Good Papers

Showing papers from Ecole Polytechnique Show all papers

67%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026Ecole Polytechnique of ParisUniversité Claude BernardInstitut Polytechnique de ParisTotalEnergiesLinear accelerator LaboratorySpatio-temporal forecasting

Learning Spectral Compositional Koopman Operators for Global-to-Regional Weather Forecasting

khalid OUBLAL, Malo Guichard, François Bertholom, Simon Albergel and 4 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
2/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 2 of 20 reviewers recommend it
lenient 2/5
medium 0/10
strict 0/5
80%Must read
?Must readVote to see the score
NeurIPS 2026INRIAEcole PolytechniqueInriaFederated learning

Principled Federated Random Forests for Heterogeneous Data

FedForest proposes a federated random forest using aggregated statistics to approximate centralized splits under heterogeneous data, enabling personalized client-indicator splits with near-centralized accuracy and low communication cost.

Rémi Khellaf, Erwan Scornet, Aurélien Bellet, Julie Josse

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
12/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 12 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 1/5