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].
On the Equivalence between Online and Private Learnability beyond Binary Classification
Authors: Young Jung, Baekjin Kim, Ambuj Tewari
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | As this paper is purely theoretical, discussing broader impact is not applicable. |
| Researcher Affiliation | Academia | Young Hun Jung Department of Statistics University of Michigan Ann Arbor, MI 48109 EMAIL Baekjin Kim Department of Statistics University of Michigan Ann Arbor, MI 48109 EMAIL Ambuj Tewari Department of Statistics University of Michigan Ann Arbor, MI 48109 EMAIL |
| Pseudocode | Yes | Algorithm 1 Standard optimal algorithm with tolerance τ (SOAτ) [...] Algorithm 2 COLORANDCHOOSE |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | This is a purely theoretical paper and does not involve the use of datasets for training or experimentation. |
| Dataset Splits | No | This is a purely theoretical paper and does not involve the use of datasets or their splits for validation. |
| Hardware Specification | No | This is a purely theoretical paper and does not report on experimental hardware specifications. |
| Software Dependencies | No | This is a purely theoretical paper and does not mention specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | This is a purely theoretical paper and does not describe an experimental setup with hyperparameters or training configurations. |