no code implementations • 16 Oct 2023 • Makoto Yamada, Yuki Takezawa, Guillaume Houry, Kira Michaela Dusterwald, Deborah Sulem, Han Zhao, Yao-Hung Hubert Tsai
We find that the model performance depends on the combination of TWD and probability model, and that the Jeffrey divergence regularization helps in model training.
no code implementations • 1 Dec 2022 • Deborah Sulem, Vincent Rivoirard, Judith Rousseau
Hawkes processes are often applied to model dependence and interaction phenomena in multivariate event data sets, such as neuronal spike trains, social interactions, and financial transactions.
no code implementations • 29 Mar 2022 • Deborah Sulem, Henry Kenlay, Mihai Cucuringu, Xiaowen Dong
The main novelty of our method is to use a siamese graph neural network architecture for learning a data-driven graph similarity function, which allows to effectively compare the current graph and its recent history.
no code implementations • 21 Mar 2022 • Deborah Sulem, Michele Donini, Muhammad Bilal Zafar, Francois-Xavier Aubet, Jan Gasthaus, Tim Januschowski, Sanjiv Das, Krishnaram Kenthapadi, Cedric Archambeau
In this work we propose a model-agnostic algorithm that generates counterfactual ensemble explanations for time series anomaly detection models.