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..
Efficient Online Learning for Dynamic k-Clustering
Authors: Dimitris Fotakis, Georgios Piliouras, Stratis Skoulakis
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 6, we present the results of an experimental evaluation, indicating that for client locations generated in a variety of natural and practically relevant ways, the realized regret of the proposed algorithms is way smaller than Θ (min(k, r)). |
| Researcher Affiliation | Academia | 1Departement of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece 2Pillar of Engineering Systems and Design, Singapore University of Technology and Design, Singapore. |
| Pseudocode | Yes | Algorithm 1 A time-efficient algorithm for solving the dual program of Lemma 2; Algorithm 2 A no-regret algorithm for Fractional Dynamic k-Clustering; Algorithm 3 Deterministic Rounding Scheme; Algorithm 4 A Θ(k)-regret deterministic online learning algorithm for Dynamic k-Clustering; Algorithm 5 A Θ(r)-regret randomized online learning algorithm |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper describes client data as 'randomly generated according to a time-varying uniform distribution' and '20 clients arrive according to several static or time-varying two-dimensional probability distributions', implying synthetic data generated by the authors, without providing access information to a pre-existing public dataset. |
| Dataset Splits | No | The paper does not provide explicit training, validation, or test dataset splits. It discusses evaluating 'time-average connection cost' over rounds. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for the experiments, such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers used in the experiments. It mentions 'Python' in relation to examples, but no specific libraries or versions. |
| Experiment Setup | Yes | In the following simulations we select ϵ = 0.1 and track the ratio between the time-average cost of Algorithm 4 and of Algorithm 2 which acts as an upper bound on the regret. |