Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems. - Yin Tat Lee; Aaron Sidford (FOCS, 2013)

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 001 (STOC, 2013)

A new approach to computing maximum flows using electrical flows. - Yin Tat Lee; Satish Rao; Nikhil Srivastava (STOC, 2013)

Matching the Universal Barrier Without Paying the Costs : Solving Linear Programs with Õ(sqrt(rank)) Linear System Solves. - Yin Tat Lee; Aaron Sidford (CoRR, 2013)

Following the Path of Least Resistance : An Õ(m sqrt(n)) Algorithm for the Minimum Cost Flow Problem. - Yin Tat Lee; Aaron Sidford (CoRR, 2013)

Path Finding Methods for Linear Programming: Solving Linear Programs in Õ(vrank) Iterations and Faster Algorithms for Maximum Flow. - Yin Tat Lee; Aaron Sidford (FOCS, 2014)

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 (SODA, 2014)

Probabilistic Spectral Sparsification In Sublinear Time. - Yin Tat Lee (CoRR, 2014)

Large Deformation Registration via n-dimensional Quasi-conformal Maps. - Yin Tat Lee; Ka Chun Lam; Lok Ming Lui (CoRR, 2014)

Efficient Inverse Maintenance and Faster Algorithms for Linear Programming. - Yin Tat Lee; Aaron Sidford (FOCS, 2015)

A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization. - Yin Tat Lee; Aaron Sidford; Sam Chiu-wai Wong (FOCS, 2015)

Uniform Sampling for Matrix Approximation. - Michael B. Cohen; Yin Tat Lee; Cameron Musco; Christopher Musco; Richard Peng; Aaron Sidford (ITCS, 2015)

A geometric alternative to Nesterov's accelerated gradient descent. - Sébastien Bubeck; Yin Tat Lee; Mohit Singh (CoRR, 2015)

Sparsified Cholesky Solvers for SDD linear systems. - Yin Tat Lee; Richard Peng; Daniel A. Spielman (CoRR, 2015)

Black-box Optimization with a Politician. - Sébastien Bubeck; Yin Tat Lee (ICML, 2016)

Using Optimization to Obtain a Width-Independent, Parallel, Simpler, and Faster Positive SDP Solver. - Zeyuan Allen Zhu; Yin Tat Lee; Lorenzo Orecchia (SODA, 2016)

Geometric median in nearly linear time. - Michael B. Cohen; Yin Tat Lee; Gary L. Miller; Jakub Pachocki; Aaron Sidford (STOC, 2016)

Sparsified Cholesky and multigrid solvers for connection laplacians. - Rasmus Kyng; Yin Tat Lee; Richard Peng; Sushant Sachdeva; Daniel A. Spielman (STOC, 2016)

Landmark-Matching Transformation with Large Deformation Via n-dimensional Quasi-conformal Maps. - Yin Tat Lee; Ka Chun Lam; Lok Ming Lui (J. Sci. Comput., 2016)

Eldan's Stochastic Localization and the KLS Hyperplane Conjecture: An Improved Lower Bound for Expansion. - Yin Tat Lee; Santosh Srinivas Vempala (FOCS, 2017)

Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks. - Kevin Scaman; Francis R. Bach; Sébastien Bubeck; Yin Tat Lee; Laurent Massoulié (ICML, 2017)

Subquadratic submodular function minimization. - Deeparnab Chakrabarty; Yin Tat Lee; Aaron Sidford; Sam Chiu-wai Wong (STOC, 2017)

An SDP-based algorithm for linear-sized spectral sparsification. - Yin Tat Lee; He Sun 001 (STOC, 2017)

Single Pass Spectral Sparsification in Dynamic Streams. - Michael Kapralov; Yin Tat Lee; Cameron Musco; Christopher Musco; Aaron Sidford (SIAM J. Comput., 2017)

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 (SIAM J. Comput., 2017)

Efficient Convex Optimization with Membership Oracles. - Yin Tat Lee; Aaron Sidford; Santosh S. Vempala (COLT, 2018)

Optimal Algorithms for Non-Smooth Distributed Optimization in Networks. - Kevin Scaman; Francis R. Bach; Sébastien Bubeck; Laurent Massoulié; Yin Tat Lee (NeurIPS, 2018)

