From the 1 of 8 linked papers with an AI index.
8 papers
Fully Offline Reinforcement Learning
Mattie Fellows, Clarisse Wibault, Uljad Berdica +3
The paper proposes fully offline reinforcement learning methods that use Bayesian model-based techniques to learn dynamics and evaluate policies without any online interaction, ena…
DiscoGen: Procedural Generation of Algorithm Discovery Tasks in Machine Learning
Alexander D. Goldie, Zilin Wang, Adrian Hayler +17
Automating the development of machine learning algorithms has the potential to unlock new breakthroughs. However, our ability to improve and evaluate algorithm discovery systems ha…
Evolving Many Worlds: Towards Open-Ended Discovery in Petri Dish NCA via Population-Based Training
Uljad Berdica, Jakob Foerster, Frank Hutter +1
The generation of sustained, open-ended complexity from local interactions remains a fundamental challenge in artificial life. Differentiable multi-agent systems, such as Petri Dis…
When Do We Need LLMs? A Diagnostic for Language-Driven Bandits
Uljad Berdica, Fernando Acero, Anton Ipsen +3
We study Contextual Multi-Armed Bandits (CMABs) for non-episodic decision-making problems where the context includes both textual and numerical information (e.g., recommendation sy…
Evolution Strategies at the Hyperscale
Bidipta Sarkar, Mattie Fellows, Juan Agustin Duque +17
Evolution Strategies (ES) is a class of powerful black-box optimisation methods that are highly parallelisable and can handle non-differentiable and noisy objectives. However, naï…
Intent Factored Generation: Unleashing the Diversity in Your Language Model
Eltayeb Ahmed, Uljad Berdica, Martha Elliott +2
Obtaining multiple meaningfully diverse, high quality samples from Large Language Models for a fixed prompt remains an open challenge. Current methods for increasing diversity ofte…