Hyebin Song
Assistant Professor of Statistics
-
414 Thomas`
University Park, PA - hps5320@psu.edu
- 814-865-3631
Publication Tags
Variable Selection High Dimensional Learning Proteins Estimator Statistical Inference Labels Class Maximum Likelihood Estimation Testing Demonstrate Minimax Simulation Logistics Selection Bias Generalized Linear Model Quantify Observation Asymptotic Variance Machine Learning Logistic Regression Optimization Sparsity Stationary Point Expectation Maximization AlgorithmMost Recent 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
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
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
PUlasso
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
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
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, 2019, Journal of the American Statistical Association on p. 334-347