aaron sidford cv

in Chemistry at the University of Chicago. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs . We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). [pdf] [talk] [poster] Selected recent papers . [pdf] rl1 [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 (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. by Aaron Sidford. Semantic parsing on Freebase from question-answer pairs. STOC 2023. If you see any typos or issues, feel free to email me. the Operations Research group. >> SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. 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 Publications and Preprints. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. My long term goal is to bring robots into human-centered domains such as homes and hospitals. Summer 2022: I am currently a research scientist intern at DeepMind in London. with Yair Carmon, Aaron Sidford and Kevin Tian International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle theory and graph applications. Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University The Complexity of Infinite-Horizon General-Sum Stochastic Games, With Yujia Jin, Vidya Muthukumar, Aaron Sidford, To appear in Innovations in Theoretical Computer Science (ITCS 2023) (arXiv), Optimal and Adaptive Monteiro-Svaiter Acceleration, With Yair Carmon, Danielle Hausler, Arun Jambulapati, and Yujia Jin, To appear in Advances in Neural Information Processing Systems (NeurIPS 2022) (arXiv), On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood, With Moses Charikar, Zhihao Jiang, and Kirankumar Shiragur, Improved Lower Bounds for Submodular Function Minimization, With Deeparnab Chakrabarty, Andrei Graur, and Haotian Jiang, In Symposium on Foundations of Computer Science (FOCS 2022) (arXiv), RECAPP: Crafting a More Efficient Catalyst for Convex Optimization, With Yair Carmon, Arun Jambulapati, and Yujia Jin, International Conference on Machine Learning (ICML 2022) (arXiv), Efficient Convex Optimization Requires Superlinear Memory, With Annie Marsden, Vatsal Sharan, and Gregory Valiant, Conference on Learning Theory (COLT 2022), Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Method, Conference on Learning Theory (COLT 2022) (arXiv), Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales, With Jonathan A. Kelner, Annie Marsden, Vatsal Sharan, Gregory Valiant, and Honglin Yuan, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching, With Arun Jambulapati, Yujia Jin, and Kevin Tian, International Colloquium on Automata, Languages and Programming (ICALP 2022) (arXiv), Fully-Dynamic Graph Sparsifiers Against an Adaptive Adversary, With Aaron Bernstein, Jan van den Brand, Maximilian Probst, Danupon Nanongkai, Thatchaphol Saranurak, and He Sun, Faster Maxflow via Improved Dynamic Spectral Vertex Sparsifiers, With Jan van den Brand, Yu Gao, Arun Jambulapati, Yin Tat Lee, Yang P. Liu, and Richard Peng, In Symposium on Theory of Computing (STOC 2022) (arXiv), Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space, With Sepehr Assadi, Arun Jambulapati, Yujia Jin, and Kevin Tian, In Symposium on Discrete Algorithms (SODA 2022) (arXiv), Algorithmic trade-offs for girth approximation in undirected graphs, With Avi Kadria, Liam Roditty, Virginia Vassilevska Williams, and Uri Zwick, In Symposium on Discrete Algorithms (SODA 2022), Computing Lewis Weights to High Precision, With Maryam Fazel, Yin Tat Lee, and Swati Padmanabhan, With Hilal Asi, Yair Carmon, Arun Jambulapati, and Yujia Jin, In Advances in Neural Information Processing Systems (NeurIPS 2021) (arXiv), Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss, In Conference on Learning Theory (COLT 2021) (arXiv), The Bethe and Sinkhorn Permanents of Low Rank Matrices and Implications for Profile Maximum Likelihood, With Nima Anari, Moses Charikar, and Kirankumar Shiragur, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs, In International Conference on Machine Learning (ICML 2021) (arXiv), Minimum cost flows, MDPs, and 1-regression in nearly linear time for dense instances, With Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, and Zhao Song, Di Wang, In Symposium on Theory of Computing (STOC 2021) (arXiv), Ultrasparse Ultrasparsifiers and Faster Laplacian System Solvers, In Symposium on Discrete Algorithms (SODA 2021) (arXiv), Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration, In Innovations in Theoretical Computer Science (ITCS 2021) (arXiv), Acceleration with a Ball Optimization Oracle, With Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, and Kevin Tian, In Conference on Neural Information Processing Systems (NeurIPS 2020), Instance Based Approximations to Profile Maximum Likelihood, In Conference on Neural Information Processing Systems (NeurIPS 2020) (arXiv), Large-Scale Methods for Distributionally Robust Optimization, With Daniel Levy*, Yair Carmon*, and John C. Duch (* denotes equal contribution), High-precision Estimation of Random Walks in Small Space, With AmirMahdi Ahmadinejad, Jonathan A. Kelner, Jack Murtagh, John Peebles, and Salil P. Vadhan, In Symposium on Foundations of Computer Science (FOCS 2020) (arXiv), Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs, With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang, In Symposium on Foundations of Computer Science (FOCS 2020), With Yair Carmon, Yujia Jin, and Kevin Tian, Unit Capacity Maxflow in Almost $O(m^{4/3})$ Time, Invited to the special issue (arXiv before merge)), Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity, In International Conference on Artificial Intelligence and Statistics (AISTATS 2020) (arXiv), Efficiently Solving MDPs with Stochastic Mirror Descent, In International Conference on Machine Learning (ICML 2020) (arXiv), Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond, With Oliver Hinder and Nimit Sharad Sohoni, In Conference on Learning Theory (COLT 2020) (arXiv), Solving Tall Dense Linear Programs in Nearly Linear Time, With Jan van den Brand, Yin Tat Lee, and Zhao Song, In Symposium on Theory of Computing (STOC 2020). 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. I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. with Aaron Sidford Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. From 2016 to 2018, I also worked in 475 Via Ortega Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods 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. [pdf] "t a","H With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. . 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). Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y In submission. with Yair Carmon, Aaron Sidford and Kevin Tian 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. There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. Yair Carmon. [pdf] [poster] KTH in Stockholm, Sweden, and my BSc + MSc at the International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods 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 . 5 0 obj CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. when do tulips bloom in maryland; indo pacific region upsc Before Stanford, I worked with John Lafferty at the University of Chicago. with Kevin Tian and Aaron Sidford 113 * 2016: The system can't perform the operation now. >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. Source: www.ebay.ie Here is a slightly more formal third-person biography, and here is a recent-ish CV. If you see any typos or issues, feel free to email me. