A Variational-Sequential Graph Autoencoder for Neural Architecture Performance Prediction. - David Friede; Jovita Lukasik; Heiner Stuckenschmidt; Margret Keuper (CoRR, 2019)

Neural Architecture Performance Prediction Using Graph Neural Networks. - Jovita Lukasik; David Friede; Heiner Stuckenschmidt; Margret Keuper (GCPR, 2020)

Smooth Variational Graph Embeddings for Efficient Neural Architecture Search. - Jovita Lukasik; David Friede; Arber Zela; Heiner Stuckenschmidt; Frank Hutter; Margret Keuper (CoRR, 2020)

Smooth Variational Graph Embeddings for Efficient Neural Architecture Search. - Jovita Lukasik; David Friede; Arber Zela; Frank Hutter; Margret Keuper (IJCNN, 2021)

Efficient Learning of Discrete-Continuous Computation Graphs. - David Friede; Mathias Niepert (NeurIPS, 2021)

Learning Disentangled Discrete Representations. - David Friede; Christian Reimers; Heiner Stuckenschmidt; Mathias Niepert (ECML/PKDD (4), 2023)

AgentQuest: A Modular Benchmark Framework to Measure Progress and Improve LLM Agents. - Luca Gioacchini; Giuseppe Siracusano; Davide Sanvito; Kiril Gashteovski; David Friede; Roberto Bifulco; Carolin Lawrence (NAACL (Demonstrations), 2024)