Kirankumar Shiragur | Data Science Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 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. Aviv Tamar - Reinforcement Learning Research Labs - Technion to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. /Producer (Apache FOP Version 1.0) ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. My CV. ReSQueing Parallel and Private Stochastic Convex Optimization. " 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. Google Scholar Digital Library; Russell Lyons and Yuval Peres. Yujia Jin. Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. University, Research Institute for Interdisciplinary Sciences (RIIS) at Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. She was 19 years old and looking forward to the start of classes and reuniting with her college pals. Best Paper Award. aaron sidford cvis sea bass a bony fish to eat. aaron sidford cv This is the academic homepage of Yang Liu (I publish under Yang P. Liu). . Aaron Sidford Stanford University Verified email at stanford.edu. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG Secured intranet portal for faculty, staff and students. Aaron Sidford - Stanford University Aleksander Mdry; Generalized preconditioning and network flow problems Adam Bouland - Stanford University I am an Assistant Professor in the School of Computer Science at Georgia Tech. By using this site, you agree to its use of cookies. Here is a slightly more formal third-person biography, and here is a recent-ish CV. I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. with Yair Carmon, Aaron Sidford and Kevin Tian ", Applied Math at Fudan View Full Stanford Profile. Yujia Jin. 475 Via Ortega Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. with Aaron Sidford [pdf] ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). In Symposium on Foundations of Computer Science (FOCS 2017) (arXiv), "Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions, With Yair Carmon, John C. Duchi, and Oliver Hinder, In International Conference on Machine Learning (ICML 2017) (arXiv), Almost-Linear-Time Algorithms for Markov Chains and New Spectral Primitives for Directed Graphs, With Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, Anup B. Rao, and, Adrian Vladu, In Symposium on Theory of Computing (STOC 2017), Subquadratic Submodular Function Minimization, With Deeparnab Chakrabarty, Yin Tat Lee, and Sam Chiu-wai Wong, In Symposium on Theory of Computing (STOC 2017) (arXiv), Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More, With Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, and Adrian Vladu, In Symposium on Foundations of Computer Science (FOCS 2016) (arXiv), With Michael B. Cohen, Yin Tat Lee, Gary L. Miller, and Jakub Pachocki, In Symposium on Theory of Computing (STOC 2016) (arXiv), With Alina Ene, Gary L. Miller, and Jakub Pachocki, Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm, With Prateek Jain, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli, In Conference on Learning Theory (COLT 2016) (arXiv), Principal Component Projection Without Principal Component Analysis, With Roy Frostig, Cameron Musco, and Christopher Musco, In International Conference on Machine Learning (ICML 2016) (arXiv), Faster Eigenvector Computation via Shift-and-Invert Preconditioning, With Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, and Praneeth Netrapalli, Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. Selected for oral presentation. I graduated with a PhD from Princeton University in 2018. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). 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 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. My interests are in the intersection of algorithms, statistics, optimization, and machine learning. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss Microsoft Research Faculty Fellowship 2020: Researchers in academia at Improved Lower Bounds for Submodular Function Minimization. . 2016. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. [1811.10722] Solving Directed Laplacian Systems in Nearly-Linear Time /Length 11 0 R Publications and Preprints. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. I am fortunate to be advised by Aaron Sidford . Semantic parsing on Freebase from question-answer pairs. Source: appliancesonline.com.au. Faster Matroid Intersection Princeton University 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 . "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& . 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, Arun Jambulapati and Aaron Sidford I am broadly interested in mathematics and theoretical computer science. Improves the stochas-tic convex optimization problem in parallel and DP setting. However, even restarting can be a hard task here. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. aaron sidford cv natural fibrin removal - libiot.kku.ac.th They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. I am broadly interested in optimization problems, sometimes in the intersection with machine learning "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. Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. It was released on november 10, 2017. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. Selected recent papers . Follow. Etude for the Park City Math Institute Undergraduate Summer School. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. July 8, 2022. ", "Sample complexity for average-reward MDPs? publications | Daogao Liu About Me. Here are some lecture notes that I have written over the years. 4 0 obj 2021. David P. Woodruff - Carnegie Mellon University Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. I regularly advise Stanford students from a variety of departments. ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? % In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. KTH in Stockholm, Sweden, and my BSc + MSc at the SHUFE, where I was fortunate Neural Information Processing Systems (NeurIPS), 2014. in Mathematics and B.A. Enrichment of Network Diagrams for Potential Surfaces. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. United States. >> 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. 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. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Title. We forward in this generation, Triumphantly. Aaron Sidford with Yang P. Liu and Aaron Sidford. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Done under the mentorship of M. Malliaris. I enjoy understanding the theoretical ground of many algorithms that are [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. This site uses cookies from Google to deliver its services and to analyze traffic. theses are protected by copyright. [pdf] [poster] Anup B. Rao. 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. >> xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y what is a blind trust for lottery winnings; ithaca college park school scholarships; Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. 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. I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. Advanced Data Structures (6.851) - Massachusetts Institute of Technology Aaron Sidford receives best paper award at COLT 2022 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 - Selected Publications ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. . 2015 Doctoral Dissertation Award - Association for Computing Machinery
Cabins For Sale At Pymatuning Lake In Jamestown Pa, Highland Village Condos For Rent Baton Rouge, University Of Denver Psyd Ranking, Dollar General Electric Skillet, Cook County, Mn Police Reports, Articles A