Hyebin Song
Assistant Professor of Statistics
-
414 Thomas`
University Park, PA - hps5320@psu.edu
- 814-865-3631
Publication Tags
High Dimensional Variable Selection Labels Convergence Rate Learning Demonstrate Guarantee Class Term Dependent Coefficients Fires Proteins Single Index Model Statistical Inference Monotone Estimator Prediction Index Model Minimax Performance Observation Maximum Likelihood Estimation Testing ModelMost Recent Publications
Convergence guarantee for the sparse monotone single index model<sup>∗</sup>
Ran Dai, Hyebin Song, Rina Foygel Barber, Garvesh Raskutti, 2022, Electronic Journal of Statistics on p. 4449-4496
Prediction in the Presence of Response-Dependent Missing Labels
Hyebin Song, Garvesh Raskutti, Rebecca Willett, 2021, on p. 451-455
Inferring Protein Sequence-Function Relationships with Large-Scale Positive-Unlabeled Learning
Hyebin Song, Bennett J. Bremer, Emily C. Hinds, Garvesh Raskutti, Philip A. Romero, 2021, Cell Systems on p. 92-101.e8
Convex and non-convex approaches for statistical inference with class-conditional noisy labels
Hyebin Song, Ran Dai, Garvesh Raskutti, Rina Foygel Barber, 2020, Journal of Machine Learning Research
Graph-Based Regularization for Regression Problems with Alignment and Highly Correlated Designs
Yuan Li, Benjamin Mark, Garvesh Raskutti, Rebecca Willett, Hyebin Song, David Neiman, 2020, SIAM J. Math. Data Sci. on p. 480--504
The bias of isotonic regression
Ran Dai, Hyebin Song, Rina Barber, Garvesh Raskutti, 2020, Electronic Journal of Statistics on p. 801-834
PUlasso: High-Dimensional Variable Selection With Presence-Only Data
Hyebin Song, Garvesh Raskutti, 2020, Journal of the American Statistical Association on p. 334-347
PUlasso: High-Dimensional Variable Selection With Presence-Only Data
Hyebin Song, Garvesh Raskutti, 2019, Journal of the American Statistical Association on p. 334-347
Most-Cited Papers
Inferring Protein Sequence-Function Relationships with Large-Scale Positive-Unlabeled Learning
Hyebin Song, Bennett J. Bremer, Emily C. Hinds, Garvesh Raskutti, Philip A. Romero, 2021, Cell Systems on p. 92-101.e8
PUlasso: High-Dimensional Variable Selection With Presence-Only Data
Hyebin Song, Garvesh Raskutti, 2020, Journal of the American Statistical Association on p. 334-347
Convex and non-convex approaches for statistical inference with class-conditional noisy labels
Hyebin Song, Ran Dai, Garvesh Raskutti, Rina Foygel Barber, 2020, Journal of Machine Learning Research
Prediction in the Presence of Response-Dependent Missing Labels
Hyebin Song, Garvesh Raskutti, Rebecca Willett, 2021, on p. 451-455
Convergence guarantee for the sparse monotone single index model<sup>∗</sup>
Ran Dai, Hyebin Song, Rina Foygel Barber, Garvesh Raskutti, 2022, Electronic Journal of Statistics on p. 4449-4496
The bias of isotonic regression
Ran Dai, Hyebin Song, Rina Barber, Garvesh Raskutti, 2020, Electronic Journal of Statistics on p. 801-834
PUlasso: High-Dimensional Variable Selection With Presence-Only Data
Hyebin Song, Garvesh Raskutti, 2019, Journal of the American Statistical Association on p. 334-347
Graph-Based Regularization for Regression Problems with Alignment and Highly Correlated Designs
Yuan Li, Benjamin Mark, Garvesh Raskutti, Rebecca Willett, Hyebin Song, David Neiman, 2020, SIAM J. Math. Data Sci. on p. 480--504