Yujia Jin. Publications and Preprints. 9-21. Title. 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. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. 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. View Full Stanford Profile. rl1 I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. I graduated with a PhD from Princeton University in 2018. Follow. The design of algorithms is traditionally a discrete endeavor. 113 * 2016: The system can't perform the operation now. [pdf] [poster] 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. Before Stanford, I worked with John Lafferty at the University of Chicago. Thesis, 2016. pdf. Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian aaron sidford cvnatural fibrin removalnatural fibrin removal If you see any typos or issues, feel free to email me. The system can't perform the operation now. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). missouri noodling association president cnn. with Yair Carmon, Kevin Tian and Aaron Sidford I am a senior researcher in the Algorithms group at Microsoft Research Redmond. Prof. Erik Demaine TAs: Timothy Kaler, Aaron Sidford [Home] [Assignments] [Open Problems] [Accessibility] sample frame from lecture videos Data structures play a central role in modern computer science. BayLearn, 2019, "Computing stationary solution for multi-agent RL is hard: Indeed, CCE for simultaneous games and NE for turn-based games are both PPAD-hard. Faculty Spotlight: Aaron Sidford. ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. theory and graph applications. 2021 - 2022 Postdoc, Simons Institute & UC . Efficient Convex Optimization Requires Superlinear Memory. with Yair Carmon, Aaron Sidford and 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. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 /Filter /FlateDecode 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 . Faculty and Staff Intranet. Associate Professor of . ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). 475 Via Ortega We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. << van vu professor, yale Verified email at yale.edu. when do tulips bloom in maryland; indo pacific region upsc 5 0 obj ", "Team-convex-optimization for solving discounted and average-reward MDPs! to be advised by Prof. Dongdong Ge. If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. Some I am still actively improving and all of them I am happy to continue polishing. [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. Intranet Web Portal. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Stanford, CA 94305 Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration If you see any typos or issues, feel free to email me. Our method improves upon the convergence rate of previous state-of-the-art linear programming . 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. Email: [name]@stanford.edu 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. Simple MAP inference via low-rank relaxations. [pdf] [slides] International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle Computer Science. with Aaron Sidford Summer 2022: I am currently a research scientist intern at DeepMind in London. A nearly matching upper and lower bound for constant error here! D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. CoRR abs/2101.05719 ( 2021 ) [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching Verified email at stanford.edu - Homepage. [pdf] International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 pdf, Sequential Matrix Completion. Try again later. I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. I was fortunate to work with Prof. Zhongzhi Zhang. Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. One research focus are dynamic algorithms (i.e. Yang P. Liu, Aaron Sidford, Department of Mathematics Conference on Learning Theory (COLT), 2015. We forward in this generation, Triumphantly. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). 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. In International Conference on Machine Learning (ICML 2016). how . By using this site, you agree to its use of cookies. My research focuses on AI and machine learning, with an emphasis on robotics applications. With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . Here is a slightly more formal third-person biography, and here is a recent-ish CV. To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization The ones marked, 2014 IEEE 55th Annual Symposium on Foundations of Computer Science, 424-433, SIAM Journal on Optimization 28 (2), 1751-1772, Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 1049-1065, 2013 ieee 54th annual symposium on foundations of computer science, 147-156, Proceedings of the forty-fifth annual ACM symposium on Theory of computing, MB Cohen, YT Lee, C Musco, C Musco, R Peng, A Sidford, Proceedings of the 2015 Conference on Innovations in Theoretical Computer, Advances in Neural Information Processing Systems 31, M Kapralov, YT Lee, CN Musco, CP Musco, A Sidford, SIAM Journal on Computing 46 (1), 456-477, P Jain, S Kakade, R Kidambi, P Netrapalli, A Sidford, MB Cohen, YT Lee, G Miller, J Pachocki, A Sidford, Proceedings of the forty-eighth annual ACM symposium on Theory of Computing, International Conference on Machine Learning, 2540-2548, P Jain, SM Kakade, R Kidambi, P Netrapalli, A Sidford, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 230-249, Mathematical Programming 184 (1-2), 71-120, P Jain, C Jin, SM Kakade, P Netrapalli, A Sidford, International conference on machine learning, 654-663, Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete, D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford, New articles related to this author's research, Path finding methods for linear programming: Solving linear programs in o (vrank) iterations and faster algorithms for maximum flow, Accelerated methods for nonconvex optimization, An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations, A faster cutting plane method and its implications for combinatorial and convex optimization, Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems, A simple, combinatorial algorithm for solving SDD systems in nearly-linear time, Uniform sampling for matrix approximation, Near-optimal time and sample complexities for solving Markov decision processes with a generative model, Single pass spectral sparsification in dynamic streams, Parallelizing stochastic gradient descent for least squares regression: mini-batching, averaging, and model misspecification, Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization, Accelerating stochastic gradient descent for least squares regression, Efficient inverse maintenance and faster algorithms for linear programming, Lower bounds for finding stationary points I, Streaming pca: Matching matrix bernstein and near-optimal finite sample guarantees for ojas algorithm, Convex Until Proven Guilty: Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions, Competing with the empirical risk minimizer in a single pass, Variance reduced value iteration and faster algorithms for solving Markov decision processes, Robust shift-and-invert preconditioning: Faster and more sample efficient algorithms for eigenvector computation.
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