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..
Multi-Player Bandits: The Adversarial Case
Authors: Pragnya Alatur, Kfir Y. Levy, Andreas Krause
JMLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We consider a setting where multiple players sequentially choose among a common set of actions (arms). ... In this work, we design the first multi-player Bandit algorithm that provably works in arbitrarily changing environments... We run experiments with three different setups and compare the performance of C&P to the Musical Chairs algorithm (MC) from Rosenski et al. (2016). ... For each setup, we create a plot that shows the average regret and the standard deviation (as a colored region around the average). |
| Researcher Affiliation | Academia | Kfir Y. Levy EMAIL Faculty of Electrical Engineering Technion Israel Institute of Technology Haifa, 3200003, Israel Andreas Krause EMAIL Department of Computer Science ETH Zurich 8092 Zürich, Switzerland |
| Pseudocode | Yes | Algorithm 1 K-Metaplayer algorithm (Input: η) Algorithm 2 C&P Ranking Algorithm 3 C&P Coordinator algorithm Algorithm 4 C&P Follower algorithm Algorithm 5 Sampling from a K-DPP (based on Algorithm 1 from Kulesza and Taskar (2012)) Algorithm 6 Sampling K eigenvectors (Algorithm 8 in Kulesza and Taskar (2012)) Algorithm 7 Sub-algorithm for Alg. 6: Computing elementary symmetric polynomials (Algorithm 7 in Kulesza and Taskar (2012)) |
| Open Source Code | No | The paper does not contain any explicit statements about code availability, such as a link to a repository, a mention of supplementary materials containing code, or phrases indicating code release. |
| Open Datasets | No | We choose N mean losses in [0,1] u.a.r. with a gap of at least 0.05 between the Kth and (K + 1)-th best arms. For each arm, the losses are sampled i.i.d. from a Bernoulli distribution with the selected means. |
| Dataset Splits | No | The paper describes generating synthetic data for its experiments but does not mention any training/test/validation splits. The data is generated on the fly based on specified parameters for the Bernoulli distribution. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU or CPU models, or memory specifications. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, frameworks, or solvers). |
| Experiment Setup | Yes | For all experiments, we set N = 8, K = 4, T = 240000, TR = 20 and T0 = 3000. ... We initially set the mean loss µi for each arm i as follows: µ1 = µ2 = µ3 = µ4 = 0.1 and µ5 = µ6 = µ7 = µ8 = 0.3. Each arm i s losses are sampled i.i.d. from Bernoulli distribution Ber(µi). At time T/4, link" (arm) 1 fails and its remaining losses are sampled i.i.d. from Ber(0.9). |