27 citations · 43 across the 11 of their papers we have counts for
13 papers
LLM-to-Flow Distillation: Teaching Natural Language Goals to Scientific Generators
Riccardo De Santi, Federico Di Gennaro, Angelo Gnazzo +6
Generative discovery in science requires coupling high-level scientific reasoning with the ability to generate samples respecting the structure of a design space. Yet these capabil…
Global Transport Couplings for Classifier-Free Guided Flows
Katarina Petrović, Zander W. Blasingame, Danyal Rehman +5
Optimal-transport couplings have been shown to reduce training variance in unconditional flow models, but their role in conditional generation remains unclear. A natural approach c…
RelAgent: LLM Agents as Data Scientists for Relational Learning
Xingyue Huang, Louis Tichelman, Jinwoo Kim +2
Relational learning is a challenging problem that has motivated a wide range of approaches, including graph-based models (e.g., graph neural networks, graph transformers), tabular…
Categorical Flow Maps
Daan Roos, Oscar Davis, Floor Eijkelboom +5
We introduce Categorical Flow Maps, a flow-matching method for accelerated few-step generation of categorical data via self-distillation. Building on recent variational formulation…
Flock: A Knowledge Graph Foundation Model via Learning on Random Walks
Jinwoo Kim, Xingyue Huang, Krzysztof Olejniczak +4
We study the problem of zero-shot link prediction on knowledge graphs (KGs), which requires models to generalize to novel entities and novel relations. Knowledge graph foundation m…
One Model, Any Conjunctive Query: Graph Neural Networks for Answering Queries over Incomplete Knowledge Graphs
Krzysztof Olejniczak, Xingyue Huang, Mikhail Galkin +1
Motivated by the incompleteness of modern knowledge graphs, a new setup for query answering has emerged, where the goal is to predict answers that do not necessarily appear in the…
Future Directions in the Theory of Graph Machine Learning
Christopher Morris, Fabrizio Frasca, Nadav Dym +7
Machine learning on graphs, especially using graph neural networks (GNNs), has seen a surge in interest due to the wide availability of graph data across a broad spectrum of discip…
PlanE: Representation Learning over Planar Graphs
Radoslav Dimitrov, Zeyang Zhao, Ralph Abboud +1
Graph neural networks are prominent models for representation learning over graphs, where the idea is to iteratively compute representations of nodes of an input graph through a se…
Shortest Path Networks for Graph Property Prediction
Ralph Abboud, Radoslav Dimitrov, İsmail İlkan Ceylan
Most graph neural network models rely on a particular message passing paradigm, where the idea is to iteratively propagate node representations of a graph to each node in the direc…
Temporal Knowledge Graph Completion using Box Embeddings
Johannes Messner, Ralph Abboud, İsmail İlkan Ceylan
Knowledge graph completion is the task of inferring missing facts based on existing data in a knowledge graph. Temporal knowledge graph completion (TKGC) is an extension of this ta…
Node Classification Meets Link Prediction on Knowledge Graphs
Ralph Abboud, İsmail İlkan Ceylan
Node classification and link prediction are widely studied in graph representation learning. While both transductive node classification and link prediction operate over a single i…
The Surprising Power of Graph Neural Networks with Random Node Initialization
Ralph Abboud, İsmail İlkan Ceylan, Martin Grohe +1
Graph neural networks (GNNs) are effective models for representation learning on relational data. However, standard GNNs are limited in their expressive power, as they cannot disti…
BoxE: A Box Embedding Model for Knowledge Base Completion
Ralph Abboud, İsmail İlkan Ceylan, Thomas Lukasiewicz +1
Knowledge base completion (KBC) aims to automatically infer missing facts by exploiting information already present in a knowledge base (KB). A promising approach for KBC is to emb…