Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Dueling Bandits with Adversarial Sleeping
Authors: Aadirupa Saha, Pierre Gaillard
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our results are corroborated empirically. |
| Researcher Affiliation | Collaboration | Microsoft Research, New York, US; EMAIL. Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France. EMAIL |
| Pseudocode | Yes | Algorithm 1 Sl DB-UCB |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code, nor does it include a link to a code repository. |
| Open Datasets | No | We use the following three different utility based Plackett Luce(θ) preference models (see Sec. 2) that ensures a total-ordering. We now construct three types of problem instances 1. Easy 2. Medium 3. Hard, for any given K, such that items with their respective θ parameters are assigned as follows: 1. Easy: θ(1 : K/2 ) = 1, θ( K/2 + 1 : K) = 0.5. 2. Medium: θ(1 : K/3 ) = 1, θ( K/3 + 1 : 2K/3 ) = 0.7, θ( 2K/3 + 1 : K) = 0.4. 3. Hard: θ(i) = 1 (i 1)/K, i [K]. |
| Dataset Splits | No | The paper describes an online learning framework where data is generated sequentially, and does not discuss traditional training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers for the experimental setup. |
| Experiment Setup | Yes | In every experiment, we set the learning parameters α = 0.51, δ = 1/T for Sl DB-UCB (Alg. 1) and as per Thm. 6 for Sl DB-ED (Alg. 2). All results are averaged over 50 runs. |