Rotting Bandits
Authors: Nir Levine, Koby Crammer, Shie Mannor
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present algorithms, accompanied by simulations, and derive theoretical guarantees. |
| Researcher Affiliation | Academia | Nir Levine Electrical Engineering Department The Technion Haifa 32000, Israel levin.nir1@gmail.com Koby Crammer Electrical Engineering Department The Technion Haifa 32000, Israel koby@ee.technion.ac.il Shie Mannor Electrical Engineering Department The Technion Haifa 32000, Israel shie@ee.technion.ac.il |
| Pseudocode | Yes | Pseudo algorithm for SWA is given by Algorithm 1. |
| Open Source Code | No | The paper does not provide any specific links to source code repositories or explicit statements about the release of code for the described methodology. |
| Open Datasets | No | Setups for all the simulations we use Normal distributions with σ2 = 0.2, and T = 30, 000. Non-Parametric: K = 2. As for the expected rewards: µ1 (n) = 0.5, n, and µ2 (n) = 1 for its first 7, 500 pulls and 0.4 afterwards. |
| Dataset Splits | No | The paper conducts simulations by generating data based on defined reward distributions and parameters, rather than using pre-existing datasets with explicit training, validation, or test splits. |
| Hardware Specification | No | The paper describes simulation parameters and algorithmic details but does not specify any hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions implementing standard benchmark algorithms, but it does not provide specific software names along with their version numbers required for replication. |
| Experiment Setup | Yes | Setups for all the simulations we use Normal distributions with σ2 = 0.2, and T = 30, 000. Non-Parametric: K = 2. As for the expected rewards: µ1 (n) = 0.5, n, and µ2 (n) = 1 for its first 7, 500 pulls and 0.4 afterwards. Parametric AV & ANV: K = 10. The rotting models are of the form µ (j; θ) = int j / 100 + 1 θ, where int( ) is the lower rounded integer, and Θ = {0.1, 0.15, .., 0.4}. The parameters that were chosen by the grid search are as follows: γ = 0.999 for the non-parametric case, and 0.999999 for the parametric cases. τ = 4e3, 8e3, and 16e3 for the nonparametric, AV, and ANV cases, respectively. α = 0.2 was chosen for all cases. |