Yin Tat Lee - RankMe

199 papers A* 82A 1B 1Journal 114Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Early science acceleration experiments with GPT-5. CoRR Sébastien Bubeck, Christian Coester, Ronen Eldan, Timothy Gowers, Yin Tat Lee, Alexandru Lupsasca, Mehtaab Sawhney, Robert Scherrer, Mark Sellke, Brian K. Spears, Derya Unutmaz, Kevin Weil, Steven Yin, Nikita Zhivotovskiy
2025	J	`jnl`	Nested Dissection Meets IPMs: Planar Min-Cost Flow in Nearly-Linear Time. J. ACM Sally Dong, Yu Gao, Gramoz Goranci, Yin Tat Lee, Sushant Sachdeva, Richard Peng, Guanghao Ye
2025	J	`jnl`	Pseudo-Asynchronous Local SGD: Robust and Efficient Data-Parallel Training. CoRR Hiroki Naganuma, Xinzhi Zhang, Man-Chung Yue, Ioannis Mitliagkas, Philipp A. Witte, Russell J. Hewett, Yin Tat Lee
2025	J	`jnl`	Pseudo-Asynchronous Local SGD: Robust and Efficient Data-Parallel Training. Trans. Mach. Learn. Res. Hiroki Naganuma, Xinzhi Zhang, Man-Chung Yue, Ioannis Mitliagkas, Russell J. Hewett, Philipp A. Witte, Yin Tat Lee
2025	J	`jnl`	Upper and Lower Bounds on the Smoothed Complexity of the Simplex Method. TheoretiCS Sophie Huiberts, Yin Tat Lee, Xinzhi Zhang
2024	A*	`conf`	Convex Minimization with Integer Minima in SODA Haotian Jiang, Yin Tat Lee, Zhao Song, Lichen Zhang
2024	J	`jnl`	Differentially Private Fine-tuning of Language Models. J. Priv. Confidentiality Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A. Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang
2024	A*	`conf`	Differentially Private Synthetic Data via Foundation Model APIs 2: Text. ICML Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin A. Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
2024	J	`jnl`	Differentially Private Synthetic Data via Foundation Model APIs 2: Text. CoRR Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin A. Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
2024	A*	`conf`	Improving the Bit Complexity of Communication for Distributed Convex Optimization. STOC Mehrdad Ghadiri, Yin Tat Lee, Swati Padmanabhan, William Swartworth, David P. Woodruff, Guanghao Ye
2024	J	`jnl`	Improving the Bit Complexity of Communication for Distributed Convex Optimization. CoRR Mehrdad Ghadiri, Yin Tat Lee, Swati Padmanabhan, William Swartworth, David P. Woodruff, Guanghao Ye
2024	J	`jnl`	Numerical Composition of Differential Privacy. J. Priv. Confidentiality Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
2024	J	`jnl`	Phi-4 Technical Report. CoRR Marah I Abdin, Jyoti Aneja, Harkirat S. Behl, Sébastien Bubeck, Ronen Eldan, Suriya Gunasekar, Michael Harrison, Russell J. Hewett, Mojan Javaheripi, Piero Kauffmann, James R. Lee, Yin Tat Lee, Yuanzhi Li, Weishung Liu, Caio C. T. Mendes, Anh Nguyen, Eric Price, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Xin Wang, Rachel Ward, Yue Wu, Dingli Yu, Cyril Zhang, Yi Zhang
2024	J	`jnl`	Private Convex Optimization via Exponential Mechanism. J. Priv. Confidentiality Sivakanth Gopi, Yin Tat Lee, Daogao Liu
2024	J	`jnl`	Short-step methods are not strongly polynomial-time. Math. Program. Manru Zong, Yin Tat Lee, Man-Chung Yue
2023	A*	`conf`	Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler. COLT Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian
2023	J	`jnl`	Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler. CoRR Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian
2023	J	`jnl`	An Empirical Study on Challenging Math Problem Solving with GPT-4. CoRR Yiran Wu, Feiran Jia, Shaokun Zhang, Hangyu Li, Erkang Zhu, Yue Wang, Yin Tat Lee, Richard Peng, Qingyun Wu, Chi Wang
