Current machine learning models have shown high efficiency in solving a wide variety of
real-world problems. However, their black box character poses a major challenge for the …

Abstract Information Pursuit (IP) is a classical active testing algorithm for predicting an output
by sequentially and greedily querying the input in order of information gain. However, IP is …

There is a growing concern about typically opaque decision-making with high-performance
machine learning algorithms. Providing an explanation of the reasoning process in domain …

Deep neural networks have seen tremendous success over the last years. Since the training
is performed on digital hardware, in this paper, we analyze what actually can be computed …

Despite the significant improvements that self-supervised representation learning has led to
when learning from unlabeled data, no methods have been developed that explain what …

Interpretability is a pressing issue for machine learning. Common approaches to
interpretable machine learning constrain interactions between features of the input …

The complex nature of artificial neural networks raises concerns on their reliability,
trustworthiness, and fairness in real-world scenarios. The Shapley value--a solution concept …

Despite the outstanding success of deep neural networks in real-world applications, ranging
from science to public life, most of the related research is empirically driven and a …

Abstract Although Multi-agent Deep Reinforcement Learning (MADRL) has shown
promising results in solving complex real-world problems, the applicability and reliability of …

Unsupervised representation learning has become an important ingredient of today's deep
learning systems. However, only a few methods exist that explain a learned vector …