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. 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. I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. with Yair Carmon, Arun Jambulapati and Aaron Sidford Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. I am broadly interested in mathematics and theoretical computer science. David P. Woodruff . with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian 2013. With Yair Carmon, John C. Duchi, and Oliver Hinder. With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. Sequential Matrix Completion. We forward in this generation, Triumphantly. 2021. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss Articles Cited by Public access. Applying this technique, we prove that any deterministic SFM algorithm . << 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. 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. COLT, 2022. AISTATS, 2021. 2023. . << (ACM Doctoral Dissertation Award, Honorable Mention.) Enrichment of Network Diagrams for Potential Surfaces. Nearly Optimal Communication and Query Complexity of Bipartite Matching . pdf, Sequential Matrix Completion. Annie Marsden. The authors of most papers are ordered alphabetically. Improves the stochas-tic convex optimization problem in parallel and DP setting. Information about your use of this site is shared with Google. [pdf] [talk] [poster] Secured intranet portal for faculty, staff and students. ! I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. 2016. CoRR abs/2101.05719 ( 2021 ) Stanford, CA 94305 Yujia Jin. [pdf] [poster] My research focuses on AI and machine learning, with an emphasis on robotics applications. 2021 - 2022 Postdoc, Simons Institute & UC . July 8, 2022. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! Articles 1-20. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. Slides from my talk at ITCS. Towards this goal, some fundamental questions need to be solved, such as how can machines learn models of their environments that are useful for performing tasks . [pdf] [talk] Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. Journal of Machine Learning Research, 2017 (arXiv). We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). /Length 11 0 R Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games Personal Website. Huang Engineering Center 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. ", "Streaming matching (and optimal transport) in $\tilde{O}(1/\epsilon)$ passes and $O(n)$ space. In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. Anup B. Rao. Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 I regularly advise Stanford students from a variety of departments. We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. 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. ", "A short version of the conference publication under the same title. Faculty Spotlight: 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 . (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. (, 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 (. In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. Title. " 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. Research Institute for Interdisciplinary Sciences (RIIS) at Another research focus are optimization algorithms. 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. Try again later. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. I am broadly interested in mathematics and theoretical computer science. ?_l) Follow. COLT, 2022. arXiv | code | conference pdf (alphabetical authorship), Annie Marsden, John Duchi and Gregory Valiant, Misspecification in Prediction Problems and Robustness via Improper Learning. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. United States. aaron sidford cvis sea bass a bony fish to eat. Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. Etude for the Park City Math Institute Undergraduate Summer School. 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. van vu professor, yale Verified email at yale.edu. which is why I created a /Filter /FlateDecode 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. AISTATS, 2021. publications by categories in reversed chronological order. SHUFE, where I was fortunate ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. Aaron Sidford. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. 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. 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 In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) View Full Stanford Profile. Full CV is available here. Links. Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. arXiv preprint arXiv:2301.00457, 2023 arXiv. with Yang P. Liu and Aaron Sidford. This site uses cookies from Google to deliver its services and to analyze traffic. Google Scholar; Probability on trees and . Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . Google Scholar Digital Library; Russell Lyons and Yuval Peres. Done under the mentorship of M. Malliaris. My CV. theses are protected by copyright. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. Two months later, he was found lying in a creek, dead from . Before attending Stanford, I graduated from MIT in May 2018. CV (last updated 01-2022): PDF Contact. I am broadly interested in optimization problems, sometimes in the intersection with machine learning She was 19 years old and looking forward to the start of classes and reuniting with her college pals. Lower bounds for finding stationary points II: first-order methods. 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 . small tool to obtain upper bounds of such algebraic algorithms. Email / We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. 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 . Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions 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 . Algorithms Optimization and Numerical Analysis. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . endobj [pdf] [slides] My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Main Menu. with Aaron Sidford In each setting we provide faster exact and approximate algorithms. with Arun Jambulapati, Aaron Sidford and Kevin Tian If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. I enjoy understanding the theoretical ground of many algorithms that are International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG Our method improves upon the convergence rate of previous state-of-the-art linear programming . They will share a $10,000 prize, with financial sponsorship provided by Google Inc. with Vidya Muthukumar and Aaron Sidford Faculty and Staff Intranet. I completed my PhD at I am a senior researcher in the Algorithms group at Microsoft Research Redmond. [pdf] 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. Aleksander Mdry; Generalized preconditioning and network flow problems We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. ", Applied Math at Fudan This is the academic homepage of Yang Liu (I publish under Yang P. Liu). Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University.
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