Discovering, Learning and Exploiting Relevance
Authors: Cem Tekin, Mihaela van der Schaar
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Due to the limited space, numerical results on the performance of our proposed algorithm is included in the supplementary material. |
| Researcher Affiliation | Academia | Cem Tekin Electrical Engineering Department University of California Los Angeles cmtkn@ucla.edu Mihaela van der Schaar Electrical Engineering Department University of California Los Angeles mihaela@ee.ucla.edu |
| Pseudocode | Yes | The pseudocode of ORL-CF is given in Fig. 2. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper defines context spaces (e.g., 'Xi = [0, 1]') but does not mention specific datasets, their availability, or provide any links or citations to public datasets used for training. |
| Dataset Splits | No | The paper does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers. |
| Experiment Setup | No | The paper describes the algorithm's input parameters (L, ρ, δ) but does not provide specific hyperparameter values or detailed system-level training settings for experimental reproduction. |