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Conference and Workshop Papers
- 2024
[c21]Parikshit Gopalan, Lunjia Hu, Guy N. Rothblum:
On Computationally Efficient Multi-Class Calibration. COLT 2024: 1983-2026- [c20]Lunjia Hu, Charlotte Peale, Judy Hanwen Shen:
Multigroup Robustness. ICML 2024 - [c19]Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Adam Tauman Kalai, Preetum Nakkiran:
Loss Minimization Yields Multicalibration for Large Neural Networks. ITCS 2024: 17:1-17:21 - 2023
- [c18]Lunjia Hu
, Inbal Livni Navon, Omer Reingold:
Generative Models of Huge Objects. CCC 2023: 5:1-5:20 - [c17]Lunjia Hu, Inbal Rachel Livni Navon, Omer Reingold, Chutong Yang:
Omnipredictors for Constrained Optimization. ICML 2023: 13497-13527 - [c16]Parikshit Gopalan, Lunjia Hu, Michael P. Kim, Omer Reingold, Udi Wieder:
Loss Minimization Through the Lens Of Outcome Indistinguishability. ITCS 2023: 60:1-60:20 - [c15]Lunjia Hu, Charlotte Peale:
Comparative Learning: A Sample Complexity Theory for Two Hypothesis Classes. ITCS 2023: 72:1-72:30 - [c14]Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran:
When Does Optimizing a Proper Loss Yield Calibration? NeurIPS 2023 - [c13]Moses Charikar, Monika Henzinger, Lunjia Hu, Maximilian Vötsch, Erik Waingarten:
Simple, Scalable and Effective Clustering via One-Dimensional Projections. NeurIPS 2023 - [c12]Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran:
A Unifying Theory of Distance from Calibration. STOC 2023: 1727-1740 - 2022
- [c11]Lunjia Hu, Charlotte Peale, Omer Reingold:
Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability. ALT 2022: 515-552 - [c10]John C. Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar:
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. NeurIPS 2022 - [c9]Vincent Cohen-Addad, Anupam Gupta, Lunjia Hu, Hoon Oh, David Saulpic:
An Improved Local Search Algorithm for k-Median. SODA 2022: 1556-1612 - [c8]Moses Charikar, Lunjia Hu:
Near-Optimal Explainable k-Means for All Dimensions. SODA 2022: 2580-2606 - 2021
- [c7]Lunjia Hu, Omer Reingold:
Robust Mean Estimation on Highly Incomplete Data with Arbitrary Outliers. AISTATS 2021: 1558-1566 - [c6]Moses Charikar, Lunjia Hu:
Approximation Algorithms for Orthogonal Non-negative Matrix Factorization. AISTATS 2021: 2728-2736 - 2020
- [c5]Andrew Bassilakis, Andrew Drucker, Mika Göös, Lunjia Hu, Weiyun Ma, Li-Yang Tan:
The Power of Many Samples in Query Complexity. ICALP 2020: 9:1-9:18 - 2018
- [c4]Avrim Blum, Lunjia Hu:
Active Tolerant Testing. COLT 2018: 474-497 - [c3]Liwei Wang, Lunjia Hu, Jiayuan Gu, Zhiqiang Hu, Yue Wu, Kun He, John E. Hopcroft:
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation. NeurIPS 2018: 9607-9616 - 2017
- [c2]Lunjia Hu, Ruihan Wu, Tianhong Li, Liwei Wang:
Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes. COLT 2017: 1147-1156 - [c1]Hu Ding, Lunjia Hu, Lingxiao Huang, Jian Li:
Capacitated Center Problems with Two-Sided Bounds and Outliers. WADS 2017: 325-336
Informal and Other Publications
- 2024
- [i25]Parikshit Gopalan, Lunjia Hu, Guy N. Rothblum:
On Computationally Efficient Multi-Class Calibration. CoRR abs/2402.07821 (2024) - [i24]Lunjia Hu, Kevin Tian, Chutong Yang:
Testing Calibration in Subquadratic Time. CoRR abs/2402.13187 (2024) - [i23]Lunjia Hu, Yifan Wu:
