2 citations · 2 across the 3 of their papers we have counts for
7 papers
Behaviour Distillation
Andrei Lupu, Chris Lu, Jarek Liesen +2
Dataset distillation aims to condense large datasets into a small number of synthetic examples that can be used as drop-in replacements when training new models. It has application…
Discovering Minimal Reinforcement Learning Environments
Jarek Liesen, Chris Lu, Andrei Lupu +3
Reinforcement learning (RL) agents are commonly trained and evaluated in the same environment. In contrast, humans often train in a specialized environment before being evaluated,…
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…
Discovering Temporally-Aware Reinforcement Learning Algorithms
Matthew Thomas Jackson, Chris Lu, Louis Kirsch +3
Recent advancements in meta-learning have enabled the automatic discovery of novel reinforcement learning algorithms parameterized by surrogate objective functions. To improve upon…
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…
Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free Trainability
Robert Tjarko Lange, Henning Sprekeler
Is the lottery ticket phenomenon an idiosyncrasy of gradient-based training or does it generalize to evolutionary optimization? In this paper we establish the existence of highly s…