no code implementations • 29 May 2024 • Andrew Jacobsen, Ashok Cutkosky
We develop algorithms for online linear regression which achieve optimal static and dynamic regret guarantees \emph{even in the complete absence of prior knowledge}.
no code implementations • 8 Jun 2023 • 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.
no code implementations • 26 Feb 2022 • 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.
no code implementations • NeurIPS 2021 • Matthew McLeod, Chunlok Lo, Matthew Schlegel, Andrew Jacobsen, Raksha Kumaraswamy, Martha White, Adam White
Learning auxiliary tasks, such as multiple predictions about the world, can provide many benefits to reinforcement learning systems.
no code implementations • 10 May 2021 • Andrew Jacobsen, Alan Chan
In parallel, progress in online learning has provided parameter-free methods that achieve minimax optimal guarantees up to logarithmic terms, but their application in reinforcement learning has yet to be explored.
no code implementations • 17 Jul 2019 • Andrew Jacobsen, Matthew Schlegel, Cameron Linke, Thomas Degris, Adam White, Martha White
This paper investigates different vector step-size adaptation approaches for non-stationary online, continual prediction problems.
no code implementations • 18 Jul 2018 • Matthew Schlegel, Andrew Jacobsen, Zaheer Abbas, Andrew Patterson, Adam White, Martha White
A general purpose strategy for state construction is to learn the state update using a Recurrent Neural Network (RNN), which updates the internal state using the current internal state and the most recent observation.