Challenging Common Assumptions in Convex Reinforcement Learning. - Mirco Mutti; Riccardo De Santi; Piersilvio De Bartolomeis; Marcello Restelli (NeurIPS, 2022)

How robust accuracy suffers from certified training with convex relaxations. - Piersilvio De Bartolomeis; Jacob Clarysse; Amartya Sanyal; Fanny Yang (CoRR, 2023)

Convex Reinforcement Learning in Finite Trials. - Mirco Mutti; Riccardo De Santi; Piersilvio De Bartolomeis; Marcello Restelli (J. Mach. Learn. Res., 2023)

Hidden yet quantifiable: A lower bound for confounding strength using randomized trials. - Piersilvio De Bartolomeis; Javier Abad Martinez; Konstantin Donhauser; Fanny Yang (AISTATS, 2024)

Detecting critical treatment effect bias in small subgroups. - Piersilvio De Bartolomeis; Javier Abad; Konstantin Donhauser; Fanny Yang (UAI, 2024)

Doubly robust identification of treatment effects from multiple environments. - Piersilvio De Bartolomeis; Julia Kostin; Javier Abad; Yixin Wang; Fanny Yang (ICLR, 2025)

Efficient Randomized Experiments Using Foundation Models. - Piersilvio De Bartolomeis; Javier Abad; Guanbo Wang; Konstantin Donhauser; Raymond M. Duch; Fanny Yang; Issa J. Dahabreh (CoRR, 2025)

Causal Lifting of Neural Representations: Zero-Shot Generalization for Causal Inferences. - Riccardo Cadei; Ilker Demirel; Piersilvio De Bartolomeis; Lukas Lindorfer; Sylvia Cremer; Cordelia Schmid; Francesco Locatello (CoRR, 2025)

Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data. - Rickard Karlsson; Piersilvio De Bartolomeis; Issa J. Dahabreh; Jesse H. Krijthe (CoRR, 2025)