Extreme bandits
Authors: Alexandra Carpentier, Michal Valko
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We propose the EXTREMEHUNTER algorithm, provide its analysis, and evaluate it empirically on synthetic and real-world experiments. |
| Researcher Affiliation | Academia | Alexandra Carpentier Statistical Laboratory, CMS University of Cambridge, UK a.carpentier@statslab.cam.ac.uk Michal Valko Seque L team INRIA Lille Nord Europe, France michal.valko@inria.fr |
| Pseudocode | Yes | Algorithm 1 EXTREMEHUNTER |
| Open Source Code | No | The paper does not provide any links to source code or explicitly state that source code is made available. |
| Open Datasets | Yes | Computer Network Traffic Data In this experiment, we evaluate EXTREMEHUNTER on heavytailed network traffic data which was collected from user laptops in the enterprise environment [2]. [2] John Mark Agosta, Jaideep Chandrashekar, Mark Crovella, Nina Taft, and Daniel Ting. Mixture models of endhost network traffic. In IEEE Proceedings of INFOCOM,, |
| Dataset Splits | No | The paper does not specify explicit training, validation, or test dataset splits. It mentions using synthetic data and real-world data, and running simulations, but no explicit split percentages or counts. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU, GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | Yes | In the light of Remark 2, we set b = 1 to consider a wide class of distributions. Figure 1 (left) displays the averaged result of 1000 simulations with the time horizon T = 104. |