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..
Optimal Algorithms for Stochastic Contextual Preference Bandits
Authors: Aadirupa Saha
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section gives empirical performances of our algorithms (Alg. 1 and 3) and compare them with some existing preference learning algorithms. |
| Researcher Affiliation | Industry | Microsoft Research, New York, US; EMAIL. |
| Pseudocode | Yes | Algorithm 1 Maximum-Informative-Pair (Max In P) |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding open-source code for the described methodology. |
| Open Datasets | No | The paper describes synthetic problem instances and functions for g() (Quadratic, Six-Hump Camel, Gold Stein) which are generated for experiments, but does not provide specific access information (links, DOIs, formal citations) to a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, citations to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or memory) used for running the experiments are provided. |
| Software Dependencies | No | The paper mentions using techniques (e.g., GP fitting, kernelized self-sparring) and refers to existing works ([29], [37]) but does not provide specific version numbers for any software, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | For this experiment we fix d = 10 and K = 50. Fig. 2 shows both our algorithms Max In P and Sta D always outperform the rest... We use thsese 3 functions as g( ): 1. Quadratic, 2. Six-Hump Camel and 3. Gold Stein. For all cases, we fix d = 3 and K = 50. |