The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games
With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. pdf, Sequential Matrix Completion. The design of algorithms is traditionally a discrete endeavor. ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? . 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. Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. 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 often do not respond to emails about applications. Aleksander Mdry; Generalized preconditioning and network flow problems 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 . ", "Sample complexity for average-reward MDPs? in Mathematics and B.A. 5 0 obj Cameron Musco - Manning College of Information & Computer Sciences Algorithms Optimization and Numerical Analysis. Abstract. >> Before Stanford, I worked with John Lafferty at the University of Chicago. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. MS&E welcomes new faculty member, Aaron Sidford ! Lower Bounds for Finding Stationary Points II: First-Order Methods I completed my PhD at
Annie Marsden. In International Conference on Machine Learning (ICML 2016). with Aaron Sidford
Source: appliancesonline.com.au. ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! 9-21.
International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle
We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . Roy Frostig, Sida Wang, Percy Liang, Chris Manning. 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. In submission. [pdf] [poster]
Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford
theses are protected by copyright. Neural Information Processing Systems (NeurIPS), 2014.
Here are some lecture notes that I have written over the years. with Kevin Tian and Aaron Sidford
[pdf]
Office: 380-T In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) A Faster Algorithm for Linear Programming and the Maximum Flow Problem II arXiv preprint arXiv:2301.00457, 2023 arXiv. with Vidya Muthukumar and Aaron Sidford
I regularly advise Stanford students from a variety of departments. Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan
Intranet Web Portal. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford.
. 22nd Max Planck Advanced Course on the Foundations of Computer Science July 8, 2022. "FV %H"Hr
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c0 L& 9cX& Assistant Professor of Management Science and Engineering and of Computer Science. with Yair Carmon, Arun Jambulapati and Aaron Sidford
the Operations Research group. To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. publications | Daogao Liu with Yair Carmon, Aaron Sidford and Kevin Tian
Nearly Optimal Communication and Query Complexity of Bipartite Matching . stream
Selected recent papers . theory and graph applications. Yin Tat Lee and Aaron Sidford. My research is on the design and theoretical analysis of efficient algorithms and data structures. 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. This site uses cookies from Google to deliver its services and to analyze traffic. [pdf] [poster]
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 . CV (last updated 01-2022): PDF Contact. I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. [pdf]
University of Cambridge MPhil. 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. We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). 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 am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Efficient Convex Optimization Requires Superlinear Memory. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. [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. Interior Point Methods for Nearly Linear Time Algorithms | ISL Full CV is available here. Yang P. Liu - GitHub Pages Contact. Title. small tool to obtain upper bounds of such algebraic algorithms. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022
Yujia Jin. If you see any typos or issues, feel free to email me. [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms.
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 . View Full Stanford Profile. Verified email at stanford.edu - Homepage. I am broadly interested in mathematics and theoretical computer science. Yang P. Liu, Aaron Sidford, Department of Mathematics Aaron Sidford - All Publications ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! Enrichment of Network Diagrams for Potential Surfaces. [pdf]
sidford@stanford.edu. Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . My CV. Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games
I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. SODA 2023: 4667-4767. [pdf]
Aaron Sidford | Stanford Online Sequential Matrix Completion. 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. which is why I created a
We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Selected for oral presentation.
Student Intranet. 2017. Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions
However, many advances have come from a continuous viewpoint. Stanford University. The following articles are merged in Scholar. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Aviv Tamar - Reinforcement Learning Research Labs - Technion Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games
data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. Aaron Sidford - Teaching [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization.
[pdf]
In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. publications by categories in reversed chronological order. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). aaron sidford cv Mary Wootters - Google CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss
[pdf] [talk] [poster]
Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) Aaron Sidford - live-simons-institute.pantheon.berkeley.edu [pdf] [talk] [poster]
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. Articles 1-20. However, even restarting can be a hard task here. Publications | Jakub Pachocki - Harvard University 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. van vu professor, yale Verified email at yale.edu. Information about your use of this site is shared with Google. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning.
SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. I graduated with a PhD from Princeton University in 2018. with Yair Carmon, Aaron Sidford and Kevin Tian
with Aaron Sidford
Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. xwXSsN`$!l{@ $@TR)XZ(
RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y 2023. . Lower bounds for finding stationary points II: first-order methods. CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. Aaron Sidford's Profile | Stanford Profiles Np%p `a!2D4! One research focus are dynamic algorithms (i.e. 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. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. << 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. Etude for the Park City Math Institute Undergraduate Summer School. 113 * 2016: The system can't perform the operation now. 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. [last name]@stanford.edu where [last name]=sidford. Best Paper Award. With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. Sivakanth Gopi at Microsoft Research Fresh Faculty: Theoretical computer scientist Aaron Sidford joins MS&E I also completed my undergraduate degree (in mathematics) at MIT. Gregory Valiant Homepage - Stanford University
Their, This "Cited by" count includes citations to the following articles in Scholar.