An homotopy method for lp regression provably beyond self-concordance and in input-sparsity time. - Sébastien Bubeck; Michael B. Cohen; Yin Tat Lee; Yuanzhi Li (STOC, 2018)

k-server via multiscale entropic regularization. - Sébastien Bubeck; Michael B. Cohen; Yin Tat Lee; James R. Lee; Aleksander Madry (STOC, 2018)

A matrix expander Chernoff bound. - Ankit Garg; Yin Tat Lee; Zhao Song 002; Nikhil Srivastava (STOC, 2018)

The Paulsen problem, continuous operator scaling, and smoothed analysis. - Tsz Chiu Kwok; Lap Chi Lau; Yin Tat Lee; Akshay Ramachandran (STOC, 2018)

Convergence rate of riemannian Hamiltonian Monte Carlo and faster polytope volume computation. - Yin Tat Lee; Santosh S. Vempala (STOC, 2018)

Stochastic localization + Stieltjes barrier = tight bound for log-Sobolev. - Yin Tat Lee; Santosh S. Vempala (STOC, 2018)

The Kannan-Lovász-Simonovits Conjecture. - Yin Tat Lee; Santosh S. Vempala (CoRR, 2018)

Chasing Nested Convex Bodies Nearly Optimally. - Sébastien Bubeck; Yin Tat Lee; Yuanzhi Li; Mark Sellke (CoRR, 2018)

Adversarial Examples from Cryptographic Pseudo-Random Generators. - Sébastien Bubeck; Yin Tat Lee; Eric Price 001; Ilya P. Razenshteyn (CoRR, 2018)

Algorithmic Theory of ODEs and Sampling from Well-conditioned Logconcave Densities. - Yin Tat Lee; Zhao Song 002; Santosh S. Vempala (CoRR, 2018)

Constructing Linear-Sized Spectral Sparsification in Almost-Linear Time. - Yin Tat Lee; He Sun 001 (SIAM J. Comput., 2018)

Near-optimal method for highly smooth convex optimization. - Sébastien Bubeck; Qijia Jiang; Yin Tat Lee; Yuanzhi Li; Aaron Sidford (COLT, 2019)

A near-optimal algorithm for approximating the John Ellipsoid. - Michael B. Cohen; Ben Cousins; Yin Tat Lee; Xin Yang 017 (COLT, 2019)

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; César A. Uribe; Bo Jiang 007; Haoyue Wang; Shuzhong Zhang; Sébastien Bubeck; Qijia Jiang; Yin Tat Lee; et al. (COLT, 2019)

Solving Empirical Risk Minimization in the Current Matrix Multiplication Time. - Yin Tat Lee; Zhao Song 002; Qiuyi Zhang 001 (COLT, 2019)

Faster Matroid Intersection. - Deeparnab Chakrabarty; Yin Tat Lee; Aaron Sidford; Sahil Singla 001; Sam Chiu-wai Wong (FOCS, 2019)

Adversarial examples from computational constraints. - Sébastien Bubeck; Yin Tat Lee; Eric Price 001; Ilya P. Razenshteyn (ICML, 2019)

Complexity of Highly Parallel Non-Smooth Convex Optimization. - Sébastien Bubeck; Qijia Jiang; Yin Tat Lee; Yuanzhi Li; Aaron Sidford (NeurIPS, 2019)

The Randomized Midpoint Method for Log-Concave Sampling. - Ruoqi Shen; Yin Tat Lee (NeurIPS, 2019)

A Nearly-Linear Bound for Chasing Nested Convex Bodies. - C. J. Argue; Sébastien Bubeck; Michael B. Cohen; Anupam Gupta 001; Yin Tat Lee (SODA, 2019)

An Õ(m/ε3.5)-Cost Algorithm for Semidefinite Programs with Diagonal Constraints. - Yin Tat Lee; Swati Padmanabhan (CoRR, 2019)

Solving Linear Programs with Sqrt(rank) Linear System Solves. - Yin Tat Lee; Aaron Sidford (CoRR, 2019)

Optimal Convergence Rates for Convex Distributed Optimization in Networks. - Kevin Scaman; Francis R. Bach; Sébastien Bubeck; Yin Tat Lee; Laurent Massoulié (J. Mach. Learn. Res., 2019)

Competitively Chasing Convex Bodies. - Sébastien Bubeck; Yin Tat Lee; Yuanzhi Li; Mark Sellke (SIAM J. Comput., 2019)

Leverage Score Sampling for Faster Accelerated Regression and ERM. - Naman Agarwal; Sham M. Kakade; Rahul Kidambi; Yin Tat Lee; Praneeth Netrapalli; Aaron Sidford (ALT, 2020)

