Scalable Supervised Optimal Transport of Gaussian Mixture Models

Damin Kühn, Michael T Schaub

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

Causal Discovery over Clusters of Variables in Non-Markovian Systems

Tara Anand, Adèle H Ribeiro, Jin Tian, George Hripcsak and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

Efficient Knowledge Transfer in Federated Bayesian Optimization through Neural Network Surrogates

Alexander Gräfe, Max van Gemmeren, Paul Brunzema, Sebastian Trimpe

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

Dyna-Style Safety Augmented Reinforcement Learning: Staying Safe in the Face of Uncertainty

Dyna-SAuR learns scalable safety filters and policies via uncertainty-aware dynamics to reduce training failures by two orders of magnitude versus baselines.

Artur Eisele, Bernd Frauenknecht, Friedrich Solowjow, Sebastian Trimpe

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

Efficient Lookahead Encoding and Abstracted Width for Learning General Policies in Classical Planning

Holistic relational encoding and abstracted width enable GNN policies to learn general classical planning strategies efficiently, surpassing LAMA on IPC 2023 benchmarks.

Michael Aichmüller, Simon Ståhlberg, Martin Funkquist, Hector Geffner

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

Differentiable Learning of Lifted Action Schemas for Classical Planning

A neural architecture learns lifted STRIPS action schemas from fully observed state traces with hidden action arguments, recovering ground-truth domain structures robustly.

Jonas Reiter, Jakob Gebler, Hector Geffner

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

Brain-OF: An Omnifunctional Foundation Model for fMRI, EEG and MEG

Brain-OF is a multimodal foundation model jointly pretrained on fMRI, EEG, and MEG via unified sampling, sparse mixture-of-experts attention, and dual-domain masked modeling, achieving superior cross-modal neuroscience performance.

Hanning Guo, Hanwen Bi, Farah Abdellatif, Andrei Galbenus and 3 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks

A feasibility-preserving graph neural network replaces SDP solvers in exact Max-Cut branch-and-bound, cutting bounding costs up to 10.6× versus Mosek.

Hao Chen, Chendi Qian, Christopher Morris, Andrea Lodi and 1 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026