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cs.LG2026
Capability-Gated Planning: Cost-to-Goal Discovery and the Limits of Myopic Experiment Selection
Ahmed Hassoon, Mark Dredze
Systems that automate scientific discovery must repeatedly decide which experiment to run, which hypothesis to test, which tool to build, and when to stop. Many systems make these…
cs.LG2025
Can Optimization Trajectories Explain Multi-Task Transfer?
David Mueller, Mark Dredze, Nicholas Andrews
Despite the widespread adoption of multi-task training in deep learning, little is understood about how multi-task learning (MTL) affects generalization. Prior work has conjectured…
cs.LG2024
Transferring Fairness using Multi-Task Learning with Limited Demographic Information
Carlos Aguirre, Mark Dredze
Training supervised machine learning systems with a fairness loss can improve prediction fairness across different demographic groups. However, doing so requires demographic annota…