Oral AI Theories

This paper introduces r-lWL, a new graph isomorphism test hierarchy that surpasses the limitations of the Weisfeiler-Leman test by counting cycles up to length r+2, and its GNN counterpart, r-lMPNN, w…

Boosting hippocampal spatial resolution surprisingly shrinks its contextual memory capacity, revealing a crucial trade-off between precision and context storage.

This paper closes the performance gap in parallel boosting algorithms by presenting improved lower bounds and a novel algorithm matching these bounds, settling the parallel complexity of sample-optima…

Neural Pfaffians revolutionize many-electron Schrödinger equation solutions by using fully learnable neural wave functions based on Pfaffians, achieving unprecedented accuracy and generalizability acr…

JKOnet* learns diffusion processes at unprecedented speed and accuracy by directly minimizing a simple quadratic loss function, bypassing complex bilevel optimization problems.

PReBiM algorithm accurately estimates bi-directional causal effects from observational data, even with invalid instruments, using a novel cluster fusion approach.

Two-stage recommender systems using tree structures achieve better generalization with more branches and harmonized training data distributions across stages.

Causal inference revolutionized: New framework estimates causal effects from exchangeable data, enabling simultaneous causal discovery and effect estimation via the Do-Finetti algorithm.