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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
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.) |