3 papers
cs.LG2026
OrthoGen: A Generative Orthogonal Learner for Time-Varying Treatments
Tomàs Garriga, Valentyn Melnychuk, Konstantin Hess +4
Estimating conditional distributional potential outcomes (CDPOs) over time is important in medicine (e.g., to estimate patient-specific risks under different treatment sequences).…
cs.LG2026
Decision-Aware Proximal Bridge Learning for Optimal Treatment Selection
Tomàs Garriga, Alejandro Almodóvar, Axel Brando +3
Individualized treatment selection with continuous actions requires accurate causal response estimation in decision-relevant regions, rather than uniformly over the entire action s…
cs.LG2023
Retrospective Uncertainties for Deep Models using Vine Copulas
Nataša Tagasovska, Firat Ozdemir, Axel Brando
Despite the major progress of deep models as learning machines, uncertainty estimation remains a major challenge. Existing solutions rely on modified loss functions or architectura…