The Power of Adaptivity in Identifying Statistical Alternatives
Authors: Kevin G. Jamieson, Daniel Haas, Benjamin Recht
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | 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 {kjamieson,dhaas,brecht}@eecs.berkeley.edu |
| 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. |