Before attending Stanford, I graduated from MIT in May 2018. Applying this technique, we prove that any deterministic SFM algorithm . I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. 9-21. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. 2017. I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. 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. In each setting we provide faster exact and approximate algorithms. International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle My interests are in the intersection of algorithms, statistics, optimization, and machine learning. ?_l) With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. Secured intranet portal for faculty, staff and students. Sequential Matrix Completion. Google Scholar Digital Library; Russell Lyons and Yuval Peres. This site uses cookies from Google to deliver its services and to analyze traffic. theses are protected by copyright. Anup B. Rao. Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. ", "Sample complexity for average-reward MDPs? ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). Secured intranet portal for faculty, staff and students. 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. I was fortunate to work with Prof. Zhongzhi Zhang. which is why I created a Yujia Jin. resume/cv; publications. Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. 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. I graduated with a PhD from Princeton University in 2018. July 8, 2022. Yang P. Liu, Aaron Sidford, Department of Mathematics to be advised by Prof. Dongdong Ge. van vu professor, yale Verified email at yale.edu. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. If you see any typos or issues, feel free to email me. [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. SODA 2023: 4667-4767. Mail Code. with Yair Carmon, Aaron Sidford and Kevin Tian However, even restarting can be a hard task here. ", Applied Math at Fudan Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. COLT, 2022. Email: [name]@stanford.edu SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. Summer 2022: I am currently a research scientist intern at DeepMind in London. 5 0 obj Yujia Jin. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. Annie Marsden. Some I am still actively improving and all of them I am happy to continue polishing. Many of my results use fast matrix multiplication We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . [pdf] I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. [pdf] About Me. 2021 - 2022 Postdoc, Simons Institute & UC . They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. Here are some lecture notes that I have written over the years. by Aaron Sidford. with Yair Carmon, Arun Jambulapati and Aaron Sidford D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ Contact. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. Links. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. Their, This "Cited by" count includes citations to the following articles in Scholar. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford 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. Before attending Stanford, I graduated from MIT in May 2018. 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. missouri noodling association president cnn. 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. Computer Science. Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space Done under the mentorship of M. Malliaris. in Chemistry at the University of Chicago. 2016. Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. Some I am still actively improving and all of them I am happy to continue polishing. Intranet Web Portal. ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. 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. I am broadly interested in mathematics and theoretical computer science. [pdf] We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). /CreationDate (D:20230304061109-08'00') sidford@stanford.edu. We also provide two . ", "Team-convex-optimization for solving discounted and average-reward MDPs! With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. ! In International Conference on Machine Learning (ICML 2016). Allen Liu. I regularly advise Stanford students from a variety of departments. [pdf] [talk] [poster] Office: 380-T Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? with Kevin Tian and Aaron Sidford with Vidya Muthukumar and Aaron Sidford DOI: 10.1109/FOCS.2016.69 Corpus ID: 3311; Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More @article{Cohen2016FasterAF, title={Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More}, author={Michael B. Cohen and Jonathan A. Kelner and John Peebles and Richard Peng and Aaron Sidford and Adrian Vladu}, journal . In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. Stanford, CA 94305 Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) From 2016 to 2018, I also worked in "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. (, 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 (. I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. I am fortunate to be advised by Aaron Sidford. [pdf] [poster] My research focuses on AI and machine learning, with an emphasis on robotics applications. 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. publications by categories in reversed chronological order. /Filter /FlateDecode /Length 11 0 R View Full Stanford Profile. Given a linear program with n variables, m > n constraints, and bit complexity L, our algorithm runs in (sqrt(n) L) iterations each consisting of solving (1) linear systems and additional nearly linear time computation. 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. 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. Lower bounds for finding stationary points II: first-order methods. [pdf] [talk] [poster] Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . The authors of most papers are ordered alphabetically. ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! what is a blind trust for lottery winnings; ithaca college park school scholarships; >> rl1 United States. I completed my PhD at It was released on november 10, 2017. . xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y 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. Before Stanford, I worked with John Lafferty at the University of Chicago. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. with Aaron Sidford Two months later, he was found lying in a creek, dead from . (ACM Doctoral Dissertation Award, Honorable Mention.) Associate Professor of . Etude for the Park City Math Institute Undergraduate Summer School. Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. 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. 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. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs Selected recent papers . Articles Cited by Public access. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . Assistant Professor of Management Science and Engineering and of Computer Science. My CV. Full CV is available here. Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." (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.

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