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..
Instance-Sensitive Algorithms for Pure Exploration in Multinomial Logit Bandit
Authors: Nikolai Karpov, Qin Zhang7096-7103
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we give efficient algorithms for pure exploration in MNL-bandit. Our algorithms achieve instancesensitive pull complexities. We also complement the upper bounds by an almost matching lower bound. |
| Researcher Affiliation | Academia | Indiana University Bloomington Luddy Hall, RM 3044 700 North Woodlawn Avenue Bloomington, IN 47408-3901, USA EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: EXPLORE(i) ... Algorithm 2: PRUNE(I, K, a, b) ... Algorithm 3: The Fixed Confidence Algorithm for MNL-Bandit ... Algorithm 4: EXPLORESET(S) ... Algorithm 5: Improved Fixed Confidence Algorithm for MNL-bandit |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on a dataset, thus it does not mention a training dataset. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments on a dataset, thus it does not mention training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithms and complexity, without discussing the hardware used for any computational tasks. |
| Software Dependencies | No | The paper is theoretical and describes algorithms, but it does not mention any specific software dependencies or version numbers. |
| Experiment Setup | No | The paper describes theoretical algorithms and their complexities but does not detail an experimental setup, as no empirical experiments are reported. |