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..
The Power of Adaptivity in Identifying Statistical Alternatives
Authors: Kevin G. Jamieson, Daniel Haas, Benjamin Recht
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper studies the trade-off between two different kinds of pure exploration: breadth versus depth. We focus on the most biased coin problem, asking how many total coin flips are required to identify a heavy coin from an infinite bag containing both heavy coins with mean µ1 ∈ (0, 1), and light" coins with mean µ0 ∈ (0, 1), where heavy coins are drawn from the bag with proportion λ ∈ (0, 1/2). |
| Researcher Affiliation | Academia | Kevin Jamieson, Daniel Haas, Ben Recht University of California, Berkeley Berkeley, CA 94720 EMAIL |
| Pseudocode | Yes | Algorithm 1 The most biased coin problem definition. Algorithm 2 Adaptive strategy for heavy distribution identification with inputs µ0, 0, δ Algorithm 3 Adaptive strategy for heavy distribution identification with unknown parameters |
| Open Source Code | No | The paper does not contain any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not describe experiments run on datasets, thus it does not mention publicly available training datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments run on datasets, thus it does not provide details on training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |