2 citations · 2 across the 4 of their papers we have counts for
5 papers · 1 filter
Unconstrained Online Learning with Unbounded Losses
Andrew Jacobsen, Ashok Cutkosky
Algorithms for online learning typically require one or more boundedness assumptions: that the domain is bounded, that the losses are Lipschitz, or both. In this paper, we develop…
Parameter-free Mirror Descent
Andrew Jacobsen, Ashok Cutkosky
We develop a modified online mirror descent framework that is suitable for building adaptive and parameter-free algorithms in unbounded domains. We leverage this technique to devel…
Continual Auxiliary Task Learning
Matthew McLeod, Chunlok Lo, Matthew Schlegel +4
Learning auxiliary tasks, such as multiple predictions about the world, can provide many benefits to reinforcement learning systems. A variety of off-policy learning algorithms hav…
Parameter-free Gradient Temporal Difference Learning
Andrew Jacobsen, Alan Chan
Reinforcement learning lies at the intersection of several challenges. Many applications of interest involve extremely large state spaces, requiring function approximation to enabl…
Meta-descent for Online, Continual Prediction
Andrew Jacobsen, Matthew Schlegel, Cameron Linke +3
This paper investigates different vector step-size adaptation approaches for non-stationary online, continual prediction problems. Vanilla stochastic gradient descent can be consid…