On the Value of Target Data in Transfer Learning
Authors: Steve Hanneke, Samory Kpotufe
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Finally, the procedures described in this work remain of a theoretical nature, but yield new insights into how various practical decisions in transfer can be made near-optimally in a data-driven fashion. |
| Researcher Affiliation | Academia | Steve Hanneke Toyota Technological Institute at Chicago steve.hanneke@gmail.com Samory Kpotufe Columbia University, Statistics skk2175@columbia.edu |
| Pseudocode | Yes | Algorithm 1: Minimize ˆRSP (h) subject to ˆRSQ(h) ˆRSQ(ˆh SQ) c ˆPSQ(h 6= ˆh SQ)An Q + c An Q (6) |
| Open Source Code | No | The paper describes theoretical algorithms and does not provide any links or statements about the availability of source code for these algorithms. |
| Open Datasets | No | The paper is theoretical and does not mention using or providing access to any specific datasets for training or other empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, therefore no training, validation, or test dataset splits are specified. |
| Hardware Specification | No | The paper does not conduct experiments and therefore does not provide any details about hardware specifications. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers for reproducibility, as it focuses on theoretical contributions. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, such as hyperparameters or training configurations. |