1 code implementation • 27 Apr 2024 • Yassine Abbahaddou, Sofiane Ennadir, Johannes F. Lutzeyer, Michalis Vazirgiannis, Henrik Boström
In this work, we theoretically define the concept of expected robustness in the context of attributed graphs and relate it to the classical definition of adversarial robustness in the graph representation learning literature.
1 code implementation • 21 Feb 2024 • Sofiane Ennadir, Yassine Abbahaddou, Johannes F. Lutzeyer, Michalis Vazirgiannis, Henrik Boström
Successful combinations of our NoisyGNN approach with existing defense techniques demonstrate even further improved adversarial defense results.
no code implementations • 24 Nov 2023 • Henrik Boström
A random forest prediction can be computed by the scalar product of the labels of the training examples and a set of weights that are determined by the leafs of the forest into which the test object falls; each prediction can hence be explained exactly by the set of training examples for which the weights are non-zero.
1 code implementation • Data Mining and Knowledge Discovery 2023 • Amir Hossein Akhavan Rahnama, Judith Bütepage, Pierre Geurts, Henrik Boström
Local model-agnostic additive explanation techniques decompose the predicted output of a black-box model into additive feature importance scores.
no code implementations • 23 Aug 2023 • Amr AlKhatib, Henrik Boström, Sofiane Ennadir, Ulf Johansson
The results also suggest that the proposed method can produce tight intervals, while providing validity guarantees.
1 code implementation • 17 Aug 2023 • Amr AlKhatib, Sofiane Ennadir, Henrik Boström, Michalis Vazirgiannis
Data in tabular format is frequently occurring in real-world applications.
no code implementations • 27 May 2022 • Nancy Xu, Giannis Nikolentzos, Michalis Vazirgiannis, Henrik Boström
Image matching is a key component of many tasks in computer vision and its main objective is to find correspondences between features extracted from different natural images.
no code implementations • 23 Mar 2021 • Negar Safinianaini, Henrik Boström
Mixtures of Hidden Markov Models (MHMMs) are frequently used for clustering of sequential data.
no code implementations • 31 Oct 2019 • 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.
no code implementations • 23 Apr 2019 • Theodore Vasiloudis, Hyunsu Cho, Henrik Boström
As a result, we are able to reduce the training time for high-dimensional data, and allow more cost-effective scale-out without the need for expensive network communication.
1 code implementation • 27 Dec 2016 • Andreas Henelius, Kai Puolamäki, Henrik Boström, Panagiotis Papapetrou
In this study, we propose a technique for quantifying the instability of a clustering solution and for finding robust clusters, termed core clusters, which correspond to clusters where the co-occurrence probability of each data item within a cluster is at least $1 - \alpha$.