1.7k citations · 1.9k across the 10 of their papers we have counts for
10 papers
Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior
Zi Lin, Jeremiah Zhe Liu, Zi Yang +2
Traditional (unstructured) pruning methods for a Transformer model focus on regularizing the individual weights by penalizing them toward zero. In this work, we explore spectral-no…
Training independent subnetworks for robust prediction
Marton Havasi, Rodolphe Jenatton, Stanislav Fort +5
Recent approaches to efficiently ensemble neural networks have shown that strong robustness and uncertainty performance can be achieved with a negligible gain in parameters over th…
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy +3
Bayesian neural networks (BNN) and deep ensembles are principled approaches to estimate the predictive uncertainty of a deep learning model. However their practicality in real-time…
Variable Selection with Rigorous Uncertainty Quantification using Deep Bayesian Neural Networks: Posterior Concentration and Bernstein-von Mises Phenomenon
Jeremiah Zhe Liu
This work develops rigorous theoretical basis for the fact that deep Bayesian neural network (BNN) is an effective tool for high-dimensional variable selection with rigorous uncert…
A Cross-validated Ensemble Approach to Robust Hypothesis Testing of Continuous Nonlinear Interactions: Application to Nutrition-Environment Studies
Jeremiah Zhe Liu, Jane Lee, Pi-i Debby Lin +6
Gene-environment and nutrition-environment studies often involve testing of high-dimensional interactions between two sets of variables, each having potentially complex nonlinear m…
Gaussian Process Regression and Classification under Mathematical Constraints with Learning Guarantees
Jeremiah Zhe Liu
We introduce constrained Gaussian process (CGP), a Gaussian process model for random functions that allows easy placement of mathematical constrains (e.g., non-negativity, monotoni…
Spatially Adaptive Ensemble Learning with Calibrated Predictive Uncertainty
Yanran Li, Jeremiah Zhe Liu, John Paisley +2
Air pollution exposure assessment often uses ensembles of spatio-temporal models, but a critical limitation is that conventional methods use deterministic weights and fail to quant…
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process
Jeremiah Zhe Liu, John Paisley, Marianthi-Anna Kioumourtzoglou +1
Ensemble learning is a mainstay in modern data science practice. Conventional ensemble algorithms assigns to base models a set of deterministic, constant model weights that (1) do…
CVEK: Robust Estimation and Testing for Nonlinear Effects using Kernel Machine Ensemble
Wenying Deng, Jeremiah Zhe Liu, Erin Lake +1
The R package CVEK introduces a suite of flexible machine learning models and robust hypothesis tests for learning the joint nonlinear effects of multiple covariates in limited sam…
Image reconstruction by domain transform manifold learning
Bo Zhu, Jeremiah Z. Liu, Bruce R. Rosen +1
Image reconstruction plays a critical role in the implementation of all contemporary imaging modalities across the physical and life sciences including optical, MRI, CT, PET, and r…