[pdf] Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. Before attending Stanford, I graduated from MIT in May 2018. with Yair Carmon, Kevin Tian and Aaron Sidford We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Aaron Sidford. Associate Professor of . Aaron's research interests lie in optimization, the theory of computation, and the . Email: [name]@stanford.edu Improved Lower Bounds for Submodular Function Minimization. (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. . arXiv preprint arXiv:2301.00457, 2023 arXiv. In International Conference on Machine Learning (ICML 2016). /N 3 with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. 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 . Verified email at stanford.edu - Homepage. Stanford University ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. 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. Faculty Spotlight: Aaron Sidford. Assistant Professor of Management Science and Engineering and of Computer Science. With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. 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 [pdf] [poster] Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. I am a senior researcher in the Algorithms group at Microsoft Research Redmond. Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. I also completed my undergraduate degree (in mathematics) at MIT. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. pdf, Sequential Matrix Completion. 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. MS&E welcomes new faculty member, Aaron Sidford ! << I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. IEEE, 147-156. with Aaron Sidford 2021 - 2022 Postdoc, Simons Institute & UC . SHUFE, where I was fortunate [pdf] [poster] Articles Cited by Public access. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. . in Mathematics and B.A. 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. SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. I often do not respond to emails about applications. With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. 2021. ReSQueing Parallel and Private Stochastic Convex Optimization. Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. My CV. The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. Follow. My interests are in the intersection of algorithms, statistics, optimization, and machine learning. 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! 2017. In Sidford's dissertation, Iterative Methods, Combinatorial . Email / Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. Main Menu. O! In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. University, where About Me. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Selected recent papers . 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. Links. I am fortunate to be advised by Aaron Sidford . I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Best Paper Award. It was released on november 10, 2017. [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. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). Email: sidford@stanford.edu. /Creator (Apache FOP Version 1.0) 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 . We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. 2013. /Producer (Apache FOP Version 1.0) ", "A short version of the conference publication under the same title. with Yair Carmon, Arun Jambulapati and Aaron Sidford Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. ", "Sample complexity for average-reward MDPs? There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. Their, This "Cited by" count includes citations to the following articles in Scholar. what is a blind trust for lottery winnings; ithaca college park school scholarships; Summer 2022: I am currently a research scientist intern at DeepMind in London. with Yair Carmon, Aaron Sidford and Kevin Tian to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford 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. with Vidya Muthukumar and Aaron Sidford Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. If you see any typos or issues, feel free to email me. with Aaron Sidford 2016. Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. Journal of Machine Learning Research, 2017 (arXiv). Information about your use of this site is shared with Google. Goethe University in Frankfurt, Germany. aaron sidford cvis sea bass a bony fish to eat. My research is on the design and theoretical analysis of efficient algorithms and data structures. theses are protected by copyright. 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 . Management Science & Engineering To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. [pdf] If you see any typos or issues, feel free to email me. 9-21. . 5 0 obj I received a B.S. with Yair Carmon, Aaron Sidford and Kevin Tian Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs Yin Tat Lee and Aaron Sidford. Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." Lower bounds for finding stationary points II: first-order methods. Before Stanford, I worked with John Lafferty at the University of Chicago. With Cameron Musco and Christopher Musco. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). Secured intranet portal for faculty, staff and students. % ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? In submission. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). 2016. 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.