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..
Parameter-free, Dynamic, and Strongly-Adaptive Online Learning
Authors: Ashok Cutkosky
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a new online learning algorithm that for the first time combines several disparate notions of adaptivity. First, our algorithm obtains a parameter-free regret bound that adapts to the norm of the comparator and the squared norm of the size of the gradients it observes. Second, it obtains a strongly-adaptive regret bound, so that for any given interval of length N, the regret over the interval is O( N). Finally, our algorithm obtains an optimal dynamic regret bound: for any sequence of comparators with path-length P, our algorithm obtains regret O( PN) over intervals of length N. Our primary technique for achieving these goals is a new method of combining constrained online learning regret bounds that does not rely on an expert meta-algorithm to aggregate learners. |
| Researcher Affiliation | Collaboration | 1Google Research 2Boston University, Boston Massachusetts, USA. |
| Pseudocode | Yes | Algorithm 1 Varying Constraints; Algorithm 2 Residual Algorithm |
| Open Source Code | No | The paper does not provide any statement or link regarding open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not mention specific datasets or their public availability for training. |
| Dataset Splits | No | The paper is theoretical and does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setup details such as hyperparameters or training configurations. |