Modular meta-learning. - Ferran Alet; Tomás Lozano-Pérez; Leslie Pack Kaelbling (CoRL, 2018)

Finding Frequent Entities in Continuous Data. - Ferran Alet; Rohan Chitnis; Leslie Pack Kaelbling; Tomás Lozano-Pérez (IJCAI, 2018)

Modular meta-learning in abstract graph networks for combinatorial generalization. - Ferran Alet; Maria Bauzá 001; Alberto Rodriguez 003; Tomás Lozano-Pérez; Leslie Pack Kaelbling (CoRR, 2018)

Graph Element Networks: adaptive, structured computation and memory. - Ferran Alet; Adarsh Keshav Jeewajee; Maria Bauzá Villalonga; Alberto Rodriguez 003; Tomás Lozano-Pérez; Leslie Pack Kaelbling (ICML, 2019)

Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video. - Maria Bauzá 001; Ferran Alet; Yen-Chen Lin; Tomás Lozano-Pérez; Leslie Pack Kaelbling; Phillip Isola; Alberto Rodriguez 003 (IROS, 2019)

Neural Relational Inference with Fast Modular Meta-learning. - Ferran Alet; Erica Weng; Tomás Lozano-Pérez; Leslie Pack Kaelbling (NeurIPS, 2019)

Meta-learning curiosity algorithms. - Ferran Alet; Martin F. Schneider; Tomás Lozano-Pérez; Leslie Pack Kaelbling (ICLR, 2020)

Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time. - Ferran Alet; Kenji Kawaguchi; Tomás Lozano-Pérez; Leslie Pack Kaelbling (CoRR, 2020)

Efficiently Building a Large Scale Dataset for Program Induction. - Lauren Milechin; Javier Lopez-Contreras; Ferran Alet (HPEC, 2021)

A large-scale benchmark for few-shot program induction and synthesis. - Ferran Alet; Javier Lopez-Contreras; James Koppel; Maxwell I. Nye; Armando Solar-Lezama; Tomás Lozano-Pérez; Leslie Pack Kaelbling; Joshua B. Tenenbaum (ICML, 2021)

Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time. - Ferran Alet; Maria Bauzá 001; Kenji Kawaguchi; Nurullah Giray Kuru; Tomás Lozano-Pérez; Leslie Pack Kaelbling (NeurIPS, 2021)

Noether Networks: meta-learning useful conserved quantities. - Ferran Alet; Dylan Doblar; Allan Zhou; Josh Tenenbaum 001; Kenji Kawaguchi; Chelsea Finn (NeurIPS, 2021)

GraphCast: Learning skillful medium-range global weather forecasting. - Rémi Lam; Alvaro Sanchez-Gonzalez; Matthew Willson; Peter Wirnsberger; Meire Fortunato; Alexander Pritzel; Suman V. Ravuri; Timo Ewalds; Ferran Alet; Zach Eaton-Rosen; Weihua Hu; Alexander Merose; et al. (CoRR, 2022)

Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching. - Andy Zeng 001; Shuran Song; Kuan-Ting Yu; Elliott Donlon; Francois Robert Hogan; Maria Bauzá 001; Daolin Ma; Orion Taylor; Melody Liu; Eudald Romo; Nima Fazeli; Ferran Alet; et al. (Int. J. Robotics Res., 2022)

Learning inside the prediction function - Ferran Alet (2022)

GenCast: Diffusion-based ensemble forecasting for medium-range weather. - Ilan Price; Alvaro Sanchez-Gonzalez; Ferran Alet; Timo Ewalds; Andrew El-Kadi; Jacklynn Stott; Shakir Mohamed; Peter W. Battaglia; Rémi Lam; Matthew Willson (CoRR, 2023)

Functional Risk Minimization. - Ferran Alet; Clement Gehring; Tomás Lozano-Pérez; Kenji Kawaguchi; Joshua B. Tenenbaum; Leslie Pack Kaelbling (CoRR, 2024)

Probabilistic weather forecasting with machine learning. - Ilan Price; Alvaro Sanchez-Gonzalez; Ferran Alet; Tom R. Andersson; Andrew El-Kadi; Dominic Masters; Timo Ewalds; Jacklynn Stott; Shakir Mohamed; Peter W. Battaglia; Rémi R. Lam; Matthew Willson (Nat., 2025)

Skillful joint probabilistic weather forecasting from marginals. - Ferran Alet; Ilan Price; Andrew El-Kadi; Dominic Masters; Stratis Markou; Tom R. Andersson; Jacklynn Stott; Rémi Lam; Matthew Willson; Alvaro Sanchez-Gonzalez; Peter W. Battaglia (CoRR, 2025)