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..
Thresholding Bandits with Augmented UCB
Authors: Subhojyoti Mukherjee, Naveen Kolar Purushothama, Nandan Sudarsanam, Balaraman Ravindran
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive simulation experiments to validate the performance of Aug UCB. |
| Researcher Affiliation | Academia | Subhojyoti Mukherjee1,Naveen Kolar Purushothama2,Nandan Sudarsanam3,Balaraman Ravindran4 1,4Department of Computer Science & Engineering, Indian Institute of Technology Madras 2Department of Electrical Engineering, Indian Institute of Technology Madras 3Department of Management Studies, Indian Institute of Technology Madras |
| Pseudocode | Yes | Algorithm 1 Aug UCB |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes simulation experiments where reward distributions are generated (e.g., 'Gaussian with means r1:4 = 0.2 + (0 : 3) 0.05'), but does not use or provide a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes simulation experiments for a multi-armed bandit problem, which does not involve traditional train/validation/test dataset splits. Performance is evaluated by tracking error percentage over time in repeated runs. |
| Hardware Specification | No | The paper describes simulation experiments but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run these simulations. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks with their versions) used for the experiments. |
| Experiment Setup | Yes | Across all experiments consists of K = 100 arms (indexed i = 1, 2, , 100) of which Sτ = {6, 7, , 10}, where we have fixed τ = 0.5. In all the experiments, each algorithm is run independently for 10000 time-steps. At every time-step, the output set, ˆSτ, suggested by each algorithm is recorded; the output is counted as an error if ˆSτ = Sτ. In Figure 1, for each experiment, we have reported the percentage of error incurred by the different algorithms as a function of time; Error percentage is obtained by repeating each experiment independently for 500 iterations, and then respectively computing the fraction of errors. The details of the considered experiments are as follows. Experiment-1: The reward distributions are Gaussian with means r1:4 = 0.2 + (0 : 3) 0.05, r5 = 0.45, r6 = 0.55, r7:10 = 0.65 + (0 : 3) 0.05 and r11:100 = 0.4. The corresponding variances are σ2 1:5 = 0.5 and σ2 6:10 = 0.6, while σ2 11:100 is chosen independently and uniform in the interval [0.38, 0.42]. |