4 accepted papers
Graph Neural Networks (GNNs) have emerged as powerful tools for learning over graph-structured data, yet recent studies have shown that their performance gains are beginning to plateau. In many cases, well-established models such as GCN and GAT, when appropriately tuned, can match or even exceed the
Large Language Models (LLMs) have revolutionized natural language processing, but their varying capabilities and costs pose challenges in practical applications. LLM routing addresses this by dynamically selecting the most suitable LLM for each query/task. Previous approaches treat this as a supervi
We focus on the learning of generalized neural policies for Relational Markov Decision Processes (RMDPs) expressed in RDDL. Recent work first converts the instances of a relational domain into an instance graph, and then trains a Graph Attention Network (GAT) of fixed depth with parameters shared ac…
Relational MDPs (RMDPs) compactly represent an infinite set of MDPs with an unbounded number of objects. Solving an RMDP requires a generalized policy that applies to all instances of a domain. Recently, Garg et al. proposed SymNet for this task– it constructs a graph neural network that shares para…