aaron sidford cv

Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. 4026. I am fortunate to be advised by Aaron Sidford. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. Stanford University Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions About Me. >> Title. 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 2017. Improves the stochas-tic convex optimization problem in parallel and DP setting. Yujia Jin. with Arun Jambulapati, Aaron Sidford and Kevin Tian Aaron Sidford. She was 19 years old and looking forward to the start of classes and reuniting with her college pals. 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 . Stanford, CA 94305 Anup B. Rao. pdf, Sequential Matrix Completion. I am broadly interested in mathematics and theoretical computer science. A nearly matching upper and lower bound for constant error here! To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. [last name]@stanford.edu where [last name]=sidford. David P. Woodruff . Here are some lecture notes that I have written over the years. Yang P. Liu, Aaron Sidford, Department of Mathematics We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. The design of algorithms is traditionally a discrete endeavor. Email: sidford@stanford.edu. In submission. (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. /Filter /FlateDecode I hope you enjoy the content as much as I enjoyed teaching the class and if you have questions or feedback on the note, feel free to email me. We prove that deterministic first-order methods, even applied to arbitrarily smooth functions, cannot achieve convergence rates in $$ better than $^{-8/5}$, which is within $^{-1/15}\\log\\frac{1}$ of the best known rate for such . /CreationDate (D:20230304061109-08'00') (ACM Doctoral Dissertation Award, Honorable Mention.) University, Research Institute for Interdisciplinary Sciences (RIIS) at xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y University, where ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. 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). 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. with Yair Carmon, Aaron Sidford and Kevin Tian ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. Journal of Machine Learning Research, 2017 (arXiv). ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). Goethe University in Frankfurt, Germany. 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. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games My interests are in the intersection of algorithms, statistics, optimization, and machine learning. [pdf] 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. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. [pdf] [talk] [poster] Huang Engineering Center Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). Slides from my talk at ITCS. ICML, 2016. 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. 4 0 obj With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Selected recent papers . In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . [pdf] [poster] with Yair Carmon, Aaron Sidford and Kevin Tian ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian [pdf] AISTATS, 2021. My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). Np%p `a!2D4! However, even restarting can be a hard task here. CoRR abs/2101.05719 ( 2021 ) ReSQueing Parallel and Private Stochastic Convex Optimization. [pdf] [pdf] with Yair Carmon, Aaron Sidford and Kevin Tian ", "Sample complexity for average-reward MDPs? Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. Conference on Learning Theory (COLT), 2015. >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. . (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. I have the great privilege and good fortune of advising the following PhD students: I have also had the great privilege and good fortune of advising the following PhD students who have now graduated: Kirankumar Shiragur (co-advised with Moses Charikar) - PhD 2022, AmirMahdi Ahmadinejad (co-advised with Amin Saberi) - PhD 2020, Yair Carmon (co-advised with John Duchi) - PhD 2020. This site uses cookies from Google to deliver its services and to analyze traffic. Eigenvalues of the laplacian and their relationship to the connectedness of a graph. Abstract. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. /Creator (Apache FOP Version 1.0) Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games 5 0 obj ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. Applying this technique, we prove that any deterministic SFM algorithm . The following articles are merged in Scholar. Efficient Convex Optimization Requires Superlinear Memory. The site facilitates research and collaboration in academic endeavors. ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. Email: [name]@stanford.edu I enjoy understanding the theoretical ground of many algorithms that are Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. Before attending Stanford, I graduated from MIT in May 2018. . aaron sidford cvis sea bass a bony fish to eat. United States. Yair Carmon. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. I was fortunate to work with Prof. Zhongzhi Zhang. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. Simple MAP inference via low-rank relaxations. Publications and Preprints. Yin Tat Lee and Aaron Sidford. I also completed my undergraduate degree (in mathematics) at MIT. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). Here are some lecture notes that I have written over the years. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! CV (last updated 01-2022): PDF Contact. My research focuses on AI and machine learning, with an emphasis on robotics applications. 2016. COLT, 2022. arXiv | code | conference pdf (alphabetical authorship), Annie Marsden, John Duchi and Gregory Valiant, Misspecification in Prediction Problems and Robustness via Improper Learning. Management Science & Engineering 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. when do tulips bloom in maryland; indo pacific region upsc With Cameron Musco and Christopher Musco. NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games ?_l) With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. 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. [pdf] If you see any typos or issues, feel free to email me. IEEE, 147-156. with Kevin Tian and Aaron Sidford Allen Liu. 2021. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Follow. Full CV is available here. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. Summer 2022: I am currently a research scientist intern at DeepMind in London. [pdf] [slides] sidford@stanford.edu. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. Before Stanford, I worked with John Lafferty at the University of Chicago. 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. {{{;}#q8?\. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods by Aaron Sidford. July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). University of Cambridge MPhil. in Mathematics and B.A. Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. 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. of practical importance. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. SHUFE, where I was fortunate stream with Aaron Sidford Thesis, 2016. pdf. with Yang P. Liu and Aaron Sidford. By using this site, you agree to its use of cookies. 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. SODA 2023: 5068-5089. I often do not respond to emails about applications. We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. I am broadly interested in mathematics and theoretical computer science. I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. Faculty Spotlight: Aaron Sidford. Yujia Jin. with Hilal Asi, Yair Carmon, Arun Jambulapati and 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). ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods Annie Marsden. [pdf] [talk] I am an Assistant Professor in the School of Computer Science at Georgia Tech. Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! 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. Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . 2021 - 2022 Postdoc, Simons Institute & UC . Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. van vu professor, yale Verified email at yale.edu. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). Contact. 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 . International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle

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