Lunjia Hu - RankMe

61 papers A* 16A 6B 2Journal 36Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Equitable Evaluation via Elicitation. CoRR Elbert Du, Cynthia Dwork, Lunjia Hu, Reid McIlroy-Young, Han Shao, Linjun Zhang
2026	J	`jnl`	Near-optimal Swap Regret Minimization for Convex Losses. CoRR Lunjia Hu, Jon Schneider, Yifan Wu
2026	J	`jnl`	Simultaneous Blackwell Approachability and Applications to Multiclass Omniprediction. CoRR Lunjia Hu, Kevin Tian, Chutong Yang
2025	J	`jnl`	A Perfectly Truthful Calibration Measure. CoRR Jason Hartline, Lunjia Hu, Yifan Wu
2025	J	`jnl`	Calibration through the Lens of Indistinguishability. CoRR Parikshit Gopalan, Lunjia Hu
2025	J	`jnl`	Efficient Swap Multicalibration of Elicitable Properties. CoRR Lunjia Hu, Haipeng Luo, Spandan Senapati, Vatsal Sharan
2025	—	`conf`	Generalized and Unified Equivalences Between Hardness and Pseudoentropy. TCC (4) Lunjia Hu, Salil P. Vadhan
2025	J	`jnl`	Generalized and Unified Equivalences between Hardness and Pseudoentropy. CoRR Lunjia Hu, Salil P. Vadhan
2025	J	`jnl`	How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension. CoRR Cynthia Dwork, Lunjia Hu, Han Shao
2025	J	`jnl`	Inducing Efficient and Equitable Professional Networks through Link Recommendations. CoRR Cynthia Dwork, Chris Hays, Lunjia Hu, Nicole Immorlica, Juan C. Perdomo
2025	J	`jnl`	Making and Evaluating Calibrated Forecasts. CoRR Yuxuan Lu, Yifan Wu, Jason Hartline, Lunjia Hu
2025	A*	`conf`	Omnipredicting Single-Index Models with Multi-index Models. STOC Lunjia Hu, Kevin Tian, Chutong Yang
2024	A	`conf`	Loss Minimization Yields Multicalibration for Large Neural Networks. ITCS Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Adam Tauman Kalai, Preetum Nakkiran
2024	A*	`conf`	Multigroup Robustness. ICML Lunjia Hu, Charlotte Peale, Judy Hanwen Shen
2024	J	`jnl`	Multigroup Robustness. CoRR Lunjia Hu, Charlotte Peale, Judy Hanwen Shen
2024	J	`jnl`	Omnipredicting Single-Index Models with Multi-Index Models. CoRR Lunjia Hu, Kevin Tian, Chutong Yang
2024	A*	`conf`	On Computationally Efficient Multi-Class Calibration. COLT Parikshit Gopalan, Lunjia Hu, Guy N. Rothblum
2024	J	`jnl`	On Computationally Efficient Multi-Class Calibration. CoRR Parikshit Gopalan, Lunjia Hu, Guy N. Rothblum
2024	A*	`conf`	Predict to Minimize Swap Regret for All Payoff-Bounded Tasks. FOCS Lunjia Hu, Yifan Wu
2024	J	`jnl`	Predict to Minimize Swap Regret for All Payoff-Bounded Tasks. CoRR Lunjia Hu, Yifan Wu
2024	A*	`conf`	Testing Calibration in Nearly-Linear Time. NeurIPS Lunjia Hu, Arun Jambulapati, Kevin Tian, Chutong Yang
2024	J	`jnl`	Testing Calibration in Subquadratic Time. CoRR Lunjia Hu, Kevin Tian, Chutong Yang
2023	A*	`conf`	A Unifying Theory of Distance from Calibration. STOC Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran
2023	A	`conf`	Comparative Learning: A Sample Complexity Theory for Two Hypothesis Classes. ITCS Lunjia Hu, Charlotte Peale
2023	A	`conf`	Generative Models of Huge Objects. CCC Lunjia Hu, Inbal Livni Navon, Omer Reingold
2023	J	`jnl`	Generative Models of Huge Objects. CoRR Lunjia Hu, Inbal Livni Navon, Omer Reingold
2023	A	`conf`	Loss Minimization Through the Lens Of Outcome Indistinguishability. ITCS Parikshit Gopalan, Lunjia Hu, Michael P. Kim, Omer Reingold, Udi Wieder
2023	J	`jnl`	Loss minimization yields multicalibration for large neural networks. CoRR Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Adam Tauman Kalai, Preetum Nakkiran
2023	A*	`conf`	Omnipredictors for Constrained Optimization. ICML Lunjia Hu, Inbal Rachel Livni Navon, Omer Reingold, Chutong Yang
2023	A*	`conf`	Simple, Scalable and Effective Clustering via One-Dimensional Projections. NeurIPS Moses Charikar, Monika Henzinger, Lunjia Hu, Maximilian Vötsch, Erik Waingarten
2023	J	`jnl`	Simple, Scalable and Effective Clustering via One-Dimensional Projections. CoRR Moses Charikar, Monika Henzinger, Lunjia Hu, Maximilian Vötsch, Erik Waingarten