An $ widetilde mathcalO(m/ varepsilon^3.5)$-Cost Algorithm for Semidefinite Programs with Diagonal Constraints. - Yin Tat Lee; Swati Padmanabhan (COLT, 2020)

Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo. - Yin Tat Lee; Ruoqi Shen; Kevin Tian (COLT, 2020)

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 002; Di Wang 005 (FOCS, 2020)

A Faster Interior Point Method for Semidefinite Programming. - Haotian Jiang; Tarun Kathuria; Yin Tat Lee; Swati Padmanabhan; Zhao Song 002 (FOCS, 2020)

Convex Optimization and Dynamic Data Structure (Invited Talk). - Yin Tat Lee (FSTTCS, 2020)

Network size and size of the weights in memorization with two-layers neural networks. - Sébastien Bubeck; Ronen Eldan; Yin Tat Lee; Dan Mikulincer (NeurIPS, 2020)

Acceleration with a Ball Optimization Oracle. - Yair Carmon; Arun Jambulapati; Qijia Jiang; Yujia Jin; Yin Tat Lee; Aaron Sidford; Kevin Tian (NeurIPS, 2020)

Chasing Nested Convex Bodies Nearly Optimally. - Sébastien Bubeck; Bo'az Klartag; Yin Tat Lee; Yuanzhi Li; Mark Sellke (SODA, 2020)

Computing Circle Packing Representations of Planar Graphs. - Sally Dong; Yin Tat Lee; Kent Quanrud (SODA, 2020)

Differentially Private Release of Synthetic Graphs. - Marek Eliás 001; Michael Kapralov; Janardhan Kulkarni; Yin Tat Lee (SODA, 2020)

Solving tall dense linear programs in nearly linear time. - Jan van den Brand; Yin Tat Lee; Aaron Sidford; Zhao Song 002 (STOC, 2020)

Positive semidefinite programming: mixed, parallel, and width-independent. - Arun Jambulapati; Yin Tat Lee; Jerry Li 001; Swati Padmanabhan; Kevin Tian (STOC, 2020)

An improved cutting plane method for convex optimization, convex-concave games, and its applications. - Haotian Jiang; Yin Tat Lee; Zhao Song 002; Sam Chiu-wai Wong (STOC, 2020)

Strong self-concordance and sampling. - Aditi Laddha; Yin Tat Lee; Santosh S. Vempala (STOC, 2020)

Network size and weights size for memorization with two-layers neural networks. - Sébastien Bubeck; Ronen Eldan; Yin Tat Lee; Dan Mikulincer (CoRR, 2020)

Composite Logconcave Sampling with a Restricted Gaussian Oracle. - Ruoqi Shen; Kevin Tian; Yin Tat Lee (CoRR, 2020)

Reducing Isotropy and Volume to KLS: An O(n3ψ2) Volume Algorithm. - He Jia; Aditi Laddha; Yin Tat Lee; Santosh S. Vempala (CoRR, 2020)

Introduction to the Special Issue on SODA'18. - Yin Tat Lee; Marcin Pilipczuk; David P. Woodruff (ACM Trans. Algorithms, 2020)

Structured Logconcave Sampling with a Restricted Gaussian Oracle. - Yin Tat Lee; Ruoqi Shen; Kevin Tian (COLT, 2021)

Fast and Memory Efficient Differentially Private-SGD via JL Projections. - Zhiqi Bu; Sivakanth Gopi; Janardhan Kulkarni; Yin Tat Lee; Judy Hanwen Shen; Uthaipon Tantipongpipat (NeurIPS, 2021)

Private Non-smooth ERM and SCO in Subquadratic Steps. - Janardhan Kulkarni; Yin Tat Lee; Daogao Liu (NeurIPS, 2021)

Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions. - Yin Tat Lee; Ruoqi Shen; Kevin Tian (NeurIPS, 2021)

Minimum cost flows, MDPs, and ℓ1-regression in nearly linear time for dense instances. - Jan van den Brand; Yin Tat Lee; Yang P. Liu; Thatchaphol Saranurak; Aaron Sidford; Zhao Song 002; Di Wang 005 (STOC, 2021)

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 (STOC, 2021)

Reducing isotropy and volume to KLS: an o*(n3ψ2) volume algorithm. - He Jia; Aditi Laddha; Yin Tat Lee; Santosh S. Vempala (STOC, 2021)

Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps. - Janardhan Kulkarni; Yin Tat Lee; Daogao Liu (CoRR, 2021)

Tutorial on the Robust Interior Point Method. - Yin Tat Lee; Santosh S. Vempala (CoRR, 2021)

Kernel-based Methods for Bandit Convex Optimization. - Sébastien Bubeck; Ronen Eldan; Yin Tat Lee (J. ACM, 2021)

Solving Linear Programs in the Current Matrix Multiplication Time. - Michael B. Cohen; Yin Tat Lee; Zhao Song 002 (J. ACM, 2021)

Universal Barrier Is n-Self-Concordant. - Yin Tat Lee; Man-Chung Yue (Math. Oper. Res., 2021)

Metrical Task Systems on Trees via Mirror Descent and Unfair Gluing. - Sébastien Bubeck; Michael B. Cohen; James R. Lee; Yin Tat Lee (SIAM J. Comput., 2021)

The Manifold Joys of Sampling (Invited Talk). - Yin Tat Lee; Santosh S. Vempala (ICALP, 2022)

A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions. - Damek Davis; Dmitriy Drusvyatskiy; Yin Tat Lee; Swati Padmanabhan; Guanghao Ye (NeurIPS, 2022)

Decomposable Non-Smooth Convex Optimization with Nearly-Linear Gradient Oracle Complexity. - Sally Dong; Haotian Jiang; Yin Tat Lee; Swati Padmanabhan; Guanghao Ye (NeurIPS, 2022)

Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space. - Yunbum Kook; Yin Tat Lee; Ruoqi Shen; Santosh S. Vempala (NeurIPS, 2022)

When Does Differentially Private Learning Not Suffer in High Dimensions? - Xuechen Li 005; Daogao Liu; Tatsunori B. Hashimoto; Huseyin A. Inan; Janardhan Kulkarni; Yin Tat Lee; Abhradeep Guha Thakurta (NeurIPS, 2022)

Computing Lewis Weights to High Precision. - Maryam Fazel; Yin Tat Lee; Swati Padmanabhan; Aaron Sidford (SODA, 2022)

Faster maxflow via improved dynamic spectral vertex sparsifiers. - Jan van den Brand; Yu Gao 001; Arun Jambulapati; Yin Tat Lee; Yang P. Liu; Richard Peng; Aaron Sidford (STOC, 2022)

A Slightly Improved Bound for the KLS Constant. - Arun Jambulapati; Yin Tat Lee; Santosh S. Vempala (CoRR, 2022)

Geodesic Walks in Polytopes. - Yin Tat Lee; Santosh S. Vempala (SIAM J. Comput., 2022)

Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler. - Sivakanth Gopi; Yin Tat Lee; Daogao Liu; Ruoqi Shen; Kevin Tian (COLT, 2023)

Condition-number-independent Convergence Rate of Riemannian Hamiltonian Monte Carlo with Numerical Integrators. - Yunbum Kook; Yin Tat Lee; Ruoqi Shen; Santosh S. Vempala (COLT, 2023)

Automatic Prompt Optimization with "Gradient Descent" and Beam Search. - Reid Pryzant; Dan Iter; Jerry Li 001; Yin Tat Lee; Chenguang Zhu 001; Michael Zeng 001 (EMNLP, 2023)

ReSQueing Parallel and Private Stochastic Convex Optimization. - Yair Carmon; Arun Jambulapati; Yujia Jin; Yin Tat Lee; Daogao Liu; Aaron Sidford; Kevin Tian (FOCS, 2023)

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 002 (ICLR, 2023)

Learning threshold neurons via edge of stability. - Kwangjun Ahn; Sébastien Bubeck; Sinho Chewi; Yin Tat Lee; Felipe Suarez; Yi Zhang (NeurIPS, 2023)

Private Convex Optimization in General Norms. - Sivakanth Gopi; Yin Tat Lee; Daogao Liu; Ruoqi Shen; Kevin Tian (SODA, 2023)

Upper and Lower Bounds on the Smoothed Complexity of the Simplex Method. - Sophie Huiberts; Yin Tat Lee; Xinzhi Zhang 002 (STOC, 2023)

kNN-Adapter: Efficient Domain Adaptation for Black-Box Language Models. - Yangsibo Huang; Daogao Liu; Zexuan Zhong; Weijia Shi; Yin Tat Lee (CoRR, 2023)

Sparks of Artificial General Intelligence: Early experiments with GPT-4. - 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; et al. (CoRR, 2023)

