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cs.LG2026
Re-Evaluating Continual Learning with Few-Shot Adaptation
Amogh Inamdar, Matthew So, Vici Milenia +1
Continual learning methods aim to maximize the stability and plasticity of machine learning models that are trained on a sequence of tasks. The standard measure of stability (i.e.,…
cs.LG2025
Exploring Human-AI Conceptual Alignment through the Prism of Chess
Semyon Lomasov, Judah Goldfeder, Mehmet Hamza Erol +5
Do AI systems truly understand human concepts or merely mimic surface patterns? We investigate this through chess, where human creativity meets precise strategic concepts. Analyzin…
cs.LG2025
Generating Auxiliary Tasks with Reinforcement Learning
Judah Goldfeder, Matthew So, Hod Lipson
Auxiliary Learning (AL) is a form of multi-task learning in which a model trains on auxiliary tasks to boost performance on a primary objective. While AL has improved generalizatio…