Issue #103 Theory Edwin Dong, Anurag Shinde
Deep Evidential Regression, Simply Explained
AI systems are now integrated in high-stakes domains such as healthcare, security, autonomous vehicles, and finance, where incorrect predictions have severe consequences.
However, most machine learning models provide only a single prediction such as a class label or numerical value without conveying how confident the model actually is.
Feed models an input they have never seen before, and they will still output a number, with no indication of a bad answer.
This limitation has motivated the development of uncertainty quantification techniques, where ML/AI models are developed to be able to simultaneously compute confidence along with their predictions.
A couple weeks ago, we looked at conformal prediction, an uncertainty quantification technique that creates guaranteed prediction sets based on quantiles.
Today, we’ll look at another helpful method from this field.
Deep Evidential Regression (Amini et al., 2020), published at NeurIPS by researchers at MIT and Harvard, proposes another method to quantify model uncertainty.
It trains a standard neural network to output not just a prediction, but uncertainty descriptors driven by probability.
The result is a model that is genuinely uncertain about doubtful outputs, calibrated enough to use in safety-critical settings, and fast enough to run in real time.
Deep Evidential Regression has the capability to understand two different types of uncertainties, which puts it a step above a method we have seen before: Gaussian Process Regression.
This is considered important because it can provide a higher level of clarity about prediction confidence and, additionally, can also significantly benefit the training process.
What You’ll Learn
The two kinds of uncertainty in machine learning
The evidential idea (what previous approaches lacked)
The crucial regularizer
Applications of evidential regression
Why evidential regression matters
What’s Helpful to Know
A statistical inference method that uses Bayes’ theorem to update a prior distribution (a model’s beliefs) given new observations.
Prior, Posterior, Conjugate Distributions
Prior Distribution: The model's initial belief about a parameter before observing any new data.
Posterior Distribution: The updated distribution after considering new observations.
Conjugate Prior: A convenience where the prior and posterior distributions belong to the same probability family.
Normal-Inverse-Gamma (NIG) Distribution
A higher-level, four-parameter probability distribution. Essentially a distribution over distributions.
Student’s t-Distribution
A probability distribution that looks like a standard normal curve with "heavier tails."
The Two Kinds of Uncertainty
Before understanding the method, it helps to be precise about what uncertainty means for a neural network.
