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 Lipschitz Bandits with Heavy-tailed Rewards
Authors: Shiyin Lu, Guanghui Wang, Yao Hu, Lijun Zhang
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct numerical experiments to demonstrate the effectiveness of our algorithms. In this section, we provide numerical experiments to illustrate the performance of our proposed algorithms: ADTM and ADMM. |
| Researcher Affiliation | Collaboration | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China 2You Ku Cognitive and Intelligent Lab, Alibaba Group, Beijing 100102, China. |
| Pseudocode | Yes | Algorithm 1 Static Discretization with Truncated Mean (SDTM); Algorithm 2 Adaptive Discretization with Truncated Mean (ADTM); Algorithm 3 Median of Means Estimator (MME); Algorithm 4 Adaptive Discretization with Median of Means (ADMM) |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code or provide a link to a code repository. |
| Open Datasets | No | The paper describes a synthetic experimental setup: "Following Magureanu et al. (2014), we set X = [0, 1] with D being the Euclidean metric on it, and choose µ(x) = a min(|x 0.4|, |x 0.8|) as the expected reward function". It does not use a publicly available or open dataset. |
| Dataset Splits | No | The paper uses a synthetic experimental setup and runs '40 independent repetitions'. It does not describe any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We consider two cases: a = 0 and a = 2. For each case, we run 40 independent repetitions and report the average cumulative regret of each tested algorithm in Figure 1. Finally, following common practice (Zhang et al., 2016; Jun et al., 2017), we scale the confidence radius by a factor c searched within [1e 2, 1]. |