On the Equivalence between Online and Private Learnability beyond Binary Classification
Authors: Young Jung, Baekjin Kim, Ambuj Tewari
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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 yhjung@umich.edu Baekjin Kim Department of Statistics University of Michigan Ann Arbor, MI 48109 baekjin@umich.edu Ambuj Tewari Department of Statistics University of Michigan Ann Arbor, MI 48109 tewaria@umich.edu |
| 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. |