28 citations · 55 across the 5 of their papers we have counts for
5 papers
Information Theoretic Measures of Causal Influences during Transient Neural Events
Kaidi Shao, Nikos K. Logothetis, Michel Besserve
Transient phenomena play a key role in coordinating brain activity at multiple scales, however,their underlying mechanisms remain largely unknown. A key challenge for neural data s…
Bayesian Information Criterion for Event-based Multi-trial Ensemble data
Kaidi Shao, Nikos K. Logothetis, Michel Besserve
Transient recurring phenomena are ubiquitous in many scientific fields like neuroscience and meteorology. Time inhomogenous Vector Autoregressive Models (VAR) may be used to charac…
A theory of independent mechanisms for extrapolation in generative models
Michel Besserve, Rémy Sun, Dominik Janzing +1
Generative models can be trained to emulate complex empirical data, but are they useful to make predictions in the context of previously unobserved environments? An intuitive idea…
Counterfactuals uncover the modular structure of deep generative models
Michel Besserve, Arash Mehrjou, Rémy Sun +1
Deep generative models can emulate the perceptual properties of complex image datasets, providing a latent representation of the data. However, manipulating such representation to…
Group invariance principles for causal generative models
Michel Besserve, Naji Shajarisales, Bernhard Schölkopf +1
The postulate of independence of cause and mechanism (ICM) has recently led to several new causal discovery algorithms. The interpretation of independence and the way it is utilize…