Conditional Meta-Learning of Linear Representations
Authors: Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments We now present preliminary experiments in which we compare the proposed conditional meta-learning approach in Alg. 1 (cond.) with the unconditional counterpart (uncond.) and solving the tasks independently (ITL, namely, running the inner algorithm separately across the tasks with the constant linear representation = Id 2 Sd +). |
| Researcher Affiliation | Collaboration | Giulia Denevi Leonardo Labs (Italy) giulia.denevi.ext@leonardo.com Massimiliano Pontil Istituto Italiano di Tecnologia (Italy) & University College of London (UK) massimiliano.pontil@iit.it Carlo Ciliberto University College of London (UK) & Istituto Italiano di Tecnologia (Italy) c.ciliberto@ucl.ac.uk |
| Pseudocode | Yes | Algorithm 1 Meta-algorithm, SGD on Eq. (15) |
| Open Source Code | Yes | We attached our code in the Supplementary material. |
| Open Datasets | Yes | We also tested the performance of the methods on the Movielens-100k and Jester-1 real-world datasets... We considered two variants of the setting described in Ex. 1 with side information corresponding to the training datasets Ztr associated to each task. ... on the regression problem on the computer survey data from [23] |
| Dataset Splits | Yes | The hyper-parameter γ was chosen by (meta-)cross validation on separate Ttr, Tva and Tte respectively meta-train, -validation and -test sets. |
| Hardware Specification | Yes | The experiments were run on a server with an Intel Xeon E3-1505M v5 CPU, 64 GB of RAM, and an NVIDIA Quadro M2000M GPU. |
| Software Dependencies | No | The paper does not specify particular software dependencies with version numbers (e.g., 'PyTorch 1.9' or 'TensorFlow 2.x'). It only generally mentions implementation in Python in Appendix E without further details. |
| Experiment Setup | Yes | The hyper-parameter γ was chosen by (meta-)cross validation on separate Ttr, Tva and Tte respectively meta-train, -validation and -test sets. For all experiments, we set n = 80, ntr = 40, nte = 40. The within-task learning rate (for the online algorithm in Eq. (3)) was set to η = 0.05 and the number of iterations to N = 1000. The batch size for the online algorithm was set to b = 1. |