6 citations · 26 across the 11 of their papers we have counts for
14 papers
Multigroup Robustness
Lunjia Hu, Charlotte Peale, Judy Hanwen Shen
To address the shortcomings of real-world datasets, robust learning algorithms have been designed to overcome arbitrary and indiscriminate data corruption. However, practical proce…
Comparative Learning: A Sample Complexity Theory for Two Hypothesis Classes
Lunjia Hu, Charlotte Peale
In many learning theory problems, a central role is played by a hypothesis class: we might assume that the data is labeled according to a hypothesis in the class (usually referred…
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise
John Duchi, Vitaly Feldman, Lunjia Hu +1
Recovering linear subspaces from data is a fundamental and important task in statistics and machine learning. Motivated by heterogeneity in Federated Learning settings, we study a…
Loss Minimization through the Lens of Outcome Indistinguishability
Parikshit Gopalan, Lunjia Hu, Michael P. Kim +2
We present a new perspective on loss minimization and the recent notion of Omniprediction through the lens of Outcome Indistingusihability. For a collection of losses and hypothesi…
Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability
Lunjia Hu, Charlotte Peale, Omer Reingold
We give the first sample complexity characterizations for outcome indistinguishability, a theoretical framework of machine learning recently introduced by Dwork, Kim, Reingold, Rot…
An Improved Local Search Algorithm for k-Median
Vincent Cohen-Addad, Anupam Gupta, Lunjia Hu +2
We present a new local-search algorithm for the -median clustering problem. We show that local optima for this algorithm give a -approximation; our result improves up…