Among the different possible strategies for evaluating the reliability of individual predictions of classifiers, robustness quantification stands out as a method that evaluates how much uncertainty a classifier could cope with before changing its prediction. However, its applicability is more limited than some of its alternatives, since it requires the use of generative models and restricts the analyses either to specific model architectures or discrete features. In this work, we propose a new robustness metric applicable to any probabilistic discriminative classifier and any type of features. We demonstrate that this new metric is capable of distinguishing between reliable and unreliable predictions, and use this observation to develop new strategies for dynamic classifier selection.

AML Trends in 2022: What Y ou Need to Know .

The UN estimates 2-5% of global GDP or $0.8 - $2.0 trillion dollars are laundered

Graph Contrastive Learning (GCL) has emerged as a powerful approach for generating graph representations without the need for manual annotation. Most advanced GCL methods fall into three main frameworks: node discrimination, group discrimination, and bootstrapping schemes, all of which achieve comparable performance. However, the underlying mechanisms and factors that contribute to their effectiveness are not yet fully understood.

GNNs to mitigate the bias as well as boost their utility in an end-to-end manner.

In this work, we propose data-efficient and interpretable machine learning models for learning to solve numeric planning tasks.

We give a model-based agent that builds a Python program representing its knowledge of the world based on its interactions with the environment.