19 citations · 56 across the 6 of their papers we have counts for
7 papers
A Hierarchy of Entropy-Shapley Games for Multivariate Predictive Uncertainty
Niklas Koenen, Claudia Battistin, Jeriek Van den Abeele +1
Modern probabilistic machine learning models increasingly produce multivariate outputs with complex dependence structure, from multi-step time-series forecasts to sample path predi…
Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles
Martin Bjerke, Lukas Schott, Kristopher T. Jensen +3
Systems neuroscience relies on two complementary views of neural data, characterized by single neuron tuning curves and analysis of population activity. These two perspectives comb…
How is model-related uncertainty quantified and reported in different disciplines?
Emily G. Simmonds, Kwaku Peprah Adjei, Christoffer Wold Andersen +40
How do we know how much we know? Quantifying uncertainty associated with our modelling work is the only way we can answer how much we know about any phenomenon. With quantitative s…
The Stochastic complexity of spin models: Are pairwise models really simple?
Alberto Beretta, Claudia Battistin, Clélia de Mulatier +2
Models can be simple for different reasons: because they yield a simple and computationally efficient interpretation of a generic dataset (e.g. in terms of pairwise dependences) -…
The appropriateness of ignorance in the inverse kinetic Ising model
Benjamin Dunn, Claudia Battistin
We develop efficient ways to consider and correct for the effects of hidden units for the paradigmatic case of the inverse kinetic Ising model with fully asymmetric couplings. We i…
Variational perturbation and extended Plefka approaches to dynamics on random networks: the case of the kinetic Ising model
Ludovica Bachschmid-Romano, Claudia Battistin, Manfred Opper +1
We describe and analyze some novel approaches for studying the dynamics of Ising spin glass models. We first briefly consider the variational approach based on minimizing the Kullb…
Belief-Propagation and replicas for inference and learning in a kinetic Ising model with hidden spins
Claudia Battistin, John Hertz, Joanna Tyrcha +1
We propose a new algorithm for inferring the state of hidden spins and reconstructing the connections in a synchronous kinetic Ising model, given the observed history. Focusing on…