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..
Mutli-Armed Bandits with Network Interference
Authors: Abhineet Agarwal, Anish Agarwal, Lorenzo Masoero, Justin Whitehouse
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we corroborate our theoretical findings via numerical simulations. [...] 6 Simulations |
| Researcher Affiliation | Collaboration | Abhineet Agarwal Department of Statistics UC Berkeley EMAIL Anish Agarwal Department of IEOR Columbia University EMAIL Lorenzo Masoero Amazon EMAIL Justin Whitehouse Computer Science Department Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1 Network Explore-Then-Commit with Known Interference [...] Algorithm 2 Network Explore-Then-Commit with Unknown Interference |
| Open Source Code | Yes | Code for our methods and experiments can be found at https://github.com/aagarwal1996/Network MAB. |
| Open Datasets | No | The paper describes a 'Data Generating Process' for simulations but does not provide public access to the generated dataset. They create the data dynamically for their experiments. |
| Dataset Splits | Yes | For our Algorithms, we choose all hyper-parameters via 3-fold CV |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU/GPU models, memory). |
| Software Dependencies | No | The paper mentions using 'the scikit-learn implementation of the Lasso' but does not specify its version number or other software dependencies with versions. |
| Experiment Setup | Yes | Data Generating Process. We generate interference patterns with varying number of units N {5, . . . , 10}, and A = 2. For each N, we use s = 4. We generate rewards rn = θn, χ(a) , where the non-zero elements of θn (i.e., θn,S for S Bn) are drawn uniform from [0, 1]. We normalize rewards so that they are contained in [0, 1], and add 1 sub-gaussian noise to sampled rewards. [...] For our Algorithms, we choose all hyper-parameters via 3-fold CV, and use the scikit-learn implementation of the Lasso. [...] Algorithm 2 run with λ = 4 p E 1 log(2AN) + δ where E := (TAs)2/3 log Nδ + N log(A) 1/3 |