2023	A*	`conf`	Automatic Prompt Optimization with "Gradient Descent" and Beam Search. EMNLP Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, Michael Zeng
2023	J	`jnl`	Automatic Prompt Optimization with "Gradient Descent" and Beam Search. CoRR Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, Michael Zeng
2023	J	`jnl`	Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine. CoRR Harsha Nori, Yin Tat Lee, Sheng Zhang, Dean Carignan, Richard Edgar, Nicolò Fusi, Nicholas King, Jonathan Larson, Yuanzhi Li, Weishung Liu, Renqian Luo, Scott Mayer McKinney, Robert Osazuwa Ness, Hoifung Poon, Tao Qin, Naoto Usuyama, Christopher M. White, Eric Horvitz
2023	A*	`conf`	Condition-number-independent Convergence Rate of Riemannian Hamiltonian Monte Carlo with Numerical Integrators. COLT Yunbum Kook, Yin Tat Lee, Ruoqi Shen, Santosh S. Vempala
2023	J	`jnl`	Convex Minimization with Integer Minima in Õ(n CoRR Haotian Jiang, Yin Tat Lee, Zhao Song, Lichen Zhang
2023	A*	`conf`	Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping. ICLR Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian
2023	A*	`conf`	Learning threshold neurons via edge of stability. NeurIPS Kwangjun Ahn, Sébastien Bubeck, Sinho Chewi, Yin Tat Lee, Felipe Suarez, Yi Zhang
2023	J	`jnl`	Positional Description Matters for Transformers Arithmetic. CoRR Ruoqi Shen, Sébastien Bubeck, Ronen Eldan, Yin Tat Lee, Yuanzhi Li, Yi Zhang
2023	A*	`conf`	Private Convex Optimization in General Norms. SODA Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian
2023	A*	`conf`	ReSQueing Parallel and Private Stochastic Convex Optimization. FOCS Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian
2023	J	`jnl`	ReSQueing Parallel and Private Stochastic Convex Optimization. CoRR Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian
2023	J	`jnl`	Sparks of Artificial General Intelligence: Early experiments with GPT-4. CoRR Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott M. Lundberg, Harsha Nori, Hamid Palangi, Marco Túlio Ribeiro, Yi Zhang
2023	J	`jnl`	Textbooks Are All You Need II: phi-1.5 technical report. CoRR Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar, Yin Tat Lee
2023	J	`jnl`	Textbooks Are All You Need. CoRR Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Harkirat Singh Behl, Xin Wang, Sébastien Bubeck, Ronen Eldan, Adam Tauman Kalai, Yin Tat Lee, Yuanzhi Li
2023	A*	`conf`	Upper and Lower Bounds on the Smoothed Complexity of the Simplex Method. STOC Sophie Huiberts, Yin Tat Lee, Xinzhi Zhang
2023	J	`jnl`	kNN-Adapter: Efficient Domain Adaptation for Black-Box Language Models. CoRR Yangsibo Huang, Daogao Liu, Zexuan Zhong, Weijia Shi, Yin Tat Lee
2022	J	`jnl`	A Slightly Improved Bound for the KLS Constant. CoRR Arun Jambulapati, Yin Tat Lee, Santosh S. Vempala
2022	A*	`conf`	A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions. NeurIPS Damek Davis, Dmitriy Drusvyatskiy, Yin Tat Lee, Swati Padmanabhan, Guanghao Ye
2022	A*	`conf`	Computing Lewis Weights to High Precision. SODA Maryam Fazel, Yin Tat Lee, Swati Padmanabhan, Aaron Sidford
2022	J	`jnl`	Condition-number-independent Convergence Rate of Riemannian Hamiltonian Monte Carlo with Numerical Integrators. CoRR Yunbum Kook, Yin Tat Lee, Ruoqi Shen, Santosh S. Vempala
2022	A*	`conf`	Decomposable Non-Smooth Convex Optimization with Nearly-Linear Gradient Oracle Complexity. NeurIPS Sally Dong, Haotian Jiang, Yin Tat Lee, Swati Padmanabhan, Guanghao Ye
2022	J	`jnl`	Decomposable Non-Smooth Convex Optimization with Nearly-Linear Gradient Oracle Complexity. CoRR Sally Dong, Haotian Jiang, Yin Tat Lee, Swati Padmanabhan, Guanghao Ye
