Learning Hidden Causal Structure from Temporal Data. - Simon Valentin; Neil Bramley; Christopher Lucas 001 (CogSci, 2020)

Interpreting neural decoding models using grouped model reliance. - Simon Valentin; Maximilian Harkotte; Tzvetan Popov (PLoS Comput. Biol., 2020)

Know your network: Sensitivity to structure in social learning. - Jan-Philipp Fränken; Simon Valentin; Chris Lucas 001; Neil R. Bramley (CogSci, 2021)

Bayesian Experimental Design for Intractable Models of Cognition. - Simon Valentin; Steven Kleinegesse; Neil R. Bramley; Michael U. Gutmann; Chris Lucas 001 (CogSci, 2021)

Symbolic and Sub-Symbolic Systems in People and Machines. - Simon Valentin; Bonan Zhao 001; Chentian Jiang; Neil Bramley; Chris Lucas 001 (CogSci, 2021)

Bayesian Optimal Experimental Design for Simulator Models of Cognition. - Simon Valentin; Steven Kleinegesse; Neil R. Bramley; Michael U. Gutmann; Christopher G. Lucas (CoRR, 2021)

Designing Optimal Behavioral Experiments Using Machine Learning. - Simon Valentin; Steven Kleinegesse; Neil R. Bramley; Peggy Seriès; Michael U. Gutmann; Christopher G. Lucas (CoRR, 2023)

Probabilistic Demand Forecasting with Graph Neural Networks. - Nikita Kozodoi; Elizaveta Zinovyeva; Simon Valentin; João Pereira; Rodrigo Agundez (CoRR, 2024)

Cost-Effective Hallucination Detection for LLMs. - Simon Valentin; Jinmiao Fu; Gianluca Detommaso; Shaoyuan Xu; Giovanni Zappella; Bryan Wang (CoRR, 2024)

SWE-PolyBench: A multi-language benchmark for repository level evaluation of coding agents. - Muhammad Shihab Rashid; Christian Bock; Yuan Zhuang; Alexander Buchholz; Timothy B. Esler; Simon Valentin; Luca Franceschi 003; Martin Wistuba; Prabhu Teja Sivaprasad; Woo Jung Kim; Anoop Deoras; Giovanni Zappella; et al. (CoRR, 2025)