2 papers
cs.LG2026
Evolving Afferent Architectures: Biologically-inspired Models for Damage-Avoidance Learning
Wolfgang Maass, Sabine Janzen, Prajvi Saxena +1
We introduce Afferent Learning, a framework that produces Computational Afferent Traces (CATs) as adaptive, internal risk signals for damage-avoidance learning. Inspired by biologi…
stat.ML2026
Many Experiments, Few Repetitions, Unpaired Data, and Sparse Effects: Is Causal Inference Possible?
Felix Schur, Niklas Pfister, Peng Ding +2
We study the problem of estimating causal effects under hidden confounding in the following unpaired data setting: we observe some covariates and an outcome under different…