110 citations · 168 across the 18 of their papers we have counts for
4 papers · 1 filter
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste +4
Learning the causal structure that underlies data is a crucial step towards robust real-world decision making. The majority of existing work in causal inference focuses on determin…
Meta-Learning Reliable Priors in the Function Space
Jonas Rothfuss, Dominique Heyn, Jinfan Chen +1
When data are scarce meta-learning can improve a learner's accuracy by harnessing previous experience from related learning tasks. However, existing methods have unreliable uncerta…
DiBS: Differentiable Bayesian Structure Learning
Lars Lorch, Jonas Rothfuss, Bernhard Schölkopf +1
Bayesian structure learning allows inferring Bayesian network structure from data while reasoning about the epistemic uncertainty -- a key element towards enabling active causal di…
Robustness to Pruning Predicts Generalization in Deep Neural Networks
Lorenz Kuhn, Clare Lyle, Aidan N. Gomez +2
Existing generalization measures that aim to capture a model's simplicity based on parameter counts or norms fail to explain generalization in overparameterized deep neural network…