In Symposium on Theory of Computing (STOC 2019) (arXiv), Memory-sample tradeoffs for linear regression with small error, Perron-Frobenius Theory in Nearly Linear Time: Positive Eigenvectors, M-matrices, Graph Kernels, and Other Applications, With AmirMahdi Ahmadinejad, Arun Jambulapati, and Amin Saberi, In Symposium on Discrete Algorithms (SODA 2019) (arXiv), Exploiting Numerical Sparsity for Efficient Learning: Faster Eigenvector Computation and Regression, In Conference on Neural Information Processing Systems (NeurIPS 2018) (arXiv), Near-Optimal Time and Sample Complexities for Solving Discounted Markov Decision Process with a Generative Model, With Mengdi Wang, Xian Wu, Lin F. Yang, and Yinyu Ye, Coordinate Methods for Accelerating Regression and Faster Approximate Maximum Flow, In Symposium on Foundations of Computer Science (FOCS 2018), Solving Directed Laplacian Systems in Nearly-Linear Time through Sparse LU Factorizations, With Michael B. Cohen, Jonathan A. Kelner, Rasmus Kyng, John Peebles, Richard Peng, and Anup B. Rao, In Symposium on Foundations of Computer Science (FOCS 2018) (arXiv), Efficient Convex Optimization with Membership Oracles, In Conference on Learning Theory (COLT 2018) (arXiv), Accelerating Stochastic Gradient Descent for Least Squares Regression, With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli, Approximating Cycles in Directed Graphs: Fast Algorithms for Girth and Roundtrip Spanners. My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). 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] [slides] 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 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 . with Yair Carmon, Aaron Sidford and Kevin Tian by Aaron Sidford. In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? The authors of most papers are ordered alphabetically. Publications and Preprints. 2023. . << Aaron Sidford - Stanford University ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). Publications | Jakub Pachocki - Harvard University 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 forward in this generation, Triumphantly. Navajo Math Circles Instructor. Annie Marsden. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. theses are protected by copyright. With Yair Carmon, John C. Duchi, and Oliver Hinder. /CreationDate (D:20230304061109-08'00') ", "Sample complexity for average-reward MDPs? to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. In each setting we provide faster exact and approximate algorithms. Information about your use of this site is shared with Google. ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. Aaron Sidford receives best paper award at COLT 2022 ?_l) Management Science & Engineering We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. [pdf] Aaron Sidford - Google Scholar This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. Microsoft Research Faculty Fellowship 2020: Researchers in academia at 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. With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. ", Applied Math at Fudan Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . This site uses cookies from Google to deliver its services and to analyze traffic. Simple MAP inference via low-rank relaxations. Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. 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. ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. Faculty Spotlight: Aaron Sidford. To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). CSE 535: Theory of Optimization and Continuous Algorithms - Yin Tat University, Research Institute for Interdisciplinary Sciences (RIIS) at Fresh Faculty: Theoretical computer scientist Aaron Sidford joins MS&E Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. Lower Bounds for Finding Stationary Points II: First-Order Methods

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