Efficient Online Learning for Dynamic k-Clustering
Authors: Dimitris Fotakis, Georgios Piliouras, Stratis Skoulakis
ICML 2021 | Conference PDF | Archive PDF | Plain Text | 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. |