5 citations · 9 across the 7 of their papers we have counts for
7 papers
Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization
Robert Tjarko Lange, Qi Sun, Aaditya Prasad +3
Recent advances in large language models (LLMs) demonstrate their effectiveness in scaling test-time compute for software engineering tasks. However, these approaches often focus o…
Competition and Attraction Improve Model Fusion
João Abrantes, Robert Tjarko Lange, Yujin Tang
Model merging is a powerful technique for integrating the specialized knowledge of multiple machine learning models into a single model. However, existing methods require manually…
Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization
Maxence Faldor, Robert Tjarko Lange, Antoine Cully
Quality-Diversity has emerged as a powerful family of evolutionary algorithms that generate diverse populations of high-performing solutions by implementing local competition princ…
Position: Leverage Foundational Models for Black-Box Optimization
Xingyou Song, Yingtao Tian, Robert Tjarko Lange +3
Undeniably, Large Language Models (LLMs) have stirred an extraordinary wave of innovation in the machine learning research domain, resulting in substantial impact across diverse fi…
Evolution Transformer: In-Context Evolutionary Optimization
Robert Tjarko Lange, Yingtao Tian, Yujin Tang
Evolutionary optimization algorithms are often derived from loose biological analogies and struggle to leverage information obtained during the sequential course of optimization. A…
Large Language Models As Evolution Strategies
Robert Tjarko Lange, Yingtao Tian, Yujin Tang
Large Transformer models are capable of implementing a plethora of so-called in-context learning algorithms. These include gradient descent, classification, sequence completion, tr…
NeuroEvoBench: Benchmarking Evolutionary Optimizers for Deep Learning Applications
Robert Tjarko Lange, Yujin Tang, Yingtao Tian
Recently, the Deep Learning community has become interested in evolutionary optimization (EO) as a means to address hard optimization problems, e.g. meta-learning through long inne…