This is the repository for the NeurIPS 2021 paper "Fast Pure Exploration via Frank-Wolfe" by Po-An Wang, Ruo-Chun Tzeng and Alexandre Proutiere.

• utilities/envelope.jl contains the key functions for our sampling rules and introduces a generic way for the objective function and its sub-gradient (i.e., f, ∇f in our code) and the generalized log-likelihood ratio (i.e., alt_min and glrt in our utilities/peps.jl) for the active learning problems under various structures.

Julia with version 1.5.4.

• LinearAlgebra, Distributions, Statistics, Random
• JuMP, Tulip
• Distributed, JLD2
• Plots, StatsPlots, CPUTime, Printf, LaTeXStrings, IterTools

• Classical Best-Arm Identification
• Linear Best-Arm Identification, Linear Threshold
• Lipschitz Best-Arm Identification

Please go to the corresponding folder, e.g., standard, linear, or lipschitz and then execute the following commands:

• For Best Arm Identification problem, execute, e.g., julia -O3 -p8 experiment_bai1.jl for parallel computing with 8 processes to speeding-up the computation.
• For Threshold Bandit problem, the command is similar as above, just replace the filename with, e.g., experiment_threshold.jl.
• After completing the experiments, the performance statistics are saved in the .dat file. You can visualize the result by e.g., julia -O3 viz.jl BAI1.

Please note that except for linear/experiment_bai.jl, all other experiments support multiple confidence δs as input. The reason why linear/experiment_bai.jl cannot support multiple confidence δs is because of the stopping rule of XYAdaptive.