2022	A*	`conf`	Differentially Private Fine-tuning of Language Models. ICLR Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A. Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang
2022	J	`jnl`	Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping. CoRR Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian
2022	A*	`conf`	Faster maxflow via improved dynamic spectral vertex sparsifiers. STOC Jan van den Brand, Yu Gao, Arun Jambulapati, Yin Tat Lee, Yang P. Liu, Richard Peng, Aaron Sidford
2022	J	`jnl`	Geodesic Walks in Polytopes. SIAM J. Comput. Yin Tat Lee, Santosh S. Vempala
2022	J	`jnl`	Learning threshold neurons via the "edge of stability". CoRR Kwangjun Ahn, Sébastien Bubeck, Sinho Chewi, Yin Tat Lee, Felipe Suarez, Yi Zhang
2022	A*	`conf`	Nested Dissection Meets IPMs: Planar Min-Cost Flow in Nearly-Linear Time. SODA Sally Dong, Yu Gao, Gramoz Goranci, Yin Tat Lee, Richard Peng, Sushant Sachdeva, Guanghao Ye
2022	J	`jnl`	Nested Dissection Meets IPMs: Planar Min-Cost Flow in Nearly-Linear Time. CoRR Sally Dong, Yu Gao, Gramoz Goranci, Yin Tat Lee, Richard Peng, Sushant Sachdeva, Guanghao Ye
2022	J	`jnl`	Private Convex Optimization in General Norms. CoRR Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian
2022	A*	`conf`	Private Convex Optimization via Exponential Mechanism. COLT Sivakanth Gopi, Yin Tat Lee, Daogao Liu
2022	J	`jnl`	Private Convex Optimization via Exponential Mechanism. CoRR Sivakanth Gopi, Yin Tat Lee, Daogao Liu
2022	A*	`conf`	Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space. NeurIPS Yunbum Kook, Yin Tat Lee, Ruoqi Shen, Santosh S. Vempala
2022	J	`jnl`	Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space. CoRR Yunbum Kook, Yin Tat Lee, Ruoqi Shen, Santosh S. Vempala
2022	A*	`conf`	The Manifold Joys of Sampling (Invited Talk). ICALP Yin Tat Lee, Santosh S. Vempala
2022	J	`jnl`	Upper and Lower Bounds on the Smoothed Complexity of the Simplex Method. CoRR Sophie Huiberts, Yin Tat Lee, Xinzhi Zhang
2022	A*	`conf`	When Does Differentially Private Learning Not Suffer in High Dimensions? NeurIPS Xuechen Li, Daogao Liu, Tatsunori B. Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta
2022	J	`jnl`	When Does Differentially Private Learning Not Suffer in High Dimensions? CoRR Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta
2021	A*	`conf`	A nearly-linear time algorithm for linear programs with small treewidth: a multiscale representation of robust central path. STOC Sally Dong, Yin Tat Lee, Guanghao Ye
2021	J	`jnl`	Computing Lewis Weights to High Precision. CoRR Maryam Fazel, Yin Tat Lee, Swati Padmanabhan, Aaron Sidford
2021	J	`jnl`	Differentially Private Fine-tuning of Language Models. CoRR Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A. Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang
2021	A*	`conf`	Fast and Memory Efficient Differentially Private-SGD via JL Projections. NeurIPS Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
2021	J	`jnl`	Fast and Memory Efficient Differentially Private-SGD via JL Projections. CoRR Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
2021	J	`jnl`	Faster Maxflow via Improved Dynamic Spectral Vertex Sparsifiers. CoRR Jan van den Brand, Yu Gao, Arun Jambulapati, Yin Tat Lee, Yang P. Liu, Richard Peng, Aaron Sidford
2021	J	`jnl`	Kernel-based Methods for Bandit Convex Optimization. J. ACM Sébastien Bubeck, Ronen Eldan, Yin Tat Lee
2021	A*	`conf`	Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions. NeurIPS Yin Tat Lee, Ruoqi Shen, Kevin Tian
2021	J	`jnl`	Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions. CoRR Yin Tat Lee, Ruoqi Shen, Kevin Tian
2021	J	`jnl`	Metrical Task Systems on Trees via Mirror Descent and Unfair Gluing. SIAM J. Comput. Sébastien Bubeck, Michael B. Cohen, James R. Lee, Yin Tat Lee
2021	J	`jnl`	Minimum Cost Flows, MDPs, and 𝓁 CoRR Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang
2021	A*	`conf`	Minimum cost flows, MDPs, and ℓ STOC Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang
2021	A*	`conf`	Numerical Composition of Differential Privacy. NeurIPS Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
2021	J	`jnl`	Numerical Composition of Differential Privacy. CoRR Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
2021	A*	`conf`	Private Non-smooth ERM and SCO in Subquadratic Steps. NeurIPS Janardhan Kulkarni, Yin Tat Lee, Daogao Liu
2021	J	`jnl`	Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps. CoRR Janardhan Kulkarni, Yin Tat Lee, Daogao Liu
2021	A*	`conf`	Reducing isotropy and volume to KLS: an STOC He Jia, Aditi Laddha, Yin Tat Lee, Santosh S. Vempala
2021	J	`jnl`	Solving Linear Programs in the Current Matrix Multiplication Time. J. ACM Michael B. Cohen, Yin Tat Lee, Zhao Song
2021	A*	`conf`	Structured Logconcave Sampling with a Restricted Gaussian Oracle. COLT Yin Tat Lee, Ruoqi Shen, Kevin Tian
2021	J	`jnl`	Tutorial on the Robust Interior Point Method. CoRR Yin Tat Lee, Santosh S. Vempala
2021	J	`jnl`	Universal Barrier Is Math. Oper. Res. Yin Tat Lee, Man-Chung Yue
2020	A*	`conf`	A Faster Interior Point Method for Semidefinite Programming. FOCS Haotian Jiang, Tarun Kathuria, Yin Tat Lee, Swati Padmanabhan, Zhao Song
2020	J	`jnl`	A Faster Interior Point Method for Semidefinite Programming. CoRR Haotian Jiang, Tarun Kathuria, Yin Tat Lee, Swati Padmanabhan, Zhao Song
2020	J	`jnl`	A Nearly-Linear Time Algorithm for Linear Programs with Small Treewidth: A Multiscale Representation of Robust Central Path. CoRR Sally Dong, Yin Tat Lee, Guanghao Ye
2020	A*	`conf`	Acceleration with a Ball Optimization Oracle. NeurIPS Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, Aaron Sidford, Kevin Tian
2020	J	`jnl`	Acceleration with a Ball Optimization Oracle. CoRR Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, Aaron Sidford, Kevin Tian
2020	A*	`conf`	An $\widetilde\mathcalO(m/\varepsilon^3.5)$-Cost Algorithm for Semidefinite Programs with Diagonal Constraints. COLT Yin Tat Lee, Swati Padmanabhan
2020	J	`jnl`	An Improved Cutting Plane Method for Convex Optimization, Convex-Concave Games and its Applications. CoRR Haotian Jiang, Yin Tat Lee, Zhao Song, Sam Chiu-wai Wong
2020	A*	`conf`	An improved cutting plane method for convex optimization, convex-concave games, and its applications. STOC Haotian Jiang, Yin Tat Lee, Zhao Song, Sam Chiu-wai Wong
2020	A*	`conf`	Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs. FOCS Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang
2020	J	`jnl`	Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs. CoRR Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang
2020	A*	`conf`	Chasing Nested Convex Bodies Nearly Optimally. SODA Sébastien Bubeck, Bo'az Klartag, Yin Tat Lee, Yuanzhi Li, Mark Sellke
2020	J	`jnl`	Composite Logconcave Sampling with a Restricted Gaussian Oracle. CoRR Ruoqi Shen, Kevin Tian, Yin Tat Lee
2020	A*	`conf`	Computing Circle Packing Representations of Planar Graphs. SODA Sally Dong, Yin Tat Lee, Kent Quanrud
2020	—	`conf`	Convex Optimization and Dynamic Data Structure (Invited Talk). FSTTCS Yin Tat Lee
2020	A*	`conf`	Differentially Private Release of Synthetic Graphs. SODA Marek Eliás, Michael Kapralov, Janardhan Kulkarni, Yin Tat Lee
2020	J	`jnl`	Introduction to the Special Issue on SODA'18. ACM Trans. Algorithms Yin Tat Lee, Marcin Pilipczuk, David P. Woodruff
2020	B	`conf`	Leverage Score Sampling for Faster Accelerated Regression and ERM. ALT Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford
2020	A*	`conf`	Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo. COLT Yin Tat Lee, Ruoqi Shen, Kevin Tian
2020	J	`jnl`	Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo. CoRR Yin Tat Lee, Ruoqi Shen, Kevin Tian
2020	A*	`conf`	Network size and size of the weights in memorization with two-layers neural networks. NeurIPS Sébastien Bubeck, Ronen Eldan, Yin Tat Lee, Dan Mikulincer
2020	J	`jnl`	Network size and weights size for memorization with two-layers neural networks. CoRR Sébastien Bubeck, Ronen Eldan, Yin Tat Lee, Dan Mikulincer
2020	J	`jnl`	Positive Semidefinite Programming: Mixed, Parallel, and Width-Independent. CoRR Arun Jambulapati, Yin Tat Lee, Jerry Li, Swati Padmanabhan, Kevin Tian
2020	A*	`conf`	Positive semidefinite programming: mixed, parallel, and width-independent. STOC Arun Jambulapati, Yin Tat Lee, Jerry Li, Swati Padmanabhan, Kevin Tian
2020	J	`jnl`	Reducing Isotropy and Volume to KLS: An O(n CoRR He Jia, Aditi Laddha, Yin Tat Lee, Santosh S. Vempala
2020	J	`jnl`	Solving Tall Dense Linear Programs in Nearly Linear Time. CoRR Jan van den Brand, Yin Tat Lee, Aaron Sidford, Zhao Song
2020	A*	`conf`	Solving tall dense linear programs in nearly linear time. STOC Jan van den Brand, Yin Tat Lee, Aaron Sidford, Zhao Song
2020	A*	`conf`	Strong self-concordance and sampling. STOC Aditi Laddha, Yin Tat Lee, Santosh S. Vempala
2020	J	`jnl`	Structured Logconcave Sampling with a Restricted Gaussian Oracle. CoRR Yin Tat Lee, Ruoqi Shen, Kevin Tian
2019	A*	`conf`	A Nearly-Linear Bound for Chasing Nested Convex Bodies. SODA C. J. Argue, Sébastien Bubeck, Michael B. Cohen, Anupam Gupta, Yin Tat Lee
2019	A*	`conf`	A near-optimal algorithm for approximating the John Ellipsoid. COLT Michael B. Cohen, Ben Cousins, Yin Tat Lee, Xin Yang
2019	J	`jnl`	A near-optimal algorithm for approximating the John Ellipsoid. CoRR Michael B. Cohen, Ben Cousins, Yin Tat Lee, Xin Yang
2019	A*	`conf`	Adversarial examples from computational constraints. ICML Sébastien Bubeck, Yin Tat Lee, Eric Price, Ilya P. Razenshteyn
2019	J	`jnl`	An Õ(m/ε CoRR Yin Tat Lee, Swati Padmanabhan
2019	J	`jnl`	Competitively Chasing Convex Bodies. SIAM J. Comput. Sébastien Bubeck, Yin Tat Lee, Yuanzhi Li, Mark Sellke
2019	A*	`conf`	Competitively chasing convex bodies. STOC Sébastien Bubeck, Yin Tat Lee, Yuanzhi Li, Mark Sellke
2019	A*	`conf`	Complexity of Highly Parallel Non-Smooth Convex Optimization. NeurIPS Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford
2019	J	`jnl`	Complexity of Highly Parallel Non-Smooth Convex Optimization. CoRR Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford
2019	J	`jnl`	Computing Circle Packing Representations of Planar Graphs. CoRR Sally Dong, Yin Tat Lee, Kent Quanrud
2019	A*	`conf`	Faster Matroid Intersection. FOCS Deeparnab Chakrabarty, Yin Tat Lee, Aaron Sidford, Sahil Singla, Sam Chiu-wai Wong
2019	J	`jnl`	Faster Matroid Intersection. CoRR Deeparnab Chakrabarty, Yin Tat Lee, Aaron Sidford, Sahil Singla, Sam Chiu-wai Wong
2019	A*	`conf`	Metrical task systems on trees via mirror descent and unfair gluing. SODA Sébastien Bubeck, Michael B. Cohen, James R. Lee, Yin Tat Lee
2019	A*	`conf`	Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives. COLT Alexander V. Gasnikov, Pavel E. Dvurechensky, Eduard Gorbunov, Evgeniya A. Vorontsova, Daniil Selikhanovych, César A. Uribe, Bo Jiang, Haoyue Wang, Shuzhong Zhang, Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford
2019	A*	`conf`	Near-optimal method for highly smooth convex optimization. COLT Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford
2019	J	`jnl`	Optimal Convergence Rates for Convex Distributed Optimization in Networks. J. Mach. Learn. Res. Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié
2019	A*	`conf`	Solving Empirical Risk Minimization in the Current Matrix Multiplication Time. COLT Yin Tat Lee, Zhao Song, Qiuyi Zhang
2019	J	`jnl`	Solving Empirical Risk Minimization in the Current Matrix Multiplication Time. CoRR Yin Tat Lee, Zhao Song, Qiuyi Zhang
2019	J	`jnl`	Solving Linear Programs with Sqrt(rank) Linear System Solves. CoRR Yin Tat Lee, Aaron Sidford
2019	A*	`conf`	Solving linear programs in the current matrix multiplication time. STOC Michael B. Cohen, Yin Tat Lee, Zhao Song
2019	J	`jnl`	Strong Self-Concordance and Sampling. CoRR Aditi Laddha, Yin Tat Lee, Santosh S. Vempala
2019	A*	`conf`	The Randomized Midpoint Method for Log-Concave Sampling. NeurIPS Ruoqi Shen, Yin Tat Lee
2019	J	`jnl`	The Randomized Midpoint Method for Log-Concave Sampling. CoRR Ruoqi Shen, Yin Tat Lee
2018	J	`jnl`	A Nearly-Linear Bound for Chasing Nested Convex Bodies. CoRR C. J. Argue, Sébastien Bubeck, Michael B. Cohen, Anupam Gupta, Yin Tat Lee
2018	A*	`conf`	A matrix expander Chernoff bound. STOC Ankit Garg, Yin Tat Lee, Zhao Song, Nikhil Srivastava
2018	J	`jnl`	Adversarial Examples from Cryptographic Pseudo-Random Generators. CoRR Sébastien Bubeck, Yin Tat Lee, Eric Price, Ilya P. Razenshteyn
2018	J	`jnl`	Algorithmic Theory of ODEs and Sampling from Well-conditioned Logconcave Densities. CoRR Yin Tat Lee, Zhao Song, Santosh S. Vempala
2018	A*	`conf`	An homotopy method for l STOC Sébastien Bubeck, Michael B. Cohen, Yin Tat Lee, Yuanzhi Li
2018	J	`jnl`	Chasing Nested Convex Bodies Nearly Optimally. CoRR Sébastien Bubeck, Yin Tat Lee, Yuanzhi Li, Mark Sellke
2018	J	`jnl`	Competitively Chasing Convex Bodies. CoRR Sébastien Bubeck, Yin Tat Lee, Yuanzhi Li, Mark Sellke
2018	J	`jnl`	Constructing Linear-Sized Spectral Sparsification in Almost-Linear Time. SIAM J. Comput. Yin Tat Lee, He Sun
2018	A*	`conf`	Convergence rate of riemannian Hamiltonian Monte Carlo and faster polytope volume computation. STOC Yin Tat Lee, Santosh S. Vempala
2018	A*	`conf`	Efficient Convex Optimization with Membership Oracles. COLT Yin Tat Lee, Aaron Sidford, Santosh S. Vempala
2018	J	`jnl`	Metrical task systems on trees via mirror descent and unfair gluing. CoRR Sébastien Bubeck, Michael B. Cohen, James R. Lee, Yin Tat Lee
2018	A*	`conf`	Optimal Algorithms for Non-Smooth Distributed Optimization in Networks. NeurIPS Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Laurent Massoulié, Yin Tat Lee
2018	J	`jnl`	Solving Linear Programs in the Current Matrix Multiplication Time. CoRR Michael B. Cohen, Yin Tat Lee, Zhao Song
2018	A*	`conf`	Stochastic localization + Stieltjes barrier = tight bound for log-Sobolev. STOC Yin Tat Lee, Santosh S. Vempala
2018	J	`jnl`	The Kannan-Lovász-Simonovits Conjecture. CoRR Yin Tat Lee, Santosh S. Vempala
2018	A*	`conf`	The Paulsen problem, continuous operator scaling, and smoothed analysis. STOC Tsz Chiu Kwok, Lap Chi Lau, Yin Tat Lee, Akshay Ramachandran
2018	A*	`conf`	k-server via multiscale entropic regularization. STOC Sébastien Bubeck, Michael B. Cohen, Yin Tat Lee, James R. Lee, Aleksander Madry
2017	J	`jnl`	An SDP-Based Algorithm for Linear-Sized Spectral Sparsification. CoRR Yin Tat Lee, He Sun
2017	A*	`conf`	An SDP-based algorithm for linear-sized spectral sparsification. STOC Yin Tat Lee, He Sun
