Regret of Queueing Bandits
Authors: Subhashini Krishnasamy, Rajat Sen, Ramesh Johari, Sanjay Shakkottai
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we present simulation results of various queueing bandit systems with K servers. These results corroborate our theoretical analysis in Sections 4 and 5. |
| Researcher Affiliation | Academia | Subhashini Krishnasamy University of Texas at Austin Rajat Sen University of Texas at Austin Ramesh Johari Stanford University Sanjay Shakkottai University of Texas at Austin |
| Pseudocode | Yes | Details of the algorithm are given in Algorithm 1 in the Appendix. |
| Open Source Code | No | The paper does not provide any concrete statement about the availability of source code for the methodology described, nor does it include a link to a code repository. |
| Open Datasets | No | The paper describes a simulated queueing system with generated arrivals and service according to Bernoulli distributions based on specified parameters (K, lambda, mu), rather than using a publicly available dataset. |
| Dataset Splits | No | The paper describes a simulated queueing system and evaluates its performance over time 't', but it does not involve traditional dataset splits (e.g., train/validation/test) in its experimental setup. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, or cloud computing instances) used to run the simulations or experiments. |
| Software Dependencies | No | The paper describes the Q-Th S algorithm (Algorithm 1) and its theoretical analysis and simulation results, but it does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | Figure 1 shows the evolution of queue-regret with time in a system with K = 5, = 0.1 and = 0.17. ... At time-slot t, Q-Th S decides to explore with probability min{1, 3K log2 t/t}, otherwise it exploits. |