Back to the Manifold: Recovering from Out-of-Distribution States. - Alfredo Reichlin; Giovanni Luca Marchetti; Hang Yin 001; Ali Ghadirzadeh; Danica Kragic (IROS, 2022)

On the Subspace Structure of Gradient-Based Meta-Learning. - Gustaf Tegnér; Alfredo Reichlin; Hang Yin 001; Mårten Björkman; Danica Kragic (CoRR, 2022)

Elastic Context: Encoding Elasticity for Data-driven Models of Textiles. - Alberta Longhini; Marco Moletta; Alfredo Reichlin; Michael C. Welle; Alexander Kravberg; Yufei Wang 007; David Held; Zackory Erickson; Danica Kragic (CoRR, 2022)

Mental Face Image Retrieval Based on a Closed-Loop Brain-Computer Interface. - Nona Rajabi; Charles Chernik; Alfredo Reichlin; Farzaneh Taleb; Miguel Vasco; Ali Ghadirzadeh; Mårten Björkman; Danica Kragic (HCI (9), 2023)

EDO-Net: Learning Elastic Properties of Deformable Objects from Graph Dynamics. - Alberta Longhini; Marco Moletta; Alfredo Reichlin; Michael C. Welle; David Held; Zackory Erickson; Danica Kragic (ICRA, 2023)

Elastic Context: Encoding Elasticity for Data-driven Models of Textiles Elastic Context: Encoding Elasticity for Data-driven Models of Textiles. - Alberta Longhini; Marco Moletta; Alfredo Reichlin; Michael C. Welle; Alexander Kravberg; Yufei Wang 007; David Held; Zackory Erickson; Danica Kragic (ICRA, 2023)

Learning Geometric Representations of Objects via Interaction. - Alfredo Reichlin; Giovanni Luca Marchetti; Hang Yin 001; Anastasiia Varava; Danica Kragic (ECML/PKDD (4), 2023)

Goal-Conditioned Offline Reinforcement Learning via Metric Learning. - Alfredo Reichlin; Miguel Vasco; Hang Yin 001; Danica Kragic (CoRR, 2024)

Model-based reinforcement learning for protein backbone design. - Frederic Renard; Cyprien Courtot; Alfredo Reichlin; Oliver Bent (CoRR, 2024)

Reducing Variance in Meta-Learning via Laplace Approximation for Regression Tasks. - Alfredo Reichlin; Gustaf Tegnér; Miguel Vasco; Hang Yin 001; Mårten Björkman; Danica Kragic (Trans. Mach. Learn. Res., 2024)

Walking on the Fiber: A Simple Geometric Approximation for Bayesian Neural Networks. - Alfredo Reichlin; Miguel Vasco; Danica Kragic (Trans. Mach. Learn. Res., 2025)