1 code implementation • 2 Oct 2017 • Magda Gregorova, Alexandros Kalousis, Stephane Marchand-Maillet
We present a new method for forecasting systems of multiple interrelated time series.
no code implementations • 7 Jul 2015 • Magda Gregorova, Alexandros Kalousis, Stéphane Marchand-Maillet
We consider the problem of learning models for forecasting multiple time-series systems together with discovering the leading indicators that serve as good predictors for the system.
no code implementations • 24 Oct 2018 • Frantzeska Lavda, Jason Ramapuram, Magda Gregorova, Alexandros Kalousis
Continual learning is the ability to sequentially learn over time by accommodating knowledge while retaining previously learned experiences.
no code implementations • 26 Mar 2019 • Magda Gregorova
In this thesis we discuss machine learning methods performing automated variable selection for learning sparse predictive models.
no code implementations • 7 Dec 2021 • Yoann Boget, Magda Gregorova, Alexandros Kalousis
One solution consists of using equivariant generative functions, which ensure the ordering invariance.
no code implementations • 13 Jun 2023 • Yoann Boget, Magda Gregorova, Alexandros Kalousis
Despite advances in generative methods, accurately modeling the distribution of graphs remains a challenging task primarily because of the absence of predefined or inherent unique graph representation.
1 code implementation • 11 Aug 2023 • Philipp Vaeth, Alexander M. Fruehwald, Benjamin Paassen, Magda Gregorova
Latest methods for visual counterfactual explanations (VCE) harness the power of deep generative models to synthesize new examples of high-dimensional images of impressive quality.
1 code implementation • 1 Dec 2022 • Yoann Boget, Magda Gregorova, Alexandros Kalousis
The model we propose tackles the problem of generating the data features constrained by the specific graph structure of each data point by splitting the task into two phases.
1 code implementation • ICLR 2018 • Jason Ramapuram, Magda Gregorova, Alexandros Kalousis
Lifelong learning is the problem of learning multiple consecutive tasks in a sequential manner, where knowledge gained from previous tasks is retained and used to aid future learning over the lifetime of the learner.