2013. pdf, Fourier Transformation at a Representation, Annie Marsden. . BayLearn, 2021, On the Sample Complexity of Average-reward MDPs
Navajo Math Circles Instructor. With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. Follow. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . 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. [pdf] [poster]
ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods
2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). This is the academic homepage of Yang Liu (I publish under Yang P. Liu). I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. [pdf]
aaron sidford cv natural fibrin removal - libiot.kku.ac.th ICML, 2016. We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. 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. Improved Lower Bounds for Submodular Function Minimization. he Complexity of Infinite-Horizon General-Sum Stochastic Games, Yujia Jin, Vidya Muthukumar, Aaron Sidford, Innovations in Theoretical Computer Science (ITCS 202, air Carmon, Danielle Hausler, Arun Jambulapati, and Yujia Jin, Advances in Neural Information Processing Systems (NeurIPS 2022), Moses Charikar, Zhihao Jiang, and Kirankumar Shiragur, Advances in Neural Information Processing Systems (NeurIPS 202, n Symposium on Foundations of Computer Science (FOCS 2022) (, International Conference on Machine Learning (ICML 2022) (, Conference on Learning Theory (COLT 2022) (, International Colloquium on Automata, Languages and Programming (ICALP 2022) (, In Symposium on Theory of Computing (STOC 2022) (, In Symposium on Discrete Algorithms (SODA 2022) (, In Advances in Neural Information Processing Systems (NeurIPS 2021) (, In Conference on Learning Theory (COLT 2021) (, In International Conference on Machine Learning (ICML 2021) (, In Symposium on Theory of Computing (STOC 2021) (, In Symposium on Discrete Algorithms (SODA 2021) (, In Innovations in Theoretical Computer Science (ITCS 2021) (, In Conference on Neural Information Processing Systems (NeurIPS 2020) (, In Symposium on Foundations of Computer Science (FOCS 2020) (, In International Conference on Artificial Intelligence and Statistics (AISTATS 2020) (, In International Conference on Machine Learning (ICML 2020) (, In Conference on Learning Theory (COLT 2020) (, In Symposium on Theory of Computing (STOC 2020) (, In International Conference on Algorithmic Learning Theory (ALT 2020) (, In Symposium on Discrete Algorithms (SODA 2020) (, In Conference on Neural Information Processing Systems (NeurIPS 2019) (, In Symposium on Foundations of Computer Science (FOCS 2019) (, In Conference on Learning Theory (COLT 2019) (, In Symposium on Theory of Computing (STOC 2019) (, In Symposium on Discrete Algorithms (SODA 2019) (, In Conference on Neural Information Processing Systems (NeurIPS 2018) (, In Symposium on Foundations of Computer Science (FOCS 2018) (, In Conference on Learning Theory (COLT 2018) (, In Symposium on Discrete Algorithms (SODA 2018) (, In Innovations in Theoretical Computer Science (ITCS 2018) (, In Symposium on Foundations of Computer Science (FOCS 2017) (, In International Conference on Machine Learning (ICML 2017) (, In Symposium on Theory of Computing (STOC 2017) (, In Symposium on Foundations of Computer Science (FOCS 2016) (, In Symposium on Theory of Computing (STOC 2016) (, In Conference on Learning Theory (COLT 2016) (, In International Conference on Machine Learning (ICML 2016) (, In International Conference on Machine Learning (ICML 2016). International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods
CoRR abs/2101.05719 ( 2021 )
Aaron Sidford.
We also provide two . 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 Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020
I was fortunate to work with Prof. Zhongzhi Zhang. [1811.10722] Solving Directed Laplacian Systems in Nearly-Linear Time (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. 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. "t a","H Aaron Sidford receives best paper award at COLT 2022 AISTATS, 2021. to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching
With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. Aaron Sidford - Google Scholar 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. (ACM Doctoral Dissertation Award, Honorable Mention.) I enjoy understanding the theoretical ground of many algorithms that are
Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. Email: sidford@stanford.edu. Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford. 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
Where Is Inhuman Resources Filmed, Articles A
Where Is Inhuman Resources Filmed, Articles A