13 citations · 21 across the 4 of their papers we have counts for
5 papers
Explaining Predictions by Characteristic Rules
Amr Alkhatib, Henrik Boström, Michalis Vazirgiannis
Characteristic rules have been advocated for their ability to improve interpretability over discriminative rules within the area of rule learning. However, the former type of rule…
Towards interpretability of Mixtures of Hidden Markov Models
Negar Safinianaini, Henrik Boström
Mixtures of Hidden Markov Models (MHMMs) are frequently used for clustering of sequential data. An important aspect of MHMMs, as of any clustering approach, is that they can be int…
A study of data and label shift in the LIME framework
Amir Hossein Akhavan Rahnama, Henrik Boström
LIME is a popular approach for explaining a black-box prediction through an interpretable model that is trained on instances in the vicinity of the predicted instance. To generate…
Block-distributed Gradient Boosted Trees
Theodore Vasiloudis, Hyunsu Cho, Henrik Boström
The Gradient Boosted Tree (GBT) algorithm is one of the most popular machine learning algorithms used in production, for tasks that include Click-Through Rate (CTR) prediction and…
Clustering with Confidence: Finding Clusters with Statistical Guarantees
Andreas Henelius, Kai Puolamäki, Henrik Boström +1
Clustering is a widely used unsupervised learning method for finding structure in the data. However, the resulting clusters are typically presented without any guarantees on their…