15 citations · 39 across the 7 of their papers we have counts for
7 papers
Pareto-Optimal Learning from Preferences with Hidden Context
Ryan Bahlous-Boldi, Li Ding, Lee Spector +1
Ensuring AI models align with human values is essential for their safety and functionality. Reinforcement learning from human feedback (RLHF) leverages human preferences to achieve…
DALex: Lexicase-like Selection via Diverse Aggregation
Andrew Ni, Li Ding, Lee Spector
Lexicase selection has been shown to provide advantages over other selection algorithms in several areas of evolutionary computation and machine learning. In its standard form, lex…
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…
Objectives Are All You Need: Solving Deceptive Problems Without Explicit Diversity Maintenance
Ryan Boldi, Li Ding, Lee Spector
Navigating deceptive domains has often been a challenge in machine learning due to search algorithms getting stuck at sub-optimal local optima. Many algorithms have been proposed t…
Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization
Li Ding, Jenny Zhang, Jeff Clune +2
Reinforcement Learning from Human Feedback (RLHF) has shown potential in qualitative tasks where easily defined performance measures are lacking. However, there are drawbacks when…
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,…
Lexicase Selection at Scale
Li Ding, Ryan Boldi, Thomas Helmuth +1
Lexicase selection is a semantic-aware parent selection method, which assesses individual test cases in a randomly-shuffled data stream. It has demonstrated success in multiple res…