Yujia Jin. /CreationDate (D:20230304061109-08'00') IEEE, 147-156. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University 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). SODA 2023: 4667-4767. . Np%p `a!2D4! Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. [pdf] [talk] Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. Anup B. Rao. [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. Enrichment of Network Diagrams for Potential Surfaces. I am fortunate to be advised by Aaron Sidford . Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. Navajo Math Circles Instructor. If you see any typos or issues, feel free to email me. Publications and Preprints. Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) (ACM Doctoral Dissertation Award, Honorable Mention.) We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . AISTATS, 2021. with Yair Carmon, Arun Jambulapati and Aaron Sidford 4026. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA Aaron Sidford. Yang P. Liu, Aaron Sidford, Department of Mathematics with Vidya Muthukumar and Aaron Sidford However, even restarting can be a hard task here. Summer 2022: I am currently a research scientist intern at DeepMind in London. The authors of most papers are ordered alphabetically. Source: appliancesonline.com.au. resume/cv; publications. aaron sidford cvnatural fibrin removalnatural fibrin removal Email: sidford@stanford.edu. 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. Huang Engineering Center pdf, Sequential Matrix Completion. Annie Marsden. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian Faster energy maximization for faster maximum flow. AISTATS, 2021. "t a","H ", "A short version of the conference publication under the same title. With Yair Carmon, John C. Duchi, and Oliver Hinder. [pdf] We also provide two . [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. Google Scholar Digital Library; Russell Lyons and Yuval Peres. Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. /Filter /FlateDecode From 2016 to 2018, I also worked in Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. [pdf] [poster] Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. Slides from my talk at ITCS. However, many advances have come from a continuous viewpoint. I am broadly interested in optimization problems, sometimes in the intersection with machine learning 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. 113 * 2016: The system can't perform the operation now. MS&E welcomes new faculty member, Aaron Sidford ! With Cameron Musco and Christopher Musco. 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 the Operations Research group. Management Science & Engineering 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. In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, Sushant Sachdeva, Online Edge Coloring via Tree Recurrences and Correlation Decay, STOC 2022 what is a blind trust for lottery winnings; ithaca college park school scholarships; I enjoy understanding the theoretical ground of many algorithms that are In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. 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. 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 . I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. /N 3 Another research focus are optimization algorithms. . 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. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. missouri noodling association president cnn. I am broadly interested in mathematics and theoretical computer science. [pdf] [talk] [poster] I received a B.S. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. {{{;}#q8?\. ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. publications by categories in reversed chronological order. Before attending Stanford, I graduated from MIT in May 2018. in Mathematics and B.A. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. Mail Code. } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ [pdf] [poster] 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).
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