5 papers
Provably Sub-Linear Two-Timescale NeuroEvolution with Online Plasticity
Shishen Lin, Yixin Chen
NeuroEvolution of Augmenting Topologies (NEAT) is a widely used neuroevolution algorithm for learning neural network architectures and weights for control tasks. However, standard…
Randomised Optimism via Competitive Co-Evolution for Matrix Games with Bandit Feedback
Shishen Lin
Learning in games is a fundamental problem in machine learning and artificial intelligence, with numerous applications~\citep{silver2016mastering,schrittwieser2020mastering}. This…
DeepMath-Creative: A Benchmark for Evaluating Mathematical Creativity of Large Language Models
Xiaoyang Chen, Xinan Dai, Yu Du +28
To advance the mathematical proficiency of large language models (LLMs), the DeepMath team has launched an open-source initiative aimed at developing an open mathematical LLM and s…
Overcoming Binary Adversarial Optimisation with Competitive Coevolution
Per Kristian Lehre, Shishen Lin
Co-evolutionary algorithms (CoEAs), which pair candidate designs with test cases, are frequently used in adversarial optimisation, particularly for binary test-based problems where…
Concentration Tail-Bound Analysis of Coevolutionary and Bandit Learning Algorithms
Per Kristian Lehre, Shishen Lin
Runtime analysis, as a branch of the theory of AI, studies how the number of iterations algorithms take before finding a solution (its runtime) depends on the design of the algorit…