no code implementations • 27 Feb 2024 • Fabian Spaeh, Charalampos E. Tsourakakis
A notable unresolved query in stochastic processes is learning mixtures of continuous-time Markov chains (CTMCs).
no code implementations • 25 May 2023 • Fabian Spaeh, Alina Ene, Huy L. Nguyen
Constrained $k$-submodular maximization is a general framework that captures many discrete optimization problems such as ad allocation, influence maximization, personalized recommendation, and many others.
1 code implementation • 9 Feb 2023 • Fabian Spaeh, Charalampos E. Tsourakakis
Finally, we empirically observe that combining an EM-algorithm with our method performs best in practice, both in terms of reconstruction error with respect to the distribution of 3-trails and the mixture of Markov Chains.
1 code implementation • 9 Aug 2022 • Fabian Spaeh, Sven Kosub
We transfer distances on clusterings to the building process of decision trees, and as a consequence extend the classical ID3 algorithm to perform modifications based on the global distance of the tree to the ground truth--instead of considering single leaves.
