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 [1].
Consistency of weighted majority votes
Authors: Daniel Berend, Aryeh Kontorovich
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We revisit from a statistical learning perspective the classical decision-theoretic problem of weighted expert voting. In particular, we examine the consistency (both asymptotic and ๏ฌnitary) of the optimal Nitzan-Paroush weighted majority and related rules. In the case of known expert competence levels, we give sharp error estimates for the optimal rule. When the competence levels are unknown, they must be empirically estimated. We provide frequentist and Bayesian analyses for this situation. Some of our proof techniques are non-standard and may be of independent interest. The bounds we derive are nearly optimal, and several challenging open problems are posed. |
| Researcher Affiliation | Academia | Daniel Berend Computer Science Department and Mathematics Department Ben Gurion University Beer Sheva, Israel EMAIL Aryeh Kontorovich Computer Science Department Ben Gurion University Beer Sheva, Israel EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using a specific dataset, nor does it mention dataset availability, citations, or splits for training purposes. |
| Dataset Splits | No | The paper is theoretical and does not mention dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running experiments, as it is a theoretical paper without empirical experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, such as hyperparameters or training configurations. |