6 citations · 33 across the 18 of their papers we have counts for
17 papers · 1 filter
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…
Calibration Error for Decision Making
Lunjia Hu, Yifan Wu
Calibration allows predictions to be reliably interpreted as probabilities by decision makers. We propose a decision-theoretic calibration error, the Calibration Decision Loss (CDL…
Testing Calibration in Nearly-Linear Time
Lunjia Hu, Arun Jambulapati, Kevin Tian +1
In the recent literature on machine learning and decision making, calibration has emerged as a desirable and widely-studied statistical property of the outputs of binary prediction…
On Computationally Efficient Multi-Class Calibration
Parikshit Gopalan, Lunjia Hu, Guy N. Rothblum
Consider a multi-class labelling problem, where the labels can take values in , and a predictor predicts a distribution over the labels. In this work, we study the following f…
Simple, Scalable and Effective Clustering via One-Dimensional Projections
Moses Charikar, Monika Henzinger, Lunjia Hu +2
Clustering is a fundamental problem in unsupervised machine learning with many applications in data analysis. Popular clustering algorithms such as Lloyd's algorithm and -means+…
When Does Optimizing a Proper Loss Yield Calibration?
Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu +1
Optimizing proper loss functions is popularly believed to yield predictors with good calibration properties; the intuition being that for such losses, the global optimum is to pred…