Online-Within-Online Meta-Learning
Authors: Giulia Denevi, Dimitris Stamos, Carlo Ciliberto, Massimiliano Pontil
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, preliminary numerical experiments confirm our theoretical findings. |
| Researcher Affiliation | Academia | Giulia Denevi1,2, Dimitris Stamos3, Carlo Ciliberto3,4 and Massimiliano Pontil1,3 1Istituto Italiano di Tecnologia (Italy), 2University of Genoa (Italy), 3University College of London (UK),4Imperial College of London (UK) |
| Pseudocode | Yes | Algorithm 1 Primal-dual online algorithm online Mirror Descent; Algorithm 2 Within-task algorithm; Algorithm 3 Meta-algorithm |
| Open Source Code | Yes | The code is available at https://github.com/dstamos/Adversarial-LTL |
| Open Datasets | Yes | 1) the Movielens-100k dataset3, containing the ratings of different users to different movies 2) the Mini-Wiki dataset from [3], containing sentences from Wikipedia pages and 3) the Jester-1 dataset4, containing the ratings of different users to different jokes. (Footnotes 3 and 4 provide URLs for Movielens-100k and Jester-1, and [3] is a formal citation for Mini-Wiki). |
| Dataset Splits | Yes | In all experiments, the hyper-parameters λ and were chosen by a meta-validation procedure (see App. I for more details) ... We split the available data into a training, validation and test set, with percentages 80%, 10% and 10% respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | In all experiments, the hyper-parameters λ and were chosen by a meta-validation procedure (see App. I for more details) and we fixed 0 = I/d for the meta-algorithm in Alg. 8. (Appendix I further specifies: The inner algorithms were run with their default parameters, i.e. λ = 1 and n = 100 for the Movielens-100k dataset. The meta-parameters λ and were chosen by meta-validation, where λ was chosen from {0.1, 1, 10} and from {0.001, 0.01, 0.1, 1, 10}.) |