Yin Tat Lee

Publication Activity (10 Years)

Years Active: 2013-2023
Publications (10 Years): 168

Top Venues

CoRR

STOC

NeurIPS

SODA

Publications

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
Textbooks Are All You Need. CoRR (2023)
Sophie Huiberts, Yin Tat Lee, Xinzhi Zhang
Upper and Lower Bounds on the Smoothed Complexity of the Simplex Method. STOC (2023)
Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar, Yin Tat Lee
Textbooks Are All You Need II: phi-1.5 technical report. CoRR (2023)
Ruoqi Shen, Sébastien Bubeck, Ronen Eldan, Yin Tat Lee, Yuanzhi Li, Yi Zhang
Positional Description Matters for Transformers Arithmetic. CoRR (2023)
Yiran Wu, Feiran Jia, Shaokun Zhang, Hangyu Li, Erkang Zhu, Yue Wang, Yin Tat Lee, Richard Peng, Qingyun Wu, Chi Wang
An Empirical Study on Challenging Math Problem Solving with GPT-4. CoRR (2023)
Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian
ReSQueing Parallel and Private Stochastic Convex Optimization. CoRR (2023)
Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, Michael Zeng
Automatic Prompt Optimization with "Gradient Descent" and Beam Search. CoRR (2023)
Yangsibo Huang, Daogao Liu, Zexuan Zhong, Weijia Shi, Yin Tat Lee
kNN-Adapter: Efficient Domain Adaptation for Black-Box Language Models. CoRR (2023)
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
Sparks of Artificial General Intelligence: Early experiments with GPT-4. CoRR (2023)
Haotian Jiang, Yin Tat Lee, Zhao Song, Lichen Zhang
) Time. CoRR (2023)
Sébastien Bubeck, Yin Tat Lee, Yuanzhi Li, Mark Sellke
Competitively Chasing Convex Bodies. SIAM J. Comput. 52 (1) (2023)
Yunbum Kook, Yin Tat Lee, Ruoqi Shen, Santosh S. Vempala
Condition-number-independent Convergence Rate of Riemannian Hamiltonian Monte Carlo with Numerical Integrators. COLT (2023)
Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian
ReSQueing Parallel and Private Stochastic Convex Optimization. FOCS (2023)
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian
Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler. CoRR (2023)
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, Chris White, Eric Horvitz
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine. CoRR (2023)
Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping. ICLR (2023)
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian
Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler. COLT (2023)
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian
Private Convex Optimization in General Norms. SODA (2023)
Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, Michael Zeng
Automatic Prompt Optimization with "Gradient Descent" and Beam Search. EMNLP (2023)
Kwangjun Ahn, Sébastien Bubeck, Sinho Chewi, Yin Tat Lee, Felipe Suarez, Yi Zhang
Learning threshold neurons via edge of stability. NeurIPS (2023)
Arun Jambulapati, Yin Tat Lee, Santosh S. Vempala
A Slightly Improved Bound for the KLS Constant. CoRR (2022)
Maryam Fazel, Yin Tat Lee, Swati Padmanabhan, Aaron Sidford
Computing Lewis Weights to High Precision. SODA (2022)
Sivakanth Gopi, Yin Tat Lee, Daogao Liu
Private Convex Optimization via Exponential Mechanism. COLT (2022)
Damek Davis, Dmitriy Drusvyatskiy, Yin Tat Lee, Swati Padmanabhan, Guanghao Ye
A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions. NeurIPS (2022)
Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta
When Does Differentially Private Learning Not Suffer in High Dimensions? CoRR (2022)
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
Differentially Private Fine-tuning of Language Models. ICLR (2022)
Sophie Huiberts, Yin Tat Lee, Xinzhi Zhang
Upper and Lower Bounds on the Smoothed Complexity of the Simplex Method. CoRR (2022)
Sally Dong, Yu Gao, Gramoz Goranci, Yin Tat Lee, Richard Peng, Sushant Sachdeva, Guanghao Ye
Nested Dissection Meets IPMs: Planar Min-Cost Flow in Nearly-Linear Time. CoRR (2022)
Jan van den Brand, Yu Gao, Arun Jambulapati, Yin Tat Lee, Yang P. Liu, Richard Peng, Aaron Sidford
Faster maxflow via improved dynamic spectral vertex sparsifiers. STOC (2022)
Yin Tat Lee, Santosh S. Vempala
The Manifold Joys of Sampling (Invited Talk). ICALP (2022)
Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping. CoRR (2022)
Yunbum Kook, Yin Tat Lee, Ruoqi Shen, Santosh S. Vempala
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space. CoRR (2022)
Sally Dong, Haotian Jiang, Yin Tat Lee, Swati Padmanabhan, Guanghao Ye
