Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes. - Lunjia Hu; Ruihan Wu; Tianhong Li; Liwei Wang 001 (COLT, 2017)

Capacitated Center Problems with Two-Sided Bounds and Outliers. - Hu Ding; Lunjia Hu; Lingxiao Huang; Jian Li 015 (WADS, 2017)

Active Tolerant Testing. - Avrim Blum; Lunjia Hu (COLT, 2018)

Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation. - Liwei Wang 001; Lunjia Hu; Jiayuan Gu; Zhiqiang Hu; Yue Wu 011; Kun He 001; John E. Hopcroft (NeurIPS, 2018)

Robust and On-the-fly Dataset Denoising for Image Classification. - Jiaming Song; Lunjia Hu; Yann N. Dauphin; Michael Auli; Tengyu Ma 001 (CoRR, 2020)

The Power of Many Samples in Query Complexity. - Andrew Bassilakis; Andrew Drucker; Mika Göös; Lunjia Hu; Weiyun Ma; Li-Yang Tan (Electron. Colloquium Comput. Complex., 2020)

Approximation Algorithms for Orthogonal Non-negative Matrix Factorization. - Moses Charikar; Lunjia Hu (AISTATS, 2021)

Robust Mean Estimation on Highly Incomplete Data with Arbitrary Outliers. - Lunjia Hu; Omer Reingold (AISTATS, 2021)

Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability. - Lunjia Hu; Charlotte Peale; Omer Reingold (ALT, 2022)

Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. - John C. Duchi; Vitaly Feldman; Lunjia Hu; Kunal Talwar (NeurIPS, 2022)

Near-Optimal Explainable k-Means for All Dimensions. - Moses Charikar; Lunjia Hu (SODA, 2022)

An Improved Local Search Algorithm for k-Median. - Vincent Cohen-Addad; Anupam Gupta 001; Lunjia Hu; Hoon Oh; David Saulpic (SODA, 2022)

Generative Models of Huge Objects. - Lunjia Hu; Inbal Livni Navon; Omer Reingold (CCC, 2023)

Omnipredictors for Constrained Optimization. - Lunjia Hu; Inbal Rachel Livni Navon; Omer Reingold; Chutong Yang (ICML, 2023)

Loss Minimization Through the Lens Of Outcome Indistinguishability. - Parikshit Gopalan; Lunjia Hu; Michael P. Kim; Omer Reingold; Udi Wieder (ITCS, 2023)

Comparative Learning: A Sample Complexity Theory for Two Hypothesis Classes. - Lunjia Hu; Charlotte Peale (ITCS, 2023)

When Does Optimizing a Proper Loss Yield Calibration? - Jaroslaw Blasiok; Parikshit Gopalan; Lunjia Hu; Preetum Nakkiran (NeurIPS, 2023)

Simple, Scalable and Effective Clustering via One-Dimensional Projections. - Moses Charikar; Monika Henzinger; Lunjia Hu; Maximilian Vötsch; Erik Waingarten (NeurIPS, 2023)

A Unifying Theory of Distance from Calibration. - Jaroslaw Blasiok; Parikshit Gopalan; Lunjia Hu; Preetum Nakkiran (STOC, 2023)

On Computationally Efficient Multi-Class Calibration. - Parikshit Gopalan; Lunjia Hu; Guy N. Rothblum (COLT, 2024)

Predict to Minimize Swap Regret for All Payoff-Bounded Tasks. - Lunjia Hu; Yifan Wu 005 (FOCS, 2024)

Multigroup Robustness. - Lunjia Hu; Charlotte Peale; Judy Hanwen Shen (ICML, 2024)

Loss Minimization Yields Multicalibration for Large Neural Networks. - Jaroslaw Blasiok; Parikshit Gopalan; Lunjia Hu; Adam Tauman Kalai; Preetum Nakkiran (ITCS, 2024)

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

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

Predict to Minimize Swap Regret for All Payoff-Bounded Tasks. - Lunjia Hu; Yifan Wu (CoRR, 2024)

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

Inducing Efficient and Equitable Professional Networks through Link Recommendations. - Cynthia Dwork; Chris Hays; Lunjia Hu; Nicole Immorlica; Juan C. Perdomo (CoRR, 2025)

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension. - Cynthia Dwork; Lunjia Hu; Han Shao (CoRR, 2025)

Generalized and Unified Equivalences between Hardness and Pseudoentropy. - Lunjia Hu; Salil P. Vadhan (CoRR, 2025)

A Perfectly Truthful Calibration Measure. - Jason Hartline; Lunjia Hu; Yifan Wu (CoRR, 2025)