Lunjia Hu

According to our database, Lunjia Hu authored at least 26 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Omnipredicting Single-Index Models with Multi-Index Models.

Lunjia Hu, Kevin Tian, Chutong Yang

CoRR, 2024

Testing Calibration in Subquadratic Time.

Lunjia Hu, Kevin Tian, Chutong Yang

CoRR, 2024

Testing Calibration in Nearly-Linear Time.

Lunjia Hu, Arun Jambulapati, Kevin Tian, Chutong Yang

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Loss Minimization Yields Multicalibration for Large Neural Networks.

Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Adam Tauman Kalai, Preetum Nakkiran

Proceedings of the 15th Innovations in Theoretical Computer Science Conference, 2024

Multigroup Robustness.

Lunjia Hu, Charlotte Peale, Judy Hanwen Shen

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Predict to Minimize Swap Regret for All Payoff-Bounded Tasks.

Lunjia Hu, Yifan Wu

Proceedings of the 65th IEEE Annual Symposium on Foundations of Computer Science, 2024

On Computationally Efficient Multi-Class Calibration.

Parikshit Gopalan, Lunjia Hu, Guy N. Rothblum

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023

A Unifying Theory of Distance from Calibration.

Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran

Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 2023

Simple, Scalable and Effective Clustering via One-Dimensional Projections.

Moses Charikar, Monika Henzinger, Lunjia Hu, Maximilian Vötsch, Erik Waingarten

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

When Does Optimizing a Proper Loss Yield Calibration?

Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Comparative Learning: A Sample Complexity Theory for Two Hypothesis Classes.

Lunjia Hu, Charlotte Peale

Proceedings of the 14th Innovations in Theoretical Computer Science Conference, 2023

Loss Minimization Through the Lens Of Outcome Indistinguishability.

Parikshit Gopalan, Lunjia Hu, Michael P. Kim, Omer Reingold, Udi Wieder

Proceedings of the 14th Innovations in Theoretical Computer Science Conference, 2023

Omnipredictors for Constrained Optimization.

Lunjia Hu, Inbal Rachel Livni Navon, Omer Reingold, Chutong Yang

Proceedings of the International Conference on Machine Learning, 2023

Generative Models of Huge Objects.

Lunjia Hu, Inbal Livni Navon, Omer Reingold

Proceedings of the 38th Computational Complexity Conference, 2023

2022

An Improved Local Search Algorithm for k-Median.

Vincent Cohen-Addad, Anupam Gupta, Lunjia Hu, Hoon Oh, David Saulpic

Proceedings of the 2022 ACM-SIAM Symposium on Discrete Algorithms, 2022

Near-Optimal Explainable k-Means for All Dimensions.

Moses Charikar, Lunjia Hu

Proceedings of the 2022 ACM-SIAM Symposium on Discrete Algorithms, 2022

Subspace Recovery from Heterogeneous Data with Non-isotropic Noise.

John C. Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability.

Lunjia Hu, Charlotte Peale, Omer Reingold

Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

2021

Robust Mean Estimation on Highly Incomplete Data with Arbitrary Outliers.

Lunjia Hu, Omer Reingold

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Approximation Algorithms for Orthogonal Non-negative Matrix Factorization.

Moses Charikar, Lunjia Hu

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

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

Robust and On-the-fly Dataset Denoising for Image Classification.

Jiaming Song, Lunjia Hu, Yann N. Dauphin, Michael Auli, Tengyu Ma

CoRR, 2020

2018

Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation.

Liwei Wang, Lunjia Hu, Jiayuan Gu, Zhiqiang Hu, Yue Wu, Kun He, John E. Hopcroft

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Active Tolerant Testing.

Avrim Blum, Lunjia Hu

Proceedings of the Conference On Learning Theory, 2018

2017

Capacitated Center Problems with Two-Sided Bounds and Outliers.

Hu Ding, Lunjia Hu, Lingxiao Huang, Jian Li

Proceedings of the Algorithms and Data Structures - 15th International Symposium, 2017

Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes.

Lunjia Hu, Ruihan Wu, Tianhong Li, Liwei Wang

Proceedings of the 30th Conference on Learning Theory, 2017