| 2024 | ICML | Differentially Private Synthetic Data via Foundation Model APIs 2: Text. | 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 | SODA | Convex Minimization with Integer Minima in | Haotian Jiang, Yin Tat Lee, Zhao Song, Lichen Zhang |
| 2024 | STOC | Improving the Bit Complexity of Communication for Distributed Convex Optimization. | Mehrdad Ghadiri, Yin Tat Lee, Swati Padmanabhan, William Swartworth, David P. Woodruff, Guanghao Ye |
| 2023 | COLT | Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler. | Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian |
| 2023 | COLT | Condition-number-independent Convergence Rate of Riemannian Hamiltonian Monte Carlo with Numerical Integrators. | Yunbum Kook, Yin Tat Lee, Ruoqi Shen, Santosh S. Vempala |
| 2023 | EMNLP | Automatic Prompt Optimization with "Gradient Descent" and Beam Search. | Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, Michael Zeng |
| 2023 | FOCS | ReSQueing Parallel and Private Stochastic Convex Optimization. | Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian |
| 2023 | ICLR | Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping. | Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian |
| 2023 | SODA | Private Convex Optimization in General Norms. | Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian |
| 2023 | STOC | Upper and Lower Bounds on the Smoothed Complexity of the Simplex Method. | Sophie Huiberts, Yin Tat Lee, Xinzhi Zhang |
| 2022 | COLT | Private Convex Optimization via Exponential Mechanism. | Sivakanth Gopi, Yin Tat Lee, Daogao Liu |
| 2022 | ICALP | The Manifold Joys of Sampling (Invited Talk). | Yin Tat Lee, Santosh S. Vempala |
| 2022 | ICLR | Differentially Private Fine-tuning of Language Models. | 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 | SODA | Nested Dissection Meets IPMs: Planar Min-Cost Flow in Nearly-Linear Time. | Sally Dong, Yu Gao, Gramoz Goranci, Yin Tat Lee, Richard Peng, Sushant Sachdeva, Guanghao Ye |
| 2022 | SODA | Computing Lewis Weights to High Precision. | Maryam Fazel, Yin Tat Lee, Swati Padmanabhan, Aaron Sidford |
| 2022 | STOC | Faster maxflow via improved dynamic spectral vertex sparsifiers. | Jan van den Brand, Yu Gao, Arun Jambulapati, Yin Tat Lee, Yang P. Liu, Richard Peng, Aaron Sidford |
| 2021 | COLT | Structured Logconcave Sampling with a Restricted Gaussian Oracle. | Yin Tat Lee, Ruoqi Shen, Kevin Tian |
| 2021 | STOC | Minimum cost flows, MDPs, and ℓ | Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang |
| 2021 | STOC | A nearly-linear time algorithm for linear programs with small treewidth: a multiscale representation of robust central path. | Sally Dong, Yin Tat Lee, Guanghao Ye |
| 2021 | STOC | Reducing isotropy and volume to KLS: an | He Jia, Aditi Laddha, Yin Tat Lee, Santosh S. Vempala |
| 2020 | ALT | Leverage Score Sampling for Faster Accelerated Regression and ERM. | Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford |
| 2020 | COLT | An $\widetilde\mathcalO(m/\varepsilon^3.5)$-Cost Algorithm for Semidefinite Programs with Diagonal Constraints. | Yin Tat Lee, Swati Padmanabhan |
| 2020 | COLT | Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo. | Yin Tat Lee, Ruoqi Shen, Kevin Tian |
| 2020 | FOCS | Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs. | Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang |
| 2020 | FOCS | A Faster Interior Point Method for Semidefinite Programming. | Haotian Jiang, Tarun Kathuria, Yin Tat Lee, Swati Padmanabhan, Zhao Song |
| 2020 | SODA | Chasing Nested Convex Bodies Nearly Optimally. | Sbastien Bubeck, Bo'az Klartag, Yin Tat Lee, Yuanzhi Li, Mark Sellke |
| 2020 | SODA | Computing Circle Packing Representations of Planar Graphs. | Sally Dong, Yin Tat Lee, Kent Quanrud |
| 2020 | SODA | Differentially Private Release of Synthetic Graphs. | Marek Elis, Michael Kapralov, Janardhan Kulkarni, Yin Tat Lee |
| 2020 | STOC | Solving tall dense linear programs in nearly linear time. | Jan van den Brand, Yin Tat Lee, Aaron Sidford, Zhao Song |
| 2020 | STOC | Positive semidefinite programming: mixed, parallel, and width-independent. | Arun Jambulapati, Yin Tat Lee, Jerry Li, Swati Padmanabhan, Kevin Tian |
| 2020 | STOC | An improved cutting plane method for convex optimization, convex-concave games, and its applications. | Haotian Jiang, Yin Tat Lee, Zhao Song, Sam Chiu-wai Wong |
| 2020 | STOC | Strong self-concordance and sampling. | Aditi Laddha, Yin Tat Lee, Santosh S. Vempala |
| 2019 | COLT | Near-optimal method for highly smooth convex optimization. | Sbastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford |
| 2019 | COLT | A near-optimal algorithm for approximating the John Ellipsoid. | Michael B. Cohen, Ben Cousins, Yin Tat Lee, Xin Yang |
