Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Unconstrained Online Learning with Unbounded Losses
Authors: Andrew Jacobsen, Ashok Cutkosky
ICML 2023 | Venue PDF | 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 ο¬rst 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 <EMAIL>. |
| 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. |