4 papers
How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings?
Julia Kostin, Kasra Jalaldoust, Elias Bareinboim +2
Machine learning models often degrade when they are deployed on a target distribution that differs from the source distributions they were trained on. Recent work in causality-base…
Doubly robust identification of treatment effects from multiple environments
Piersilvio De Bartolomeis, Julia Kostin, Javier Abad +2
Practical and ethical constraints often require the use of observational data for causal inference, particularly in medicine and social sciences. Yet, observational datasets are pr…
Achievable distributional robustness when the robust risk is only partially identified
Julia Kostin, Nicola Gnecco, Fanny Yang
In safety-critical applications, machine learning models should generalize well under worst-case distribution shifts, that is, have a small robust risk. Invariance-based algorithms…
How robust is randomized blind deconvolution via nuclear norm minimization against adversarial noise?
Julia Kostin, Felix Krahmer, Dominik Stöger
In this paper, we study the problem of recovering two unknown signals from their convolution, which is commonly referred to as blind deconvolution. Reformulation of blind deconvolu…