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..
Online Stochastic Optimization under Correlated Bandit Feedback
Authors: Mohammad Gheshlaghi azar, Alessandro Lazaric, Emma Brunskill
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | While our primary contribution is the de๏ฌnition of HCT and its technical analysis, we also give preliminary simulation results to demonstrate some of its properties. and 6. Numerical Results |
| Researcher Affiliation | Academia | Mohammad Gheshlaghi Azar EMAIL Rehabilitation Institute of Chicago, Northwestern University, Alessandro Lazaric EMAIL Team Seque L, INRIA Nord Europe, Emma Brunskill EMAIL School of Computer Science, CMU |
| Pseudocode | Yes | Algorithm 1 The HCT algorithm. and Algorithm 2 The Opt Traverse function. and Algorithm 3 The Update B function. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | We focus on minimizing the regret across repeated noisy evaluations of the garland function f(x) = x(1 x)(4 | sin(60x)|) relative to repeatedly selecting its global optima. We discuss some properties of the garland function in Sect. C of the supplement where the function is illustrated in Fig. 3. (The garland function is a synthetic function used for simulation, not a publicly available dataset.) |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or citations to predefined splits) needed to reproduce data partitioning for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | For all the algorithms compared in the following, parameters are optimized to maximize their performance. (This statement is too general and does not specify concrete hyperparameter values or detailed configurations.) |