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..
Best-Arm Identification in Linear Bandits
Authors: Marta Soare, Alessandro Lazaric, Remi Munos
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the performance of XY-Adaptive and compare it to the XY-Oracle strategy (Eq. 5), the static allocations XY and G, as well as with the fully-adaptive version of XY where X is updated at each round and the bound from Prop.2 is used. For a fixed confidence δ = 0.05, we compare the sampling budget needed to identify the best arm with probability at least 1 δ. We consider a set of arms X Rd, with |X| = d + 1 including the canonical basis (e1, . . . , ed) and an additional arm xd+1 = [cos(ω) sin(ω) 0 . . . 0] . We choose θ = [2 0 0 . . . 0] , and fix ω = 0.01, so that Δmin = (x1 xd+1) θ is much smaller than the other gaps. In this setting, an efficient sampling strategy should focus on reducing the uncertainty in the direction y = (x1 xd+1) by pulling the arm x2 = e2 which is almost aligned with y. In fact, from the rewards obtained from x2 it is easier to decrease the uncertainty about the second component of θ , that is precisely the dimension which allows to discriminate between x1 and xd+1. Also, we fix α = 1/10, and the noise ε N(0, 1). Each phase begins with an initialization matrix A0, obtained by pulling once each canonical arm. In Fig. 4 we report the sampling budget of the algorithms, averaged over 100 runs, for d = 2 . . . 10. |
| Researcher Affiliation | Collaboration | Marta Soare Alessandro Lazaric Rémi Munos INRIA Lille Nord Europe, Seque L Team EMAIL This work was done when the author was a visiting researcher at Microsoft Research New-England. Current affiliation: Google Deep Mind. |
| Pseudocode | Yes | Figure 2: Static allocation algorithms Figure 3: XY-Adaptive allocation algorithm |
| Open Source Code | No | The paper provides a link to a technical report (http://arxiv.org/abs/1409.6110), but it does not contain an explicit statement about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes a synthetic setup for its experiments: 'We consider a set of arms X Rd, with |X| = d + 1 including the canonical basis (e1, . . . , ed) and an additional arm xd+1 = [cos(ω) sin(ω) 0 . . . 0] . We choose θ = [2 0 0 . . . 0] , and fix ω = 0.01, so that Δmin = (x1 xd+1) θ is much smaller than the other gaps.' This is a custom-generated environment rather than a publicly available or open dataset with provided access information. |
| Dataset Splits | No | The paper defines a synthetic experimental setup but does not specify train, validation, or test dataset splits (e.g., percentages or sample counts), nor does it reference predefined splits from standard benchmarks. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers required to reproduce the experiments. |
| Experiment Setup | Yes | For a fixed confidence δ = 0.05, we compare the sampling budget needed to identify the best arm with probability at least 1 δ. ... Also, we fix α = 1/10, and the noise ε N(0, 1). Each phase begins with an initialization matrix A0, obtained by pulling once each canonical arm. |