2017	J	`jnl`	An homotopy method for 𝓵 CoRR Sébastien Bubeck, Michael B. Cohen, Yin Tat Lee, Yuanzhi Li
2017	J	`jnl`	Convergence Rate of Riemannian Hamiltonian Monte Carlo and Faster Polytope Volume Computation. CoRR Yin Tat Lee, Santosh Srinivas Vempala
2017	J	`jnl`	Efficient Convex Optimization with Membership Oracles. CoRR Yin Tat Lee, Aaron Sidford, Santosh S. Vempala
2017	A*	`conf`	Eldan's Stochastic Localization and the KLS Hyperplane Conjecture: An Improved Lower Bound for Expansion. FOCS Yin Tat Lee, Santosh Srinivas Vempala
2017	A*	`conf`	Geodesic walks in polytopes. STOC Yin Tat Lee, Santosh S. Vempala
2017	J	`jnl`	Improved Cheeger's Inequality and Analysis of Local Graph Partitioning using Vertex Expansion and Expansion Profile. SIAM J. Comput. Tsz Chiu Kwok, Lap Chi Lau, Yin Tat Lee
2017	A*	`conf`	Kernel-based methods for bandit convex optimization. STOC Sébastien Bubeck, Yin Tat Lee, Ronen Eldan
2017	J	`jnl`	Leverage Score Sampling for Faster Accelerated Regression and ERM. CoRR Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford
2017	A*	`conf`	Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks. ICML Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié
2017	J	`jnl`	Single Pass Spectral Sparsification in Dynamic Streams. SIAM J. Comput. Michael Kapralov, Yin Tat Lee, Cameron Musco, Christopher Musco, Aaron Sidford
2017	A*	`conf`	Subquadratic submodular function minimization. STOC Deeparnab Chakrabarty, Yin Tat Lee, Aaron Sidford, Sam Chiu-wai Wong
2017	J	`jnl`	The Paulsen Problem, Continuous Operator Scaling, and Smoothed Analysis. CoRR Tsz Chiu Kwok, Lap Chi Lau, Yin Tat Lee, Akshay Ramachandran
2017	J	`jnl`	k-server via multiscale entropic regularization. CoRR Sébastien Bubeck, Michael B. Cohen, James R. Lee, Yin Tat Lee, Aleksander Madry
2016	A*	`conf`	Black-box Optimization with a Politician. ICML Sébastien Bubeck, Yin Tat Lee
2016	J	`jnl`	Black-box optimization with a politician. CoRR Sébastien Bubeck, Yin Tat Lee
2016	J	`jnl`	Eldan's Stochastic Localization and the KLS Hyperplane Conjecture: An Improved Lower Bound for Expansion. CoRR Yin Tat Lee, Santosh S. Vempala
2016	J	`jnl`	Geodesic Walks on Polytopes. CoRR Yin Tat Lee, Santosh S. Vempala
2016	J	`jnl`	Geometric Median in Nearly Linear Time. CoRR Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, Aaron Sidford
2016	A*	`conf`	Geometric median in nearly linear time. STOC Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, Aaron Sidford
2016	A*	`conf`	Improved Cheeger's Inequality and Analysis of Local Graph Partitioning using Vertex Expansion and Expansion Profile. SODA Tsz Chiu Kwok, Lap Chi Lau, Yin Tat Lee
2016	J	`jnl`	Kernel-based methods for bandit convex optimization. CoRR Sébastien Bubeck, Ronen Eldan, Yin Tat Lee
2016	J	`jnl`	Landmark-Matching Transformation with Large Deformation Via n-dimensional Quasi-conformal Maps. J. Sci. Comput. Yin Tat Lee, Ka Chun Lam, Lok Ming Lui
2016	A*	`conf`	Sparsified Cholesky and multigrid solvers for connection laplacians. STOC Rasmus Kyng, Yin Tat Lee, Richard Peng, Sushant Sachdeva, Daniel A. Spielman
2016	J	`jnl`	Subquadratic Submodular Function Minimization. CoRR Deeparnab Chakrabarty, Yin Tat Lee, Aaron Sidford, Sam Chiu-wai Wong
2016	A*	`conf`	Using Optimization to Obtain a Width-Independent, Parallel, Simpler, and Faster Positive SDP Solver. SODA Zeyuan Allen Zhu, Yin Tat Lee, Lorenzo Orecchia
2015	A*	`conf`	A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization. FOCS Yin Tat Lee, Aaron Sidford, Sam Chiu-wai Wong
2015	J	`jnl`	A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization. CoRR Yin Tat Lee, Aaron Sidford, Sam Chiu-wai Wong
