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..
Reward Maximization Under Uncertainty: Leveraging Side-Observations on Networks
Authors: Swapna Buccapatnam, Fang Liu, Atilla Eryilmaz, Ness B. Shroff
JMLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we use numerical examples on a real-world social network and a routing example network to demonstrate the benefits obtained by our policies over other existing policies. Keywords: Multi-armed Bandits, Side Observations, Bipartite Graph, Regret Bounds |
| Researcher Affiliation | Collaboration | Swapna Buccapatnam EMAIL AT&T Labs Research, Middletown, NJ 07748, USA Fang Liu EMAIL Atilla Eryilmaz EMAIL Department of Electrical and Computer Engineering The Ohio State University Columbus, OH 43210, USA |
| Pseudocode | Yes | Algorithm 1 : ϵt-greedy-LP Algorithm 2 : UCB-LP policy |
| Open Source Code | No | The paper does not provide an explicit statement about code release or a link to a code repository. The license mentioned is for the paper itself, not the methodology's implementation. |
| Open Datasets | Yes | We consider the Flixster network dataset for the numerical evaluation of our algorithms. The authors in Jamali and Ester (2010) collected this social network data, which contains about 1 million users and 14 million links. |
| Dataset Splits | Yes | We use graph clustering by Dhillon et al. (2007) to identify two strongly clustered sub-networks of sizes 1000 and 2000 nodes. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. It only mentions 'simulation' and 'numerical evaluation'. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiments. |
| Experiment Setup | Yes | For the ϵt-greedy-LP policy, we let c = 5 and d = 0.2 (we choose d = 0.2 to show that our algorithm seems to have good performance in more general settings, even when the bounds in the Proposition 7 are not known or used). For both networks, we see that our policies outperform the UCB-N and UCB-Max N policies Caron et al. (2012). For the ϵt-greedy-LP policy, we let c = 4 and d = 0.05. |