Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning. - Junyi Zhu 002; Ruicong Yao; Matthew B. Blaschko (ICML, 2023)

Alleviating Exposure Bias in Diffusion Models through Sampling with Shifted Time Steps. - Mingxiao Li 002; Tingyu Qu; Ruicong Yao; Wei Sun 046; Marie-Francine Moens (ICLR, 2024)

Fast linear model trees by PILOT. - Jakob Raymaekers; Peter J. Rousseeuw; Tim Verdonck; Ruicong Yao (Mach. Learn., 2024)

Differentiable Causal Structure Learning with Identifiability by NOTIME. - Jeroen Berrevoets; Jakob Raymaekers; Mihaela van der Schaar; Tim Verdonck; Ruicong Yao (AISTATS, 2025)

Causal discovery in mixed additive noise models. - Ruicong Yao; Tim Verdonck; Jakob Raymaekers (AISTATS, 2025)

A Powerful Random Forest Featuring Linear Extensions (RaFFLE). - Jakob Raymaekers; Peter J. Rousseeuw; Thomas Servotte; Tim Verdonck; Ruicong Yao (CoRR, 2025)

Unlocking the Value of Decentralized Data: A Federated Dual Learning Approach for Model Aggregation. - Junyi Zhu 002; Ruicong Yao; Taha Ceritli; Savas Özkan; Matthew B. Blaschko; Eunchung Noh; Jeongwon Min; Cho Jung Min; Mete Ozay (CoRR, 2025)