Statistical Learning
Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic
Actor-critic (AC) algorithms, empowered by neural networks, have had significant empirical success in recent years.
Assisted Learning: A Framework for Multi-Organization Learning
In an increasing number of AI scenarios, collaborations among different organizations or agents (e.g., human and robots, mobile units) are often essential to
Supplementary Materials: Probabilistic Tensor Decomposition of Neural Population Spiking Activity
In this section, we derive the variational updates. The last term contains cross entropies between prior and variational PG distributions. The full VB model affects this limit in two ways. Its impact on the second parameter is indirect. How plausible is this "good fit" regime?
Probabilistic Tensor Decomposition of Neural Population Spiking Activity
V is constrained to vary along a limited set of dimensions.
Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering
Recent self-supervised pre-training methods on Heterogeneous Information Networks (HINs) have shown promising competitiveness over traditional semi-supervised Heterogeneous Graph Neural Networks (HGNNs).
Scalable Graph Neural Networks via Bidirectional Propagation
Recently, there has been increased interest in designing GNN that scales to large graphs. Most existing methods use "graph sampling" or "layer-wise
