The Rashomon set is the set of models that perform approximately equally well on a given
dataset, and the Rashomon ratio is the fraction of all models in a given hypothesis space …

Machine learning models are becoming increasingly popular in different types of settings.
This is mainly caused by their ability to achieve a level of predictive performance that is hard …

It is almost always easier to find an accurate-but-complex model than an accurate-yet-simple
model. Finding optimal, sparse, accurate models of various forms (linear models with integer …

In any given machine learning problem, there may be many models that could explain the
data almost equally well. However, most learning algorithms return only one of these …

The* Rashomon Effect*, coined by Leo Breiman, describes the phenomenon that there exist
many equally good predictive models for the same dataset. This phenomenon happens for …

The machine learning modeling process conventionally culminates in selecting a single
model that maximizes a selected performance metric. However, this approach leads to …

There has been recent interest in improving performance of simple models for multiple
reasons such as interpretability, robust learning from small data, deployment in memory …

This work proposes xGEMs or manifold guided exemplars, a framework to understand black-
box classifier behavior by exploring the landscape of the underlying data manifold as data …

Neural networks exhibit simplicity bias; they rely on simpler features while ignoring equally
predictive but more complex features. In this work, we introduce a novel approach termed …

An important feature of successful supervised machine learning applications is to be able to
explain the predictions given by the regression or classification model being used. However …