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02/13/2023
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models
Contrastively trained text-image models have the remarkable ability to p...
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02/11/2023
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play
Standard empirical risk minimization (ERM) training can produce deep neu...
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05/01/2022
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
Accurate uncertainty quantification is a major challenge in deep learnin...
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04/15/2022
Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees
We develop a simple and unified framework for nonlinear variable selecti...
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10/13/2020
Training independent subnetworks for robust prediction
Recent approaches to efficiently ensemble neural networks have shown tha...
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10/05/2020
Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior
Traditional (unstructured) pruning methods for a Transformer model focus...
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06/17/2020
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Bayesian neural networks (BNN) and deep ensembles are principled approac...
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12/03/2019
Variable Selection with Rigorous Uncertainty Quantification using Deep Bayesian Neural Networks: Posterior Concentration and Bernstein-von Mises Phenomenon
This work develops rigorous theoretical basis for the fact that deep Bay...
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11/11/2019
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
Ensemble learning is a standard approach to building machine learning sy...
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04/24/2019
A Cross-validated Ensemble Approach to Robust Hypothesis Testing of Continuous Nonlinear Interactions: Application to Nutrition-Environment Studies
Gene-environment and nutrition-environment studies often involve testing...
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04/21/2019
Gaussian Process Regression and Classification under Mathematical Constraints with Learning Guarantees
We introduce constrained Gaussian process (CGP), a Gaussian process mode...
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04/01/2019
Adaptive Ensemble Learning of Spatiotemporal Processes with Calibrated Predictive Uncertainty: A Bayesian Nonparametric Approach
Ensemble learning is a mainstay in modern data science practice. Convent...
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12/08/2018
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process
Ensemble learning is a mainstay in modern data science practice. Convent...
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11/26/2018
Cross-Validated Kernel Ensemble: Robust Hypothesis Test for Nonlinear Effect with Gaussian Process
The R package CVEK introduces a robust hypothesis test for nonlinear eff...
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10/03/2017