Unconstrained Online Learning with Unbounded Losses
Authors: Andrew Jacobsen, Ashok Cutkosky
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | For this setting we provide an algorithm which guarantees RT (u) e O(G u T) regret on any problem where the subgradients satisfy gt G+L wt , and show that this bound is unimprovable without further assumptions. We leverage this algorithm to develop new saddle-point optimization algorithms that converge in duality gap in unbounded domains, even in the absence of meaningful curvature. Finally, we provide the first algorithm achieving non-trivial dynamic regret in an unbounded domain for non-Lipschitz losses, as well as a matching lower bound. |
| Researcher Affiliation | Academia | Andrew Jacobsen 1 2 Ashok Cutkosky 3 1Department of Computing Science, University of Alberta, Edmonton, Canada 2Alberta Machine Intelligence Institute (Amii), Edmonton, Canada 3Department of Electrical and Computer Engineering, Boston University, Boston, Massachussetts. Correspondence to: Andrew Jacobsen <ajjacobs@ualberta.ca>. |
| Pseudocode | Yes | Algorithm 1 Algorithm for Quadratically Bounded Losses, Algorithm 2 Saddle-point Reduction, Algorithm 3 Dynamic Regret Algorithm, Algorithm 4 Centered Mirror Descent with Adjustment, Algorithm 5 Multi-scale Fixed-share |
| Open Source Code | No | No statement about open-source code release or links to repositories found in the paper. |
| Open Datasets | No | The paper is theoretical and does not involve specific datasets or empirical training. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments or dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details on experimental setup or hyperparameters. |