Towards high-quality I/O virtualization. - Yaozu Dong; Jinquan Dai; Zhiteng Huang; Haibing Guan; Kevin Tian; Yunhong Jiang (SYSTOR, 2009)

On the Power Dominating Sets of Hypercubes. - Nathaniel Dean; Alexandra Ilic; Ignacio Ramírez; Jian Shen; Kevin Tian (CSE, 2011)

Virtualization challenges: a view from server consolidation perspective. - Hui Lv 001; Yaozu Dong; Jiangang Duan; Kevin Tian (VEE, 2012)

On the Complexity of Shortest Path Problems on Discounted Cost Graphs. - Rajeev Alur; Sampath Kannan; Kevin Tian; Yifei Yuan 001 (LATA, 2013)

Public Information Exposure Detection: Helping Users Understand Their Web Footprints. - Lisa Singh; Grace Hui Yang; Micah Sherr; Andrew Hian-Cheong; Kevin Tian; Janet Zhu; Sicong Zhang (ASONAM, 2015)

Helping Users Understand Their Web Footprints. - Lisa Singh; Grace Hui Yang; Micah Sherr; Yifang Wei; Andrew Hian-Cheong; Kevin Tian; Janet Zhu; Sicong Zhang; Tavish Vaidya; Elchin Asgarli (WWW (Companion Volume), 2015)

Detecting Character Dependencies in Stochastic Models of Evolution. - Deeparnab Chakrabarty; Sampath Kannan; Kevin Tian (J. Comput. Biol., 2016)

Learning Populations of Parameters. - Kevin Tian; Weihao Kong; Gregory Valiant (NIPS, 2017)

K-mer Set Memory (KSM) Motif Representation Enables Accurate Prediction of the Impact of Regulatory Variants. - Yuchun Guo; Kevin Tian; Haoyang Zeng; David K. Gifford (RECOMB, 2017)

Optimally Learning Populations of Parameters. - Kevin Tian; Weihao Kong; Gregory Valiant (CoRR, 2017)

Coordinate Methods for Accelerating ℓ∞ Regression and Faster Approximate Maximum Flow. - Aaron Sidford; Kevin Tian (FOCS, 2018)

CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions. - Kevin Tian; Teng Zhang; James Zou 001 (ICML, 2018)

Coordinate Methods for Accelerating 𝓁∞ Regression and Faster Approximate Maximum Flow. - Aaron Sidford; Kevin Tian (CoRR, 2018)

A Rank-1 Sketch for Matrix Multiplicative Weights. - Yair Carmon; John C. Duchi; Aaron Sidford; Kevin Tian (COLT, 2019)

Variance Reduction for Matrix Games. - Yair Carmon; Yujia Jin; Aaron Sidford; Kevin Tian (NeurIPS, 2019)

A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport. - Arun Jambulapati; Aaron Sidford; Kevin Tian (NeurIPS, 2019)

A Direct Õ(1/ε) Iteration Parallel Algorithm for Optimal Transport. - Arun Jambulapati; Aaron Sidford; Kevin Tian (CoRR, 2019)

Locating Errors in Faulty Formulas. - Sampath Kannan; Kevin Tian (ACM Trans. Algorithms, 2019)

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

Information Exposure From Relational Background Knowledge on Social Media. - Shuo Liu 011; Lisa Singh; Kevin Tian (DSAA, 2020)

Coordinate Methods for Matrix Games. - Yair Carmon; Yujia Jin; Aaron Sidford; Kevin Tian (FOCS, 2020)

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

Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing. - Arun Jambulapati; Jerry Li 001; Kevin Tian (NeurIPS, 2020)

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

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

Well-Conditioned Methods for Ill-Conditioned Systems: Linear Regression with Semi-Random Noise. - Jerry Li 001; Aaron Sidford; Kevin Tian; Huishuai Zhang (CoRR, 2020)

Semi-Streaming Bipartite Matching in Fewer Passes and Less Space. - Yujia Jin; Aaron Sidford; Kevin Tian (CoRR, 2020)

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

Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration. - Michael B. Cohen; Aaron Sidford; Kevin Tian (ITCS, 2021)

List-Decodable Mean Estimation in Nearly-PCA Time. - Ilias Diakonikolas; Daniel Kane 001; Daniel Kongsgaard; Jerry Li 001; Kevin Tian (NeurIPS, 2021)

Robust Regression Revisited: Acceleration and Improved Estimation Rates. - Arun Jambulapati; Jerry Li 001; Tselil Schramm; Kevin Tian (NeurIPS, 2021)

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

Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods. - Yujia Jin; Aaron Sidford; Kevin Tian (COLT, 2022)

Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching. - Arun Jambulapati; Yujia Jin; Aaron Sidford; Kevin Tian (ICALP, 2022)

Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space. - Sepehr Assadi; Arun Jambulapati; Yujia Jin; Aaron Sidford; Kevin Tian (SODA, 2022)

Clustering mixture models in almost-linear time via list-decodable mean estimation. - Ilias Diakonikolas; Daniel M. Kane; Daniel Kongsgaard; Jerry Li 001; Kevin Tian (STOC, 2022)

Improved Sampling-to-Counting Reductions in High-Dimensional Expanders and Faster Parallel Determinantal Sampling. - Nima Anari; Callum Burgess; Kevin Tian; Thuy-Duong Vuong (CoRR, 2022)

Iterative methods for structured algorithmic data science. - Kevin Tian (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)

Semi-Random Sparse Recovery in Nearly-Linear Time. - Jonathan A. Kelner; Jerry Li 001; Allen Liu; Aaron Sidford; Kevin Tian (COLT, 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)

Matrix Completion in Almost-Verification Time. - Jonathan A. Kelner; Jerry Li 001; Allen Liu; Aaron Sidford; Kevin Tian (FOCS, 2023)

Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling. - Adam Bouland; Yosheb M. Getachew; Yujia Jin; Aaron Sidford; Kevin Tian (ICML, 2023)

Structured Semidefinite Programming for Recovering Structured Preconditioners. - Arun Jambulapati; Jerry Li 001; Christopher Musco; Kirankumar Shiragur; Aaron Sidford; Kevin Tian (NeurIPS, 2023)

Revisiting Area Convexity: Faster Box-Simplex Games and Spectrahedral Generalizations. - Arun Jambulapati; Kevin Tian (NeurIPS, 2023)

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

Quadratic Speedups in Parallel Sampling from Determinantal Distributions. - Nima Anari; Callum Burgess; Kevin Tian; Thuy-Duong Vuong (SPAA, 2023)

Black-Box k-to-1-PCA Reductions: Theory and Applications. - Arun Jambulapati; Syamantak Kumar; Jerry Li 001; Shourya Pandey; Ankit Pensia; Kevin Tian (COLT, 2024)

Closing the Computational-Query Depth Gap in Parallel Stochastic Convex Optimization. - Arun Jambulapati; Aaron Sidford; Kevin Tian (COLT, 2024)

Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions. - Hilal Asi; Daogao Liu; Kevin Tian (NeurIPS, 2024)

Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random. - Gautam Chandrasekaran; Vasilis Kontonis; Konstantinos Stavropoulos; Kevin Tian (NeurIPS, 2024)

Testing Calibration in Nearly-Linear Time. - Lunjia Hu; Arun Jambulapati; Kevin Tian; Chutong Yang (NeurIPS, 2024)

Semi-Random Matrix Completion via Flow-Based Adaptive Reweighting. - Jonathan A. Kelner; Jerry Li 001; Allen Liu; Aaron Sidford; Kevin Tian (NeurIPS, 2024)

Linear-Sized Sparsifiers via Near-Linear Time Discrepancy Theory. - Arun Jambulapati; Victor Reis; Kevin Tian (SODA, 2024)

A CS guide to the quantum singular value transformation. - Ewin Tang; Kevin Tian (SOSA, 2024)

Testing Calibration in Subquadratic Time. - Lunjia Hu; Kevin Tian; Chutong Yang (CoRR, 2024)

Spike-and-Slab Posterior Sampling in High Dimensions. - Symantak Kumar; Purnamrita Sarkar; Kevin Tian; Yusong Zhu (COLT, 2025)

Eulerian Graph Sparsification by Effective Resistance Decomposition. - Arun Jambulapati; Sushant Sachdeva; Aaron Sidford; Kevin Tian; Yibin Zhao 003 (SODA, 2025)

Omnipredicting Single-Index Models with Multi-index Models. - Lunjia Hu; Kevin Tian; Chutong Yang (STOC, 2025)

More of the Same: Persistent Representational Harms Under Increased Representation. - Jennifer Mickel; Maria De-Arteaga; Leqi Liu; Kevin Tian (CoRR, 2025)

Spike-and-Slab Posterior Sampling in High Dimensions. - Syamantak Kumar; Purnamrita Sarkar; Kevin Tian; Yusong Zhu (CoRR, 2025)

Radial Isotropic Position via an Implicit Newton's Method. - Arun Jambulapati; Jonathan Li 003; Kevin Tian (CoRR, 2025)

The Power of Iterative Filtering for Supervised Learning with (Heavy) Contamination. - Adam R. Klivans; Konstantinos Stavropoulos; Kevin Tian; Arsen Vasilyan (CoRR, 2025)

Private Geometric Median in Nearly-Linear Time. - Syamantak Kumar; Daogao Liu; Kevin Tian; Chutong Yang (CoRR, 2025)