3 citations · 3 across the 6 of their papers we have counts for
6 papers
Sublinearly Structured Deep Neural Networks Achieve Feature Learning Consistency for Compositional Functions
Sehwan Kim, Yan Sun, Faming Liang
Over the past decade, deep neural networks (DNNs) have achieved remarkable success on complex machine-learning tasks, yet the theoretical foundations of their performance remain in…
A Scalable Nonparametric Continuous-Time Survival Model through Numerical Quadrature
Chaeyeon Lee, Sehwan Kim, Hyungrok Do
Flexible continuous-time survival modeling is critical for capturing complex time-varying hazard dynamics in high-dimensional data; however, training such models remains challengin…
Extended Fiducial Inference for Individual Treatment Effects via Deep Neural Networks
Sehwan Kim, Faming Liang
Individual treatment effect estimation has gained significant attention in recent data science literature. This work introduces the Double Neural Network (Double-NN) method to addr…
Extended Fiducial Inference: Toward an Automated Process of Statistical Inference
Faming Liang, Sehwan Kim, Yan Sun
While fiducial inference was widely considered a big blunder by R.A. Fisher, the goal he initially set --`inferring the uncertainty of model parameters on the basis of observations…
A New Paradigm for Generative Adversarial Networks based on Randomized Decision Rules
Sehwan Kim, Qifan Song, Faming Liang
The Generative Adversarial Network (GAN) was recently introduced in the literature as a novel machine learning method for training generative models. It has many applications in st…
Stochastic Gradient Langevin Dynamics Algorithms with Adaptive Drifts
Sehwan Kim, Qifan Song, Faming Liang
Bayesian deep learning offers a principled way to address many issues concerning safety of artificial intelligence (AI), such as model uncertainty,model interpretability, and predi…