Decomposable Non-Smooth Convex Optimization with Nearly-Linear Gradient Oracle Complexity. NeurIPS (2022)
Yunbum Kook, Yin Tat Lee, Ruoqi Shen, Santosh S. Vempala
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space. NeurIPS (2022)
Sivakanth Gopi, Yin Tat Lee, Daogao Liu
Private Convex Optimization via Exponential Mechanism. CoRR (2022)
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian
Private Convex Optimization in General Norms. CoRR (2022)
Yunbum Kook, Yin Tat Lee, Ruoqi Shen, Santosh S. Vempala
Condition-number-independent Convergence Rate of Riemannian Hamiltonian Monte Carlo with Numerical Integrators. CoRR (2022)
Sally Dong, Yu Gao, Gramoz Goranci, Yin Tat Lee, Richard Peng, Sushant Sachdeva, Guanghao Ye
Nested Dissection Meets IPMs: Planar Min-Cost Flow in Nearly-Linear Time. SODA (2022)
Yin Tat Lee, Santosh S. Vempala
Geodesic Walks in Polytopes. SIAM J. Comput. 51 (2) (2022)
Kwangjun Ahn, Sébastien Bubeck, Sinho Chewi, Yin Tat Lee, Felipe Suarez, Yi Zhang
Learning threshold neurons via the "edge of stability". CoRR (2022)
Sally Dong, Haotian Jiang, Yin Tat Lee, Swati Padmanabhan, Guanghao Ye
Decomposable Non-Smooth Convex Optimization with Nearly-Linear Gradient Oracle Complexity. CoRR (2022)
He Jia, Aditi Laddha, Yin Tat Lee, Santosh S. Vempala
) volume algorithm. STOC (2021)
Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
Fast and Memory Efficient Differentially Private-SGD via JL Projections. CoRR (2021)
Maryam Fazel, Yin Tat Lee, Swati Padmanabhan, Aaron Sidford
Computing Lewis Weights to High Precision. CoRR (2021)
Yin Tat Lee, Ruoqi Shen, Kevin Tian
Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions. NeurIPS (2021)
Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang
-Regression in Nearly Linear Time for Dense Instances. CoRR (2021)
Michael B. Cohen, Yin Tat Lee, Zhao Song
Solving Linear Programs in the Current Matrix Multiplication Time. J. ACM 68 (1) (2021)
Yin Tat Lee, Man-Chung Yue
-Self-Concordant. Math. Oper. Res. 46 (3) (2021)
Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang
-regression in nearly linear time for dense instances. STOC (2021)
Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
Fast and Memory Efficient Differentially Private-SGD via JL Projections. NeurIPS (2021)
Sébastien Bubeck, Ronen Eldan, Yin Tat Lee
Kernel-based Methods for Bandit Convex Optimization. J. ACM 68 (4) (2021)
Yin Tat Lee, Ruoqi Shen, Kevin Tian
Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions. CoRR (2021)
Janardhan Kulkarni, Yin Tat Lee, Daogao Liu
Private Non-smooth ERM and SCO in Subquadratic Steps. NeurIPS (2021)
Janardhan Kulkarni, Yin Tat Lee, Daogao Liu
Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps. CoRR (2021)
Yin Tat Lee, Ruoqi Shen, Kevin Tian
Structured Logconcave Sampling with a Restricted Gaussian Oracle. COLT (2021)
Yin Tat Lee, Santosh S. Vempala
Tutorial on the Robust Interior Point Method. CoRR (2021)
Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
Numerical Composition of Differential Privacy. CoRR (2021)
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
Differentially Private Fine-tuning of Language Models. CoRR (2021)
Sally Dong, Yin Tat Lee, Guanghao Ye
A nearly-linear time algorithm for linear programs with small treewidth: a multiscale representation of robust central path. STOC (2021)
Jan van den Brand, Yu Gao, Arun Jambulapati, Yin Tat Lee, Yang P. Liu, Richard Peng, Aaron Sidford
Faster Maxflow via Improved Dynamic Spectral Vertex Sparsifiers. CoRR (2021)
Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
Numerical Composition of Differential Privacy. NeurIPS (2021)
Sébastien Bubeck, Michael B. Cohen, James R. Lee, Yin Tat Lee
Metrical Task Systems on Trees via Mirror Descent and Unfair Gluing. SIAM J. Comput. 50 (3) (2021)
Jan van den Brand, Yin Tat Lee, Aaron Sidford, Zhao Song
Solving tall dense linear programs in nearly linear time. STOC (2020)
Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, Aaron Sidford, Kevin Tian
Acceleration with a Ball Optimization Oracle. NeurIPS (2020)
Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang
Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs. CoRR (2020)
Yin Tat Lee, Ruoqi Shen, Kevin Tian
Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo. CoRR (2020)
Sally Dong, Yin Tat Lee, Kent Quanrud
Computing Circle Packing Representations of Planar Graphs. SODA (2020)
Sally Dong, Yin Tat Lee, Guanghao Ye
