! aaron sidford cvnatural fibrin removalnatural fibrin removal AISTATS, 2021. View Full Stanford Profile. Yin Tat Lee and Aaron Sidford. We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. sidford@stanford.edu. Slides from my talk at ITCS. The design of algorithms is traditionally a discrete endeavor. } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ They will share a $10,000 prize, with financial sponsorship provided by Google Inc. 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 . I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions Try again later. NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games 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. 475 Via Ortega Yang P. Liu, Aaron Sidford, Department of Mathematics Navajo Math Circles Instructor. With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. 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). ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? /Producer (Apache FOP Version 1.0) With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). Contact. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. 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 Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 [pdf] Personal Website. Secured intranet portal for faculty, staff and students. CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. She was 19 years old and looking forward to the start of classes and reuniting with her college pals. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. Mail Code. In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. Np%p `a!2D4! /Filter /FlateDecode Aaron Sidford Stanford University Verified email at stanford.edu. (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. My research focuses on AI and machine learning, with an emphasis on robotics applications. The site facilitates research and collaboration in academic endeavors. In submission. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). . [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." In Sidford's dissertation, Iterative Methods, Combinatorial . 4 0 obj Aaron Sidford. << [pdf] I am an Assistant Professor in the School of Computer Science at Georgia Tech. Group Resources. Etude for the Park City Math Institute Undergraduate Summer School. with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian Neural Information Processing Systems (NeurIPS), 2014. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. I regularly advise Stanford students from a variety of departments. in Mathematics and B.A. We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. COLT, 2022. arXiv | code | conference pdf (alphabetical authorship), Annie Marsden, John Duchi and Gregory Valiant, Misspecification in Prediction Problems and Robustness via Improper Learning. There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. Before attending Stanford, I graduated from MIT in May 2018. [pdf] July 8, 2022. ICML, 2016. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. 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). [pdf] [poster] CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. Yair Carmon. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. [pdf] [poster] 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. 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. In each setting we provide faster exact and approximate algorithms. 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. to be advised by Prof. Dongdong Ge. . Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. . stream [pdf] pdf, Sequential Matrix Completion. My CV. by Aaron Sidford. I am broadly interested in optimization problems, sometimes in the intersection with machine learning Improves the stochas-tic convex optimization problem in parallel and DP setting. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. Student Intranet. International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. Some I am still actively improving and all of them I am happy to continue polishing. SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y /Creator (Apache FOP Version 1.0) with Aaron Sidford Assistant Professor of Management Science and Engineering and of Computer Science. 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 . I am a senior researcher in the Algorithms group at Microsoft Research Redmond. . I enjoy understanding the theoretical ground of many algorithms that are Many of my results use fast matrix multiplication Stanford University. with Yang P. Liu and Aaron Sidford. We also provide two . Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. ", Applied Math at Fudan To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. 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. >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. small tool to obtain upper bounds of such algebraic algorithms. 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 . I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. 2013. Faculty Spotlight: Aaron Sidford. Algorithms Optimization and Numerical Analysis. With Cameron Musco and Christopher Musco. 5 0 obj Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. Another research focus are optimization algorithms. ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! arXiv preprint arXiv:2301.00457, 2023 arXiv. Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent Unlike previous ADFOCS, this year the event will take place over the span of three weeks. 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. CV (last updated 01-2022): PDF Contact. 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). "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. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. with Yair Carmon, Kevin Tian and Aaron Sidford David P. Woodruff . ", "A short version of the conference publication under the same title. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . with Yair Carmon, Arun Jambulapati and Aaron Sidford Enrichment of Network Diagrams for Potential Surfaces. Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space /N 3 Faculty and Staff Intranet. SODA 2023: 4667-4767. "t a","H data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. We forward in this generation, Triumphantly. Office: 380-T Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. ", "Team-convex-optimization for solving discounted and average-reward MDPs! [pdf] [poster] Nearly Optimal Communication and Query Complexity of Bipartite Matching . A nearly matching upper and lower bound for constant error here! Main Menu. 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. 2021 - 2022 Postdoc, Simons Institute & UC . with Aaron Sidford The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . [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. My long term goal is to bring robots into human-centered domains such as homes and hospitals. If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. In International Conference on Machine Learning (ICML 2016). (, In Symposium on Foundations of Computer Science (FOCS 2015) (, In Conference on Learning Theory (COLT 2015) (, In International Conference on Machine Learning (ICML 2015) (, In Innovations in Theoretical Computer Science (ITCS 2015) (, In Symposium on Fondations of Computer Science (FOCS 2013) (, In Symposium on the Theory of Computing (STOC 2013) (, Book chapter in Building Bridges II: Mathematics of Laszlo Lovasz, 2020 (, Journal of Machine Learning Research, 2017 (. I am broadly interested in mathematics and theoretical computer science. My interests are in the intersection of algorithms, statistics, optimization, and machine learning. Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan with Yair Carmon, Aaron Sidford and Kevin Tian Abstract. Winter 2020 Teaching assistant for EE364a: Convex Optimization I taught by John Duchi, Fall 2018 Teaching assitant for CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019 taught by Greg Valiant. to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching SHUFE, where I was fortunate Aaron Sidford. Alcatel flip phones are also ready to purchase with consumer cellular. Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. Management Science & Engineering Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . F+s9H [pdf] [talk] Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA 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 "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& With Yair Carmon, John C. Duchi, and Oliver Hinder. IEEE, 147-156. 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. 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 . ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. University, where 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.
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