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04/30/2023
Reliable Gradient-free and Likelihood-free Prompt Tuning
Due to privacy or commercial constraints, large pre-trained language mod...
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12/14/2022
Post-hoc Uncertainty Learning using a Dirichlet Meta-Model
It is known that neural networks have the problem of being over-confiden...
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12/13/2022
Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting
In consequential decision-making applications, mitigating unwanted biase...
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12/01/2022
Are you using test log-likelihood correctly?
Test log-likelihood is commonly used to compare different models of the ...
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06/13/2021
Post-hoc loss-calibration for Bayesian neural networks
Bayesian decision theory provides an elegant framework for acting optima...
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06/11/2021
Measuring the sensitivity of Gaussian processes to kernel choice
Gaussian processes (GPs) are used to make medical and scientific decisio...
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06/02/2021
Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI
In this paper, we describe an open source Python toolkit named Uncertain...
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06/01/2021
Uncertainty Characteristics Curves: A Systematic Assessment of Prediction Intervals
Accurate quantification of model uncertainty has long been recognized as...
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12/18/2020
EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders
Researchers require timely access to real-world longitudinal electronic ...
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07/13/2020
Model Fusion with Kullback–Leibler Divergence
We propose a method to fuse posterior distributions learned from heterog...
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07/05/2020
Mobility driven Cloud-Fog-Edge Framework for Location-aware Services: A Comprehensive Review
With the pervasiveness of IoT devices, smart-phones and improvement of l...
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06/23/2020
Approximate Cross-Validation for Structured Models
Many modern data analyses benefit from explicitly modeling dependence st...
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11/01/2019
Statistical Model Aggregation via Parameter Matching
We consider the problem of aggregating models learned from sequestered, ...
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06/24/2019
Quality of Uncertainty Quantification for Bayesian Neural Network Inference
Bayesian Neural Networks (BNNs) place priors over the parameters in a ne...
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05/28/2019
Bayesian Nonparametric Federated Learning of Neural Networks
In federated learning problems, data is scattered across different serve...
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04/26/2019
DPVis: Visual Exploration of Disease Progression Pathways
Clinical researchers use disease progression modeling algorithms to pred...
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11/16/2018
Latent Projection BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights
While modern neural networks are making remarkable gains in terms of pre...
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11/14/2018
Unsupervised learning with contrastive latent variable models
In unsupervised learning, dimensionality reduction is an important tool ...
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06/13/2018
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors
Bayesian Neural Networks (BNNs) have recently received increasing attent...
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02/19/2018
Simultaneous Modeling of Multiple Complications for Risk Profiling in Diabetes Care
Type 2 diabetes mellitus (T2DM) is a chronic disease that often results ...
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05/29/2017