A Nearly-Linear Time Algorithm for Linear Programs with Small Treewidth: A Multiscale Representation of Robust Central Path. CoRR (2020)
Yin Tat Lee, Ruoqi Shen, Kevin Tian
Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo. COLT (2020)
He Jia, Aditi Laddha, Yin Tat Lee, Santosh S. Vempala
) Volume Algorithm. CoRR (2020)
Yin Tat Lee, Marcin Pilipczuk, David P. Woodruff
Introduction to the Special Issue on SODA'18. ACM Trans. Algorithms 16 (1) (2020)
Sébastien Bubeck, Ronen Eldan, Yin Tat Lee, Dan Mikulincer
Network size and weights size for memorization with two-layers neural networks. CoRR (2020)
Ruoqi Shen, Kevin Tian, Yin Tat Lee
Composite Logconcave Sampling with a Restricted Gaussian Oracle. CoRR (2020)
Yin Tat Lee, Ruoqi Shen, Kevin Tian
Structured Logconcave Sampling with a Restricted Gaussian Oracle. CoRR (2020)
Aditi Laddha, Yin Tat Lee, Santosh S. Vempala
Strong self-concordance and sampling. STOC (2020)
Arun Jambulapati, Yin Tat Lee, Jerry Li, Swati Padmanabhan, Kevin Tian
Positive Semidefinite Programming: Mixed, Parallel, and Width-Independent. CoRR (2020)
Sébastien Bubeck, Ronen Eldan, Yin Tat Lee, Dan Mikulincer
Network size and size of the weights in memorization with two-layers neural networks. NeurIPS (2020)
Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang
Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs. FOCS (2020)
Haotian Jiang, Tarun Kathuria, Yin Tat Lee, Swati Padmanabhan, Zhao Song
A Faster Interior Point Method for Semidefinite Programming. FOCS (2020)
Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford
Leverage Score Sampling for Faster Accelerated Regression and ERM. ALT (2020)
Sébastien Bubeck, Bo'az Klartag, Yin Tat Lee, Yuanzhi Li, Mark Sellke
Chasing Nested Convex Bodies Nearly Optimally. SODA (2020)
Haotian Jiang, Tarun Kathuria, Yin Tat Lee, Swati Padmanabhan, Zhao Song
A Faster Interior Point Method for Semidefinite Programming. CoRR (2020)
Yin Tat Lee, Swati Padmanabhan
An $\widetilde\mathcalO(m/\varepsilon^3.5)$-Cost Algorithm for Semidefinite Programs with Diagonal Constraints. COLT (2020)
Haotian Jiang, Yin Tat Lee, Zhao Song, Sam Chiu-wai Wong
An Improved Cutting Plane Method for Convex Optimization, Convex-Concave Games and its Applications. CoRR (2020)
Marek Eliás, Michael Kapralov, Janardhan Kulkarni, Yin Tat Lee
Differentially Private Release of Synthetic Graphs. SODA (2020)
Haotian Jiang, Yin Tat Lee, Zhao Song, Sam Chiu-wai Wong
An improved cutting plane method for convex optimization, convex-concave games, and its applications. STOC (2020)
Jan van den Brand, Yin Tat Lee, Aaron Sidford, Zhao Song
Solving Tall Dense Linear Programs in Nearly Linear Time. CoRR (2020)
Arun Jambulapati, Yin Tat Lee, Jerry Li, Swati Padmanabhan, Kevin Tian
Positive semidefinite programming: mixed, parallel, and width-independent. STOC (2020)
Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, Aaron Sidford, Kevin Tian
Acceleration with a Ball Optimization Oracle. CoRR (2020)
Yin Tat Lee
Convex Optimization and Dynamic Data Structure (Invited Talk). FSTTCS (2020)
Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié
Optimal Convergence Rates for Convex Distributed Optimization in Networks. J. Mach. Learn. Res. 20 (2019)
Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford
Complexity of Highly Parallel Non-Smooth Convex Optimization. CoRR (2019)
Yin Tat Lee, Zhao Song, Qiuyi Zhang
Solving Empirical Risk Minimization in the Current Matrix Multiplication Time. COLT (2019)
Sébastien Bubeck, Yin Tat Lee, Eric Price, Ilya P. Razenshteyn
Adversarial examples from computational constraints. ICML (2019)
Deeparnab Chakrabarty, Yin Tat Lee, Aaron Sidford, Sahil Singla, Sam Chiu-wai Wong
Faster Matroid Intersection. CoRR (2019)
Michael B. Cohen, Ben Cousins, Yin Tat Lee, Xin Yang
A near-optimal algorithm for approximating the John Ellipsoid. COLT (2019)
Sébastien Bubeck, Yin Tat Lee, Yuanzhi Li, Mark Sellke
Competitively chasing convex bodies. STOC (2019)
Sébastien Bubeck, Michael B. Cohen, James R. Lee, Yin Tat Lee
Metrical task systems on trees via mirror descent and unfair gluing. SODA (2019)
Sally Dong, Yin Tat Lee, Kent Quanrud
Computing Circle Packing Representations of Planar Graphs. CoRR (2019)
Alexander V. Gasnikov, Pavel E. Dvurechensky, Eduard A. 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
Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives. COLT (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Yin Tat Lee