Country
CalibrateandBoostLogicalExpressivenessofGNN OverMulti-RelationalandTemporalGraphs
Thistransformation enablesR2-GNN to effectively capture anyFOC2 classifiers when applied to the "transformed" inputgraph. It cannot answerwhich Boolean node classifier can be expressed by GNNs.
Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees Dohyeong Kim
However, the nonlinearity of risk measures makes it challenging to achieve convergence and optimality.
ConStat: Performance-Based Contamination Detection in Large Language Models Jasper Dekoninck 1, Mark Niklas Müller 1,2, Martin Vechev 1 Department of Computer Science 1
Public benchmarks play an essential role in the evaluation of large language models.
A new perspective on building efficient and expressive 3D equivariant graph neural networks
Geometric deep learning enables the encoding of physical symmetries in modeling 3D objects.
QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers
Queuing network control determines the allocation of scarce resources to manage congestion, a fundamental problem in manufacturing, communications, and healthcare.
PAC Learning Linear Thresholds from Label Proportions
The goal is to train a good instance classifier.
Particle-based Variational Inference with Generalized Wasserstein Gradient Flow
We show that GWG exhibits strong convergence guarantees.