aaron sidford cv
Etude for the Park City Math Institute Undergraduate Summer School. 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University publications by categories in reversed chronological order. van vu professor, yale Verified email at yale.edu. Faculty Spotlight: Aaron Sidford. 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 . what is a blind trust for lottery winnings; ithaca college park school scholarships; Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. Their, This "Cited by" count includes citations to the following articles in Scholar. To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. [pdf] [talk] [poster] Aaron Sidford Stanford University Verified email at stanford.edu. ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. 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 appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching stream [pdf] [poster] with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford In International Conference on Machine Learning (ICML 2016). In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. aaron sidford cvnatural fibrin removalnatural fibrin removal Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. Selected recent papers . Improved Lower Bounds for Submodular Function Minimization. Email: [name]@stanford.edu Goethe University in Frankfurt, Germany. Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) Yang P. Liu, Aaron Sidford, Department of Mathematics MI #~__ Q$.R$sg%f,a6GTLEQ!/B)EogEA?l kJ^- \?l{ P&d\EAt{6~/fJq2bFn6g0O"yD|TyED0Ok-\~[`|4P,w\A8vD$+)%@P4 0L ` ,\@2R 4f I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. . I completed my PhD at 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! %PDF-1.4 Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. KTH in Stockholm, Sweden, and my BSc + MSc at the Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. 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. Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. [pdf] SODA 2023: 5068-5089. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . Full CV is available here. . to be advised by Prof. Dongdong Ge. SODA 2023: 4667-4767. Alcatel flip phones are also ready to purchase with consumer cellular. Try again later. Aaron Sidford. Aaron Sidford. "t a","H Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space 9-21. [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. 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. With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. [pdf] Done under the mentorship of M. Malliaris. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). 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. My long term goal is to bring robots into human-centered domains such as homes and hospitals. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. Huang Engineering Center rl1 Semantic parsing on Freebase from question-answer pairs. [pdf] [poster] ! Roy Frostig, Sida Wang, Percy Liang, Chris Manning. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 Applying this technique, we prove that any deterministic SFM algorithm . Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. ", "A short version of the conference publication under the same title. The design of algorithms is traditionally a discrete endeavor. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . If you see any typos or issues, feel free to email me. Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games Student Intranet. Slides from my talk at ITCS. Many of my results use fast matrix multiplication They will share a $10,000 prize, with financial sponsorship provided by Google Inc. 2021 - 2022 Postdoc, Simons Institute & UC . I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. A nearly matching upper and lower bound for constant error here! Title. About Me. Source: appliancesonline.com.au. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. I also completed my undergraduate degree (in mathematics) at MIT. MS&E welcomes new faculty member, Aaron Sidford ! 2017. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! With Yair Carmon, John C. Duchi, and Oliver Hinder. ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG BayLearn, 2021, On the Sample Complexity of Average-reward MDPs I graduated with a PhD from Princeton University in 2018. % >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . 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. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Navajo Math Circles Instructor. missouri noodling association president cnn. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. Selected for oral presentation. CV (last updated 01-2022): PDF Contact. 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. with Aaron Sidford With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). I am fortunate to be advised by Aaron Sidford. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). I am an Assistant Professor in the School of Computer Science at Georgia Tech. [last name]@stanford.edu where [last name]=sidford. 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. Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. with Yair Carmon, Arun Jambulapati and Aaron Sidford Neural Information Processing Systems (NeurIPS), 2014. [pdf] [talk] with Yair Carmon, Aaron Sidford and Kevin Tian Group Resources. I enjoy understanding the theoretical ground of many algorithms that are 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. I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. with Aaron Sidford [pdf] [poster] 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 . Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization SHUFE, where I was fortunate [pdf] 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. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. Stanford, CA 94305 Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. /Creator (Apache FOP Version 1.0) ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). Before attending Stanford, I graduated from MIT in May 2018. 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 of practical importance. Computer Science. Articles Cited by Public access. Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. F+s9H 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. with Aaron Sidford Faster energy maximization for faster maximum flow. Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. 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 . 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 However, many advances have come from a continuous viewpoint. [pdf] The system can't perform the operation now. in Mathematics and B.A. [pdf] [poster] with Yair Carmon, Aaron Sidford and Kevin Tian By using this site, you agree to its use of cookies. The following articles are merged in Scholar. xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. "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. 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. Main Menu. Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). 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).