| 2019 | COLT | Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives. | Alexander V. Gasnikov, Pavel E. Dvurechensky, Eduard Gorbunov, Evgeniya A. Vorontsova, Daniil Selikhanovych, Csar A. Uribe, Bo Jiang, Haoyue Wang, Shuzhong Zhang, Sbastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford |
| 2019 | COLT | Solving Empirical Risk Minimization in the Current Matrix Multiplication Time. | Yin Tat Lee, Zhao Song, Qiuyi Zhang |
| 2019 | FOCS | Faster Matroid Intersection. | Deeparnab Chakrabarty, Yin Tat Lee, Aaron Sidford, Sahil Singla, Sam Chiu-wai Wong |
| 2019 | ICML | Adversarial examples from computational constraints. | Sbastien Bubeck, Yin Tat Lee, Eric Price, Ilya P. Razenshteyn |
| 2019 | SODA | A Nearly-Linear Bound for Chasing Nested Convex Bodies. | C. J. Argue, Sbastien Bubeck, Michael B. Cohen, Anupam Gupta, Yin Tat Lee |
| 2019 | SODA | Metrical task systems on trees via mirror descent and unfair gluing. | Sbastien Bubeck, Michael B. Cohen, James R. Lee, Yin Tat Lee |
| 2019 | STOC | Competitively chasing convex bodies. | Sbastien Bubeck, Yin Tat Lee, Yuanzhi Li, Mark Sellke |
| 2019 | STOC | Solving linear programs in the current matrix multiplication time. | Michael B. Cohen, Yin Tat Lee, Zhao Song |
| 2018 | COLT | Efficient Convex Optimization with Membership Oracles. | Yin Tat Lee, Aaron Sidford, Santosh S. Vempala |
| 2018 | STOC | An homotopy method for l | Sbastien Bubeck, Michael B. Cohen, Yin Tat Lee, Yuanzhi Li |
| 2018 | STOC | k-server via multiscale entropic regularization. | Sbastien Bubeck, Michael B. Cohen, Yin Tat Lee, James R. Lee, Aleksander Madry |
| 2018 | STOC | A matrix expander Chernoff bound. | Ankit Garg, Yin Tat Lee, Zhao Song, Nikhil Srivastava |
| 2018 | STOC | The Paulsen problem, continuous operator scaling, and smoothed analysis. | Tsz Chiu Kwok, Lap Chi Lau, Yin Tat Lee, Akshay Ramachandran |
| 2018 | STOC | Convergence rate of riemannian Hamiltonian Monte Carlo and faster polytope volume computation. | Yin Tat Lee, Santosh S. Vempala |
| 2018 | STOC | Stochastic localization + Stieltjes barrier = tight bound for log-Sobolev. | Yin Tat Lee, Santosh S. Vempala |
| 2017 | FOCS | Eldan's Stochastic Localization and the KLS Hyperplane Conjecture: An Improved Lower Bound for Expansion. | Yin Tat Lee, Santosh Srinivas Vempala |
| 2017 | ICML | Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks. | Kevin Scaman, Francis R. Bach, Sbastien Bubeck, Yin Tat Lee, Laurent Massouli |
| 2017 | STOC | Kernel-based methods for bandit convex optimization. | Sbastien Bubeck, Yin Tat Lee, Ronen Eldan |
| 2017 | STOC | Subquadratic submodular function minimization. | Deeparnab Chakrabarty, Yin Tat Lee, Aaron Sidford, Sam Chiu-wai Wong |
| 2017 | STOC | An SDP-based algorithm for linear-sized spectral sparsification. | Yin Tat Lee, He Sun |
| 2017 | STOC | Geodesic walks in polytopes. | Yin Tat Lee, Santosh S. Vempala |
| 2016 | ICML | Black-box Optimization with a Politician. | Sbastien Bubeck, Yin Tat Lee |
| 2016 | SODA | Using Optimization to Obtain a Width-Independent, Parallel, Simpler, and Faster Positive SDP Solver. | Zeyuan Allen Zhu, Yin Tat Lee, Lorenzo Orecchia |
| 2016 | SODA | Improved Cheeger's Inequality and Analysis of Local Graph Partitioning using Vertex Expansion and Expansion Profile. | Tsz Chiu Kwok, Lap Chi Lau, Yin Tat Lee |
| 2016 | STOC | Geometric median in nearly linear time. | Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, Aaron Sidford |
| 2016 | STOC | Sparsified Cholesky and multigrid solvers for connection laplacians. | Rasmus Kyng, Yin Tat Lee, Richard Peng, Sushant Sachdeva, Daniel A. Spielman |
| 2015 | FOCS | Efficient Inverse Maintenance and Faster Algorithms for Linear Programming. | Yin Tat Lee, Aaron Sidford |
| 2015 | FOCS | Constructing Linear-Sized Spectral Sparsification in Almost-Linear Time. | Yin Tat Lee, He Sun |
| 2015 | FOCS | A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization. | Yin Tat Lee, Aaron Sidford, Sam Chiu-wai Wong |
| 2014 | FOCS | Single Pass Spectral Sparsification in Dynamic Streams. | Michael Kapralov, Yin Tat Lee, Cameron Musco, Christopher Musco, Aaron Sidford |
| 2014 | FOCS | Path Finding Methods for Linear Programming: Solving Linear Programs in (vrank) Iterations and Faster Algorithms for Maximum Flow. | Yin Tat Lee, Aaron Sidford |
| 2014 | SODA | An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations. | Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, Aaron Sidford |
| 2013 | FOCS | Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems. | Yin Tat Lee, Aaron Sidford |
| 2013 | STOC | Improved Cheeger's inequality: analysis of spectral partitioning algorithms through higher order spectral gap. | Tsz Chiu Kwok, Lap Chi Lau, Yin Tat Lee, Shayan Oveis Gharan, Luca Trevisan |
| 2013 | STOC | A new approach to computing maximum flows using electrical flows. | Yin Tat Lee, Satish Rao, Nikhil Srivastava |