Learning to Screen
Authors: Alon Cohen, Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Shay Moran
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We give near-optimal bounds on the best-possible number of retained items in each of these variants. These results demonstrate that one can retain exponentially less items in the second variant (with the training set). Our algorithms and analysis utilize ideas and techniques from statistical learning theory and from discrete algorithms. |
| Researcher Affiliation | Collaboration | Technion Israel Inst. of Technology and Google Research. aloncohen@technion.ac.il Bar-Ilan University and Google Research. avinatanh@gmail.com Tel-Aviv University and Google Research. haimk@tau.ac.il Tel-Aviv University and Google Research. mansour.yishay@gmail.com Princeton University. shaymoran1@gmail.com. This work was done while the author was working at Google Research. |
| Pseudocode | No | The paper mentions a 'greedy algorithm' and states 'The particular details of the algorithm are given in the supplementary material.', implying any pseudocode would be outside the provided text. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology or links to a code repository. |
| Open Datasets | No | The paper describes using items 'drawn independently from an unknown distribution D' and a 'training set of n items drawn independently from the same unknown distribution', but it does not specify or provide access information for any publicly available or open dataset. |
| Dataset Splits | No | The paper mentions a 'training set' and 'real-time input sample' but does not provide specific details on dataset splits (e.g., percentages, sample counts, or defined methodologies for partitioning data). |
| Hardware Specification | No | As a theoretical paper, it does not describe any experiments that would require specific hardware, therefore no hardware specifications are provided. |
| Software Dependencies | No | As a theoretical paper, it does not describe experiments requiring specific software dependencies with version numbers, and thus no such details are provided. |
| Experiment Setup | No | As a theoretical paper, it describes algorithms and their properties but does not provide specific experimental setup details such as hyperparameter values or training configurations. |