Finding All $\epsilon$-Good Arms in Stochastic Bandits
Authors: Blake Mason, Lalit Jain, Ardhendu Tripathy, Robert Nowak
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We introduce two algorithms to overcome these and demonstrate their great empirical performance on a large-scale crowd-sourced dataset of 2.2M ratings collected by the New Yorker Caption Contest as well as a dataset testing hundreds of possible cancer drugs. |
| Researcher Affiliation | Academia | Blake Mason University of Wisconsin Madison, WI 53706 bmason3@wisc.edu Lalit Jain University of Washington Seattle, WA 98115 lalitj@uw.edu Ardhendu Tripathy University of Wisconsin Madison, WI 53706 astripathy@wisc.edu Robert Nowak University of Wisconsin Madison, WI 53706 rdnowak@wisc.edu |
| Pseudocode | Yes | Algorithm 1 (ST)2: Sample the Threshold, Split the Threshold ... Algorithm 4.1: additive FAREAST with γ = 0 |
| Open Source Code | Yes | Implementations of all algorithms and baselines used in this paper are available on Git Hub. |
| Open Datasets | No | The paper mentions using a "large-scale crowd-sourced dataset of 2.2M ratings collected by the New Yorker Caption Contest" and a "dataset [27] of 189 inhibitors". While it cites a paper for the cancer drug dataset, it does not provide a direct link, DOI, or repository access information for either dataset. |
| Dataset Splits | No | The paper does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as CPU/GPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | In the first example on the left, δ = 0.1, = β = 0.05. ... for δ = 0.01... We set = 0.1 and focus on the multiplicative setting... In this experiment, we use the multiplicative case of ALLwith = 0.8 and δ = 0.001. |