Stage-Aware Dual Alignment for Covariate Shift in Graph Domain Adaptation

Hongwei Wen, Can Zhang, Haoyu He, Xintao Zhao and 2 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

Beyond Masked Sparsity: SNACK Enables Truly Sparse Neural Networks on GPU

Jafar Badour, Elena Mocanu, Maurice Keulen

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

Breaking the Grid: Distance-Guided Reinforcement Learning in Large Discrete Action Spaces

DGRL enables efficient reinforcement learning in discrete action spaces up to 10^20 via distance-guided exploration and regression-based updates, improving performance by up to 66%.

Heiko Hoppe, Fabian Akkerman, Wouter van Heeswijk, Maximilian Schiffer

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

Hyper Input Convex Neural Networks for Shape Constrained Learning and Optimal Transport

HyCNNs combine Maxout and ICNN principles to learn convex functions with exponentially fewer parameters than ICNNs, outperforming baselines in convex regression and optimal transport.

Shayan Hundrieser, Insung Kong, Johannes Schmidt-Hieber

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

Inpainting physics: self-supervised learning for context-driven fluid simulation

Steady CFD inference is reformulated as self-supervised inpainting with a local tokeniser, yielding reusable flow priors that outperform supervised surrogates under boundary shifts and enable local geometry editing.

Jonas Weidner, Yeray Martin-Ruisanchez, Daniel Rueckert, Benedikt Wiestler and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026