Predict to Minimize Swap Regret for All Payoff-Bounded Tasks. CoRR abs/2404.13503 (2024) - [i22]Lunjia Hu, Charlotte Peale, Judy Hanwen Shen:
Multigroup Robustness. CoRR abs/2405.00614 (2024) - 2023
- [i21]Lunjia Hu, Inbal Livni Navon, Omer Reingold:
Generative Models of Huge Objects. CoRR abs/2302.12823 (2023) - [i20]Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Adam Tauman Kalai, Preetum Nakkiran:
Loss minimization yields multicalibration for large neural networks. CoRR abs/2304.09424 (2023) - [i19]Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran:
When Does Optimizing a Proper Loss Yield Calibration? CoRR abs/2305.18764 (2023) - [i18]Moses Charikar, Monika Henzinger, Lunjia Hu, Maximilian Vötsch, Erik Waingarten:
Simple, Scalable and Effective Clustering via One-Dimensional Projections. CoRR abs/2310.16752 (2023) - 2022
- [i17]Lunjia Hu, Charlotte Peale, Omer Reingold:
Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability. CoRR abs/2203.04536 (2022) - [i16]Lunjia Hu, Inbal Livni Navon, Omer Reingold, Chutong Yang:
Omnipredictors for Constrained Optimization. CoRR abs/2209.07463 (2022) - [i15]Parikshit Gopalan, Lunjia Hu, Michael P. Kim, Omer Reingold, Udi Wieder:
Loss Minimization through the Lens of Outcome Indistinguishability. CoRR abs/2210.08649 (2022) - [i14]John C. Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar:
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. CoRR abs/2210.13497 (2022) - [i13]Lunjia Hu, Charlotte Peale:
Comparative Learning: A Sample Complexity Theory for Two Hypothesis Classes. CoRR abs/2211.09101 (2022) - [i12]Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran:
A Unifying Theory of Distance from Calibration. CoRR abs/2211.16886 (2022) - 2021
- [i11]Moses Charikar, Lunjia Hu:
Approximation Algorithms for Orthogonal Non-negative Matrix Factorization. CoRR abs/2103.01398 (2021) - [i10]Moses Charikar, Lunjia Hu:
Near-Optimal Explainable k-Means for All Dimensions. CoRR abs/2106.15566 (2021) - [i9]Vincent Cohen-Addad, Anupam Gupta, Lunjia Hu, Hoon Oh, David Saulpic:
An Improved Local Search Algorithm for k-Median. CoRR abs/2111.04589 (2021) - 2020
- [i8]Andrew Bassilakis, Andrew Drucker, Mika Göös, Lunjia Hu, Weiyun Ma, Li-Yang Tan:
The Power of Many Samples in Query Complexity. CoRR abs/2002.10654 (2020) - [i7]Jiaming Song, Lunjia Hu, Yann N. Dauphin, Michael Auli, Tengyu Ma:
Robust and On-the-fly Dataset Denoising for Image Classification. CoRR abs/2003.10647 (2020) - [i6]Lunjia Hu, Omer Reingold:
Robust Mean Estimation on Highly Incomplete Data with Arbitrary Outliers. CoRR abs/2008.08071 (2020) - [i5]Andrew Bassilakis, Andrew Drucker, Mika Göös, Lunjia Hu, Weiyun Ma, Li-Yang Tan:
The Power of Many Samples in Query Complexity. Electron. Colloquium Comput. Complex. TR20 (2020) - 2018
- [i4]Liwei Wang, Lunjia Hu, Jiayuan Gu, Yue Wu, Zhiqiang Hu, Kun He, John E. Hopcroft:
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation. CoRR abs/1810.11750 (2018) - 2017
- [i3]Lunjia Hu, Ruihan Wu, Tianhong Li, Liwei Wang:
Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes. CoRR abs/1702.05677 (2017) - [i2]Hu Ding, Lunjia Hu, Lingxiao Huang, Jian Li:
Capacitated Center Problems with Two-Sided Bounds and Outliers. CoRR abs/1702.07435 (2017) - [i1]Avrim Blum, Lunjia Hu:
Active Tolerant Testing. CoRR abs/1711.00388 (2017)
Coauthor Index
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