136 citations · 322 across the 19 of their papers we have counts for
20 papers
Optimizing Neural Networks with Gradient Lexicase Selection
Li Ding, Lee Spector
One potential drawback of using aggregated performance measurement in machine learning is that models may learn to accept higher errors on some training cases as compromises for lo…
Particularity
Lee Spector, Li Ding, Ryan Boldi
We describe a design principle for adaptive systems under which adaptation is driven by particular challenges that the environment poses, as opposed to average or otherwise aggrega…
Probabilistic Lexicase Selection
Li Ding, Edward Pantridge, Lee Spector
Lexicase selection is a widely used parent selection algorithm in genetic programming, known for its success in various task domains such as program synthesis, symbolic regression,…
Can the Problem-Solving Benefits of Quality Diversity Be Obtained Without Explicit Diversity Maintenance?
Ryan Boldi, Lee Spector
When using Quality Diversity (QD) optimization to solve hard exploration or deceptive search problems, we assume that diversity is extrinsically valuable. This means that diversity…
A Static Analysis of Informed Down-Samples
Ryan Boldi, Alexander Lalejini, Thomas Helmuth +1
We present an analysis of the loss of population-level test coverage induced by different down-sampling strategies when combined with lexicase selection. We study recorded populati…
Untangling the Effects of Down-Sampling and Selection in Genetic Programming
Ryan Boldi, Ashley Bao, Martin Briesch +4
Genetic programming systems often use large training sets to evaluate the quality of candidate solutions for selection, which is often computationally expensive. Down-sampling trai…