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cs.LG2026
Probabilistic Counterfactual Inference for Discrete Outcomes in Gaussian-Process Causal Models
Juliette Sinnott, Amir-Hossein Karimi, Mohammad Kohandel
Counterfactual inference in Gaussian-process structural causal models (GP-SCMs) has been developed primarily for continuous endogenous variables, limiting applicability to causal g…
cs.LG2026
Private and interpretable clinical prediction with quantum-inspired tensor train models
José Ramón Pareja Monturiol, Juliette Sinnott, Roger G. Melko +1
Publicly available clinical machine learning models pose an underappreciated privacy risk: their parameters or outputs can be exploited to recover information from patients whose d…