2 papers
stat.ML2026
ProximalFM: Amortized Proximal Causal Inference under Hidden Confounding
Christophe Muller, Ayub Kharel, Alex Luedtke +7
Standard causal identification methods often assume no unmeasured confounding and can fail when relevant confounders are unobserved. Proximal causal inference instead uses proxy va…
stat.ML2026
Generalization in Nonlinear Least Squares via Learned Feature Geometry
Ayub Kharel, Ilja Kuzborskij, Patrick Rebeschini +1
We study the generalization of ridge-regularized nonlinear least-squares models via on-average algorithmic stability, deriving error bounds for local minimizers in terms of a data-…