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..
Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards
Authors: Anmol Kagrecha, Jayakrishnan Nair, Krishna Jagannathan
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 5 Empirical Evaluations We conduct extensive numerical experiments on both synthetic and real-world datasets to evaluate the performance of the proposed algorithms and compare them with state-of-the-art baselines. |
| Researcher Affiliation | Academia | Liang-Ching Lin, Shi-Cho Cha, Pratik Kumar, Siva Theja Maguluri, Harsha Honnappa, Siva Kumar Sastry University of Maryland, Georgia Institute of Technology, Carnegie Mellon University, University of Minnesota |
| Pseudocode | Yes | Algorithm 1: CORRAL (Confidence Region based Online Risk-Aware Learning) |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | We use the Yahoo! Today Module dataset, which contains click-through rates (CTR) for various news articles over a period of 10 days. |
| Dataset Splits | No | The paper describes the generation of synthetic datasets and the characteristics of the real-world Yahoo! Today Module dataset, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts) needed for reproduction in a traditional sense. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | For the numerical experiments, the parameters are chosen as τ = 0.5, ρ = 0.1, λ = 0.05, c1 = 1, and c2 = 1. The confidence level is set to δ = 0.01. |