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..
Batched Dueling Bandits
Authors: Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we also validate our theoretical results via experiments on synthetic and real data. |
| Researcher Affiliation | Academia | Arpit Argarwal 1 Rohan Ghuge 2 Viswanath Nagarajan 2 1Data Science Institute, Columbia University, New York, USA 2Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, USA. |
| Pseudocode | Yes | Algorithm 1 PCOMP(ALL PAIRS COMPARISONS) [...] Algorithm 2 SCOMP(SEEDED COMPARISONS) [...] Algorithm 3 SCOMP2 (SEEDED COMPARISONS 2) |
| Open Source Code | No | The paper mentions using a 'dueling bandit library due to (Komiyama et al., 2015)' for comparison but does not state that the authors' own code or implementations are open-source or provide a link. |
| Open Datasets | Yes | Sushi. The Sushi dataset is based on the Sushi preference dataset (Kamishima, 2003) that contains the preference data regarding 100 types of Sushi. |
| Dataset Splits | No | The paper describes an online learning problem (dueling bandits) evaluated on cumulative regret over a time horizon. It does not mention traditional train/validation/test splits of a static dataset for model tuning or evaluation. |
| Hardware Specification | Yes | We conducted our computations using C++ and Python 2.7 with a 2.3 Ghz Intel Core i5 processor and 16 GB 2133 MHz LPDDR3 memory. |
| Software Dependencies | No | The paper mentions 'C++ and Python 2.7' and 'the dueling bandit library due to (Komiyama et al., 2015)' but does not provide specific version numbers for C++ compilers or the mentioned library, only for Python itself. |
| Experiment Setup | Yes | We set T = 10^5, δ = 1/TK^2 and B = log(T) = 16. We set α = 0.51 for RUCB, and f(K) = 0.3K^1.01 for RMED1, and γ = 1.3 for BTM. |