Optimal Exploitation of Clustering and History Information in Multi-armed Bandit
Authors: Djallel Bouneffouf, Srinivasan Parthasarathy, Horst Samulowitz, Martin Wistuba
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on synthetic and real world datasets on Warafin drug dosage and web server selection for latency minimization validate our theoretical insights and demonstrate that META is a robust strategy for optimally exploiting the pre-clustering information. |
| Researcher Affiliation | Industry | Djallel Bouneffouf, Srinivasan Parthasarathy, Horst Samulowitz, Martin Wistuba IBM Research, Yorktown Heights, NY, USA {djallel.bouneffouf@, spartha@us., samulowitz@us., martin.wistuba@}ibm.com |
| Pseudocode | Yes | Algorithm 1 The HUCBC algorithm |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | The university web page data set3 features more than 700 sources with about 1300 response times each. 3https://sourceforge.net/projects/bandit/ |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We report the results of 20 trials in in Figure 1a. Each trial consisted of 104 rounds and we plot the the number of rounds per-round-reward (cumulative reward until that round / number of rounds). We report the results for ϵ = 0.1 in Figure 1b. As in the case of classical bandits, we repeat the experiment 20 times, where each trial consists of 10,000 rounds. We compare the performance of HLINUCBC vs. LINUCBC under different values of ϵ {0.1, 0.8, 3.2}. |