aaron sidford cv
/N 3 I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. Articles Cited by Public access. Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. >> Yang P. Liu - GitHub Pages theory and graph applications. Advanced Data Structures (6.851) - Massachusetts Institute of Technology Many of my results use fast matrix multiplication (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. Research Institute for Interdisciplinary Sciences (RIIS) at Done under the mentorship of M. Malliaris. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . Enrichment of Network Diagrams for Potential Surfaces. Jan van den Brand Yujia Jin. Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) One research focus are dynamic algorithms (i.e. [pdf] [pdf] [talk] % 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. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs aaron sidford cv ICML, 2016. 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. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. Algorithms Optimization and Numerical Analysis. 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. 4 0 obj Secured intranet portal for faculty, staff and students. Vatsal Sharan - GitHub Pages Unlike previous ADFOCS, this year the event will take place over the span of three weeks. Interior Point Methods for Nearly Linear Time Algorithms | ISL 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. endobj Here are some lecture notes that I have written over the years. ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. Assistant Professor of Management Science and Engineering and of Computer Science. Aaron Sidford's research works | Stanford University, CA (SU) and other Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. Journal of Machine Learning Research, 2017 (arXiv). publications by categories in reversed chronological order. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. ", Applied Math at Fudan [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. CV (last updated 01-2022): PDF Contact. I enjoy understanding the theoretical ground of many algorithms that are Cameron Musco - Manning College of Information & Computer Sciences I am fortunate to be advised by Aaron Sidford. Email: [name]@stanford.edu 2023. . ", "Team-convex-optimization for solving discounted and average-reward MDPs! Accelerated Methods for NonConvex Optimization | Semantic Scholar which is why I created a Email / ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! Before attending Stanford, I graduated from MIT in May 2018. I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Aaron Sidford Stanford University Verified email at stanford.edu. Publications | Jakub Pachocki - Harvard University Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. 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 CSE 535: Theory of Optimization and Continuous Algorithms - Yin Tat with Kevin Tian and Aaron Sidford CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in Aaron Sidford | Stanford Online 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. Mary Wootters - Google They will share a $10,000 prize, with financial sponsorship provided by Google Inc. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. Slides from my talk at ITCS. Anup B. Rao - Google Scholar I also completed my undergraduate degree (in mathematics) at MIT. Stanford University. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA Yin Tat Lee and Aaron Sidford. Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 Main Menu. when do tulips bloom in maryland; indo pacific region upsc IEEE, 147-156. My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). University, where with Aaron Sidford 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 . Semantic parsing on Freebase from question-answer pairs. My CV. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford July 8, 2022. We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. {{{;}#q8?\. Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. AISTATS, 2021. >> with Aaron Sidford [pdf] [poster] United States. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. Alcatel flip phones are also ready to purchase with consumer cellular. stream with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford 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. 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).