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..
No-Regret Algorithms for Heavy-Tailed Linear Bandits
Authors: Andres Munoz Medina, Scott Yang
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also present empirical results showing that our algorithms achieve a better performance than the current state of the art for bounded noise when the L1 bound on the noise is large yet the 1+ moment of the noise is small. 6. Experiments We now present empirical results showing that the truncation algorithm benefits from a better regret than the vanilla linear bandit algorithm of (Abbasi-Yadkori et al., 2011). |
| Researcher Affiliation | Collaboration | Andres Munoz Medina EMAIL Google Research, 111 8th Av, New York, NY 10011 Scott Yang EMAIL Courant Institute, 251 Mercer Street, New York, NY 10012 |
| Pseudocode | Yes | Algorithm 1 Confidence Region, Algorithm 2 Estimate by Truncation, Algorithm 3 Mini-Batch Confidence Region, Algorithm 4 Median of Means (Mo M) |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or include a link to a code repository. |
| Open Datasets | No | The paper describes generating synthetic data for experiments ('Our experimental setup is as follows: we let d = 50 and µ = 1 pn1 2 Rn...'), but it does not use a publicly available or open dataset with access information (link, DOI, citation). |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits. It describes a simulation setting with T=10^6 iterations and 20 replicas, but no explicit data partitioning. |
| Hardware Specification | No | The paper describes the parameters of the experimental setup and data generation, but it does not provide any specific hardware details such as CPU/GPU models, memory, or cloud resources used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., specific programming languages, libraries, or frameworks and their versions). |
| Experiment Setup | Yes | Our experimental setup is as follows: we let d = 50 and µ = 1 pn1 2 Rn, where 1 is a vector with all entries set to 1. For every x 2 B1 the reward function is given by x 7! µ>x + , where is a random variable taking values γ with probability 1 γ2 and 1 γ with probability γ2 where γ = 1 p 40T . Figure 1(a) shows the mean regret over 20 replicas of the same experiment, ... for T = 10^6. |