2023	A*	`conf`	When Does Optimizing a Proper Loss Yield Calibration? NeurIPS Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran
2023	J	`jnl`	When Does Optimizing a Proper Loss Yield Calibration? CoRR Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran
2022	J	`jnl`	A Unifying Theory of Distance from Calibration. CoRR Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran
2022	A*	`conf`	An Improved Local Search Algorithm for k-Median. SODA Vincent Cohen-Addad, Anupam Gupta, Lunjia Hu, Hoon Oh, David Saulpic
2022	J	`jnl`	Comparative Learning: A Sample Complexity Theory for Two Hypothesis Classes. CoRR Lunjia Hu, Charlotte Peale
2022	J	`jnl`	Loss Minimization through the Lens of Outcome Indistinguishability. CoRR Parikshit Gopalan, Lunjia Hu, Michael P. Kim, Omer Reingold, Udi Wieder
2022	B	`conf`	Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability. ALT Lunjia Hu, Charlotte Peale, Omer Reingold
2022	J	`jnl`	Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability. CoRR Lunjia Hu, Charlotte Peale, Omer Reingold
2022	A*	`conf`	Near-Optimal Explainable k-Means for All Dimensions. SODA Moses Charikar, Lunjia Hu
2022	J	`jnl`	Omnipredictors for Constrained Optimization. CoRR Lunjia Hu, Inbal Livni Navon, Omer Reingold, Chutong Yang
2022	A*	`conf`	Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. NeurIPS John C. Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar
2022	J	`jnl`	Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. CoRR John C. Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar
2021	J	`jnl`	An Improved Local Search Algorithm for k-Median. CoRR Vincent Cohen-Addad, Anupam Gupta, Lunjia Hu, Hoon Oh, David Saulpic
2021	A	`conf`	Approximation Algorithms for Orthogonal Non-negative Matrix Factorization. AISTATS Moses Charikar, Lunjia Hu
2021	J	`jnl`	Approximation Algorithms for Orthogonal Non-negative Matrix Factorization. CoRR Moses Charikar, Lunjia Hu
2021	J	`jnl`	Near-Optimal Explainable k-Means for All Dimensions. CoRR Moses Charikar, Lunjia Hu
2021	A	`conf`	Robust Mean Estimation on Highly Incomplete Data with Arbitrary Outliers. AISTATS Lunjia Hu, Omer Reingold
2020	J	`jnl`	Robust Mean Estimation on Highly Incomplete Data with Arbitrary Outliers. CoRR Lunjia Hu, Omer Reingold
2020	J	`jnl`	Robust and On-the-fly Dataset Denoising for Image Classification. CoRR Jiaming Song, Lunjia Hu, Yann N. Dauphin, Michael Auli, Tengyu Ma
2020	A*	`conf`	The Power of Many Samples in Query Complexity. ICALP Andrew Bassilakis, Andrew Drucker, Mika Göös, Lunjia Hu, Weiyun Ma, Li-Yang Tan
2020	J	`jnl`	The Power of Many Samples in Query Complexity. CoRR Andrew Bassilakis, Andrew Drucker, Mika Göös, Lunjia Hu, Weiyun Ma, Li-Yang Tan
2020	J	`jnl`	The Power of Many Samples in Query Complexity. Electron. Colloquium Comput. Complex. Andrew Bassilakis, Andrew Drucker, Mika Göös, Lunjia Hu, Weiyun Ma, Li-Yang Tan
2018	A*	`conf`	Active Tolerant Testing. COLT Avrim Blum, Lunjia Hu
2018	A*	`conf`	Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation. NeurIPS Liwei Wang, Lunjia Hu, Jiayuan Gu, Zhiqiang Hu, Yue Wu, Kun He, John E. Hopcroft
2018	J	`jnl`	Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation. CoRR Liwei Wang, Lunjia Hu, Jiayuan Gu, Yue Wu, Zhiqiang Hu, Kun He, John E. Hopcroft
2017	J	`jnl`	Active Tolerant Testing. CoRR Avrim Blum, Lunjia Hu
2017	B	`conf`	Capacitated Center Problems with Two-Sided Bounds and Outliers. WADS Hu Ding, Lunjia Hu, Lingxiao Huang, Jian Li
2017	J	`jnl`	Capacitated Center Problems with Two-Sided Bounds and Outliers. CoRR Hu Ding, Lunjia Hu, Lingxiao Huang, Jian Li
2017	A*	`conf`	Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes. COLT Lunjia Hu, Ruihan Wu, Tianhong Li, Liwei Wang
2017	J	`jnl`	Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes. CoRR Lunjia Hu, Ruihan Wu, Tianhong Li, Liwei Wang