26 citations · 94 across the 25 of their papers we have counts for
3 papers · 1 filter
Applications of fractional calculus in learned optimization
Teodor Alexandru Szente, James Harrison, Mihai Zanfir +1
Fractional gradient descent has been studied extensively, with a focus on its ability to extend traditional gradient descent methods by incorporating fractional-order derivatives.…
Offline Hierarchical Reinforcement Learning via Inverse Optimization
Carolin Schmidt, Daniele Gammelli, James Harrison +2
Hierarchical policies enable strong performance in many sequential decision-making problems, such as those with high-dimensional action spaces, those requiring long-horizon plannin…
Universal Neural Functionals
Allan Zhou, Chelsea Finn, James Harrison
A challenging problem in many modern machine learning tasks is to process weight-space features, i.e., to transform or extract information from the weights and gradients of a neura…