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..
Nearly Optimal Regret for Stochastic Linear Bandits with Heavy-Tailed Payoffs
Authors: Bo Xue, Guanghui Wang, Yimu Wang, Lijun Zhang
IJCAI 2020 | 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 and the empirical results strongly support our theoretical guarantees. |
| Researcher Affiliation | Academia | National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China |
| Pseudocode | Yes | Algorithm 1 Basic algorithm through Median of Means (BMM); Algorithm 2 Basic algorithm through Truncation (BTC); Algorithm 3 Master Algorithm (Sup BMM and Sup BTC) |
| Open Source Code | No | The paper does not provide any statement about making its source code available or include a link to a code repository. |
| Open Datasets | No | The paper describes generating synthetic data for experiments ('Each element of the vector xt,a is sampled from the uniform distribution of [0, 1]') and adding different types of noise, but it does not use a publicly available or open dataset with access information. |
| Dataset Splits | No | The paper describes its experimental setup including noise types and parameter settings, but it does not specify any training/validation/test dataset splits or cross-validation methodology. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers for reproducing the experiments. |
| Experiment Setup | Yes | All algorithms parameters are set to ϵ = 1 and δ = 0.01. We adopt Mo M and CRT of Medina and Yang [2016], MENU and TOFU of Shao et al. [2018] as baselines for comparison. Let the feature dimension d = 10, the number of arms K = 20 and θ = 1/√d Rd, where 1 is an all-1 vector so that θ = 1. Each element of the vector xt,a is sampled from the uniform distribution of [0, 1], and then the vector is normalized to a unit vector ( xt,a = 1). According to the linear bandit model, the observed payoff is rt,a = x t,aθ + ηt where ηt is generated from the following two noises. (i) Student s t-Noise... (ii) Pareto Noise... We run 10 independent repetitions for each algorithm and display the average cumulative regret with time evolution. |