The adoption of machine learning in healthcare calls for model transpare...
Transformer based large language models with emergent capabilities are
b...
A common way to explore text corpora is through low-dimensional projecti...
Discount regularization, using a shorter planning horizon when calculati...
N-of-1 trials aim to estimate treatment effects on the individual level ...
Mobile health (mHealth) technologies empower patients to adopt/maintain
...
Comparing Bayesian neural networks (BNNs) with different widths is
chall...
Interpretability provides a means for humans to verify aspects of machin...
For responsible decision making in safety-critical settings, machine lea...
We develop a Reinforcement Learning (RL) framework for improving an exis...
Bayesian neural networks (BNNs) combine the expressive power of deep lea...
Machine learning models that incorporate concept learning as an intermed...
Variational inference enables approximate posterior inference of the hig...
Variational Auto-encoders (VAEs) are deep generative latent variable mod...
Traditional training of deep classifiers yields overconfident models tha...
Neural Linear Models (NLM) are deep models that produce predictive
uncer...
Contextual bandits often provide simple and effective personalization in...
Variational Auto-encoders (VAEs) are deep generative latent variable mod...
Many ensemble methods encourage their constituent models to be diverse,
...
Bayesian Neural Networks with Latent Variables (BNN+LV's) provide
uncert...
Bayesian Neural Networks (BNNs) place priors over the parameters in a ne...
Tensor decomposition methods allow us to learn the parameters of latent
...
In real-world applications, it is often expensive and time-consuming to
...
While modern neural networks are making remarkable gains in terms of
pre...
There has been growing interest in developing accurate models that can a...
Tensor decomposition methods are popular tools for learning latent varia...
In this work, we empirically explore the question: how can we assess the...