Convex Minimization with Integer Minima in Õ(n4) Time. - Haotian Jiang; Yin Tat Lee; Zhao Song 002; Lichen Zhang 003 (CoRR, 2023)

An Empirical Study on Challenging Math Problem Solving with GPT-4. - Yiran Wu; Feiran Jia; Shaokun Zhang; Hangyu Li; Erkang Zhu; Yue Wang; Yin Tat Lee; Richard Peng; Qingyun Wu; Chi Wang 001 (CoRR, 2023)

Textbooks Are All You Need. - Suriya Gunasekar; Yi Zhang 001; Jyoti Aneja; Caio César Teodoro Mendes; Allie Del Giorno; Sivakanth Gopi; Mojan Javaheripi; Piero Kauffmann; Gustavo de Rosa; Olli Saarikivi; Adil Salim; Shital Shah; et al. (CoRR, 2023)

Textbooks Are All You Need II: phi-1.5 technical report. - Yuanzhi Li; Sébastien Bubeck; Ronen Eldan; Allie Del Giorno; Suriya Gunasekar; Yin Tat Lee (CoRR, 2023)

Positional Description Matters for Transformers Arithmetic. - Ruoqi Shen; Sébastien Bubeck; Ronen Eldan; Yin Tat Lee; Yuanzhi Li; Yi Zhang (CoRR, 2023)

Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine. - Harsha Nori; Yin Tat Lee; Sheng Zhang 012; Dean Carignan; Richard Edgar; Nicolò Fusi; Nicholas King; Jonathan Larson; Yuanzhi Li; Weishung Liu; Renqian Luo; Scott Mayer McKinney; et al. (CoRR, 2023)

Differentially Private Synthetic Data via Foundation Model APIs 2: Text. - Chulin Xie; Zinan Lin 001; Arturs Backurs; Sivakanth Gopi; Da Yu; Huseyin A. Inan; Harsha Nori; Haotian Jiang; Huishuai Zhang; Yin Tat Lee; Bo Li 026; Sergey Yekhanin (ICML, 2024)

Convex Minimization with Integer Minima in Õ(n4) Time. - Haotian Jiang; Yin Tat Lee; Zhao Song 002; Lichen Zhang 003 (SODA, 2024)

Improving the Bit Complexity of Communication for Distributed Convex Optimization. - Mehrdad Ghadiri; Yin Tat Lee; Swati Padmanabhan; William Swartworth; David P. Woodruff; Guanghao Ye (STOC, 2024)

Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone. - Marah I Abdin; Sam Ade Jacobs; Ammar Ahmad Awan; Jyoti Aneja; Ahmed Awadallah 001; Hany Awadalla; Nguyen Bach; Amit Bahree; Arash Bakhtiari; Harkirat S. Behl; Alon Benhaim; Misha Bilenko; et al. (CoRR, 2024)

Phi-4 Technical Report. - 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; et al. (CoRR, 2024)

Private Convex Optimization via Exponential Mechanism. - Sivakanth Gopi; Yin Tat Lee; Daogao Liu (J. Priv. Confidentiality, 2024)

Numerical Composition of Differential Privacy. - Sivakanth Gopi; Yin Tat Lee; Lukas Wutschitz (J. Priv. Confidentiality, 2024)

Differentially Private Fine-tuning of Language Models. - Da Yu; Saurabh Naik; Arturs Backurs; Sivakanth Gopi; Huseyin A. Inan; Gautam Kamath 001; Janardhan Kulkarni; Yin Tat Lee; Andre Manoel; Lukas Wutschitz; Sergey Yekhanin; Huishuai Zhang (J. Priv. Confidentiality, 2024)

Short-step methods are not strongly polynomial-time. - Manru Zong; Yin Tat Lee; Man-Chung Yue (Math. Program., 2024)

Pseudo-Asynchronous Local SGD: Robust and Efficient Data-Parallel Training. - Hiroki Naganuma; Xinzhi Zhang 002; Man-Chung Yue; Ioannis Mitliagkas; Philipp A. Witte; Russell J. Hewett; Yin Tat Lee (CoRR, 2025)

Nested Dissection Meets IPMs: Planar Min-Cost Flow in Nearly-Linear Time. - Sally Dong; Yu Gao 001; Gramoz Goranci; Yin Tat Lee; Sushant Sachdeva; Richard Peng; Guanghao Ye (J. ACM, 2025)