[pdf] [poster]
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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.
The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. (ACM Doctoral Dissertation Award, Honorable Mention.) 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. I enjoy understanding the theoretical ground of many algorithms that are
I completed my PhD at
Roy Frostig - Stanford University with Yair Carmon, Kevin Tian and Aaron Sidford
Etude for the Park City Math Institute Undergraduate Summer School. 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. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford
Aaron Sidford - Teaching "FV %H"Hr
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c0 L& 9cX& 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. Annie Marsden. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. Neural Information Processing Systems (NeurIPS), 2014. 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. United States.
publications | Daogao Liu ReSQueing Parallel and Private Stochastic Convex Optimization. Improves the stochas-tic convex optimization problem in parallel and DP setting. of practical importance. Their, This "Cited by" count includes citations to the following articles in Scholar. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games
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RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y " 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. This is the academic homepage of Yang Liu (I publish under Yang P. Liu).
Fresh Faculty: Theoretical computer scientist Aaron Sidford joins MS&E She was 19 years old and looking forward to the start of classes and reuniting with her college pals.
/Producer (Apache FOP Version 1.0) "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.
aaron sidford cv natural fibrin removal - libiot.kku.ac.th 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 . Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. With Yair Carmon, John C. Duchi, and Oliver Hinder.
Stanford University Aleksander Mdry; Generalized preconditioning and network flow problems
Algorithms Optimization and Numerical Analysis.
arXiv preprint arXiv:2301.00457, 2023 arXiv. University of Cambridge MPhil. Summer 2022: I am currently a research scientist intern at DeepMind in London. Personal Website. We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. Stanford University. 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. aaron sidford cvnatural fibrin removalnatural fibrin removal With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli.
2013. with Yair Carmon, Aaron Sidford and Kevin Tian
Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. [pdf]
We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. Computer Science. Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. Stanford, CA 94305 In International Conference on Machine Learning (ICML 2016). resume/cv; publications. 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. [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. 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).
aaron sidford cv International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods
Alcatel flip phones are also ready to purchase with consumer cellular. Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. . I graduated with a PhD from Princeton University in 2018. 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. [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. Efficient Convex Optimization Requires Superlinear Memory.
", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers.
Aaron Sidford - Google Scholar I was fortunate to work with Prof. Zhongzhi Zhang.
dblp: Yin Tat Lee Anup B. Rao. in Mathematics and B.A. [pdf] [slides]
My research is on the design and theoretical analysis of efficient algorithms and data structures. 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
Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. 2023. .
", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! with Vidya Muthukumar and Aaron Sidford
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. Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford. [pdf] [poster]
. Here are some lecture notes that I have written over the years. with Arun Jambulapati, Aaron Sidford and Kevin Tian
Done under the mentorship of M. Malliaris. ", "A short version of the conference publication under the same title. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. Thesis, 2016. pdf.
Iterative methods, combinatorial optimization, and linear programming 2021 - 2022 Postdoc, Simons Institute & UC .
Yujia Jin - Stanford University
Publications | Jakub Pachocki - Harvard University In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. Faculty and Staff Intranet.
Email /
aaron sidford cvis sea bass a bony fish to eat. 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 . [pdf]
I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees.
Aaron Sidford's Profile | Stanford Profiles Roy Frostig, Sida Wang, Percy Liang, Chris Manning.
Before attending Stanford, I graduated from MIT in May 2018. Huang Engineering Center
Aaron Sidford - My Group Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries
Jan van den Brand Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory.
[PDF] Faster Algorithms for Computing the Stationary Distribution Many of my results use fast matrix multiplication
2017. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures.
CME 305/MS&E 316: Discrete Mathematics and Algorithms
It was released on november 10, 2017. February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. Slides from my talk at ITCS.
Mary Wootters - Google Associate Professor of . CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. Our method improves upon the convergence rate of previous state-of-the-art linear programming . Aaron Sidford.
Aaron Sidford's Homepage - Stanford University This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). 5 0 obj CV (last updated 01-2022): PDF Contact. 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 Faster energy maximization for faster maximum flow. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM).
Lower Bounds for Finding Stationary Points II: First-Order Methods
I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures.
Publications | Salil Vadhan We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University.
stream Enrichment of Network Diagrams for Potential Surfaces.
Aaron Sidford - All Publications Google Scholar; Probability on trees and . >> F+s9H David P. Woodruff . ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. Secured intranet portal for faculty, staff and students.
Aaron Sidford | Management Science and Engineering ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. About Me. Applying this technique, we prove that any deterministic SFM algorithm . I received a B.S. 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. >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012.
Anup B. Rao - Google Scholar
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