2 citations · 2 across the 1 of their papers we have counts for
2 papers
cs.HC2022★ 2 cited
Explainability in Machine Learning: a Pedagogical Perspective
Andreas Bueff, Ioannis Papantonis, Auste Simkute +1
Given the importance of integrating of explainability into machine learning, at present, there are a lack of pedagogical resources exploring this. Specifically, we have found a nee…
cs.LG2018
Tractable Querying and Learning in Hybrid Domains via Sum-Product Networks
Andreas Bueff, Stefanie Speichert, Vaishak Belle
Probabilistic representations, such as Bayesian and Markov networks, are fundamental to much of statistical machine learning. Thus, learning probabilistic representations directly…