2015	J	`jnl`	A geometric alternative to Nesterov's accelerated gradient descent. CoRR Sébastien Bubeck, Yin Tat Lee, Mohit Singh
2015	A*	`conf`	Constructing Linear-Sized Spectral Sparsification in Almost-Linear Time. FOCS Yin Tat Lee, He Sun
2015	J	`jnl`	Constructing Linear-Sized Spectral Sparsification in Almost-Linear Time. CoRR Yin Tat Lee, He Sun
2015	A*	`conf`	Efficient Inverse Maintenance and Faster Algorithms for Linear Programming. FOCS Yin Tat Lee, Aaron Sidford
2015	J	`jnl`	Efficient Inverse Maintenance and Faster Algorithms for Linear Programming. CoRR Yin Tat Lee, Aaron Sidford
2015	J	`jnl`	Improved Cheeger's Inequality and Analysis of Local Graph Partitioning using Vertex Expansion and Expansion Profile. CoRR Tsz Chiu Kwok, Lap Chi Lau, Yin Tat Lee
2015	J	`jnl`	Sparsified Cholesky Solvers for SDD linear systems. CoRR Yin Tat Lee, Richard Peng, Daniel A. Spielman
2015	J	`jnl`	Sparsified Cholesky and Multigrid Solvers for Connection Laplacians. CoRR Rasmus Kyng, Yin Tat Lee, Richard Peng, Sushant Sachdeva, Daniel A. Spielman
2015	A	`conf`	Uniform Sampling for Matrix Approximation. ITCS Michael B. Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, Richard Peng, Aaron Sidford
2015	J	`jnl`	Using Optimization to Obtain a Width-Independent, Parallel, Simpler, and Faster Positive SDP Solver. CoRR Zeyuan Allen Zhu, Yin Tat Lee, Lorenzo Orecchia
2014	A*	`conf`	An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations. SODA Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, Aaron Sidford
2014	J	`jnl`	Large Deformation Registration via n-dimensional Quasi-conformal Maps. CoRR Yin Tat Lee, Ka Chun Lam, Lok Ming Lui
2014	A*	`conf`	Path Finding Methods for Linear Programming: Solving Linear Programs in Õ(vrank) Iterations and Faster Algorithms for Maximum Flow. FOCS Yin Tat Lee, Aaron Sidford
2014	J	`jnl`	Probabilistic Spectral Sparsification In Sublinear Time. CoRR Yin Tat Lee
2014	A*	`conf`	Single Pass Spectral Sparsification in Dynamic Streams. FOCS Michael Kapralov, Yin Tat Lee, Cameron Musco, Christopher Musco, Aaron Sidford
2014	J	`jnl`	Single Pass Spectral Sparsification in Dynamic Streams. CoRR Michael Kapralov, Yin Tat Lee, Cameron Musco, Christopher Musco, Aaron Sidford
2014	J	`jnl`	Uniform Sampling for Matrix Approximation. CoRR Michael B. Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, Richard Peng, Aaron Sidford
2013	A*	`conf`	A new approach to computing maximum flows using electrical flows. STOC Yin Tat Lee, Satish Rao, Nikhil Srivastava
2013	J	`jnl`	An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations CoRR Jonathan A. Kelner, Lorenzo Orecchia, Yin Tat Lee, Aaron Sidford
2013	J	`jnl`	Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems CoRR Yin Tat Lee, Aaron Sidford
2013	A*	`conf`	Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems. FOCS Yin Tat Lee, Aaron Sidford
2013	J	`jnl`	Following the Path of Least Resistance : An Õ(m sqrt(n)) Algorithm for the Minimum Cost Flow Problem. CoRR Yin Tat Lee, Aaron Sidford
2013	J	`jnl`	Improved Cheeger's Inequality: Analysis of Spectral Partitioning Algorithms through Higher Order Spectral Gap CoRR Tsz Chiu Kwok, Lap Chi Lau, Yin Tat Lee, Shayan Oveis Gharan, Luca Trevisan
2013	A*	`conf`	Improved Cheeger's inequality: analysis of spectral partitioning algorithms through higher order spectral gap. STOC Tsz Chiu Kwok, Lap Chi Lau, Yin Tat Lee, Shayan Oveis Gharan, Luca Trevisan
2013	J	`jnl`	Matching the Universal Barrier Without Paying the Costs : Solving Linear Programs with Õ(sqrt(rank)) Linear System Solves. CoRR Yin Tat Lee, Aaron Sidford