publications | Daogao Liu In this talk, I will present a new algorithm for solving linear programs. Vatsal Sharan - GitHub Pages 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University Navajo Math Circles Instructor. to be advised by Prof. Dongdong Ge. Mary Wootters - Google Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford. 4026. aaron sidford cvis sea bass a bony fish to eat. David P. Woodruff . Improves the stochas-tic convex optimization problem in parallel and DP setting. to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. Intranet Web Portal. ", Applied Math at Fudan ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. COLT, 2022. 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space Summer 2022: I am currently a research scientist intern at DeepMind in London. SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. % Fresh Faculty: Theoretical computer scientist Aaron Sidford joins MS&E Associate Professor of . Neural Information Processing Systems (NeurIPS), 2014. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA 9-21. MS&E213 / CS 269O - Introduction to Optimization Theory In submission. aaron sidford cv [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. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs with Arun Jambulapati, Aaron Sidford and 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." ReSQueing Parallel and Private Stochastic Convex Optimization. University, Research Institute for Interdisciplinary Sciences (RIIS) at Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 Stanford University data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. By using this site, you agree to its use of cookies. Lower Bounds for Finding Stationary Points II: First-Order Methods [pdf] 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. Another research focus are optimization algorithms. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration Huang Engineering Center With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. 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. stream [pdf] [poster] Improved Lower Bounds for Submodular Function Minimization. Yang P. Liu - GitHub Pages ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). 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. Many of my results use fast matrix multiplication 22nd Max Planck Advanced Course on the Foundations of Computer Science Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. Before Stanford, I worked with John Lafferty at the University of Chicago. Yujia Jin. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& pdf, Sequential Matrix Completion. F+s9H Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. 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. ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. We forward in this generation, Triumphantly. In each setting we provide faster exact and approximate algorithms. Here are some lecture notes that I have written over the years. Allen Liu. with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian My interests are in the intersection of algorithms, statistics, optimization, and machine learning. [pdf] with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian Follow. International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle From 2016 to 2018, I also worked in I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. Thesis, 2016. pdf. Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. Email: [name]@stanford.edu To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. University, where Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. Cameron Musco - Manning College of Information & Computer Sciences Aleksander Mdry; Generalized preconditioning and network flow problems 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. dblp: Daogao Liu with Kevin Tian and Aaron Sidford I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. [pdf] [slides] [pdf] [talk] [poster] . I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. 2021 - 2022 Postdoc, Simons Institute & UC . Aaron Sidford | Stanford Online We also provide two . I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. 2013. 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). Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. theory and graph applications. If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. I graduated with a PhD from Princeton University in 2018. small tool to obtain upper bounds of such algebraic algorithms. University of Cambridge MPhil. It was released on november 10, 2017. 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. AISTATS, 2021. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. MS&E welcomes new faculty member, Aaron Sidford ! Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. Aaron Sidford - All Publications in Mathematics and B.A. Semantic parsing on Freebase from question-answer pairs. Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. I am broadly interested in optimization problems, sometimes in the intersection with machine learning ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). Simple MAP inference via low-rank relaxations. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games 2021. 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 . aaron sidford cvnatural fibrin removalnatural fibrin removal Information about your use of this site is shared with Google. KTH in Stockholm, Sweden, and my BSc + MSc at the Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Roy Frostig, Sida Wang, Percy Liang, Chris Manning. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG Alcatel flip phones are also ready to purchase with consumer cellular. theses are protected by copyright. (, 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 (. [pdf] [poster] David P. Woodruff - Carnegie Mellon University The system can't perform the operation now. Faster energy maximization for faster maximum flow. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford of practical importance. 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. %PDF-1.4 5 0 obj We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . missouri noodling association president cnn. Secured intranet portal for faculty, staff and students. Selected for oral presentation. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. Faculty and Staff Intranet. Lower bounds for finding stationary points II: first-order methods. Roy Frostig - Stanford University Google Scholar Digital Library; Russell Lyons and Yuval Peres. [pdf] With Yair Carmon, John C. Duchi, and Oliver Hinder. With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. 4 0 obj {{{;}#q8?\. [PDF] Faster Algorithms for Computing the Stationary Distribution 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).