Bill Squires
Papers
1
Total Citations
4
H-Index
1
About
Bill Squires is a researcher whose work explores the intersection of machine learning and robotics, with a particular focus on the challenges of learning from demonstration. His most cited paper, "Unlearning from demonstration" (2013), addresses a critical problem in robotic learning: how to effectively correct errant behavior when a demonstrator provides corrective examples. Squires developed algorithms that use this corrective data to identify and remove noisy examples in datasets that cause misclassification, enabling robots to more efficiently unlearn problematic behaviors. While his citation count of 4 reflects the niche and emerging nature of his work, Squires' contribution is notable for tackling the often-overlooked issue of how to gracefully handle and correct mistakes in learning systems—a problem that becomes increasingly important as robots and AI agents are deployed in real-world environments where errors are inevitable. His research provides a foundation for more robust, adaptable learning systems that can improve through targeted correction rather than requiring complete retraining.
Research Focus
Key Achievements
Top Papers
- 1Unlearning from demonstration4 citations · 2013