Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Conditional Meta-Learning of Linear Representations
Authors: Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto
NeurIPS 2022 | Venue PDF | 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) EMAIL Massimiliano Pontil Istituto Italiano di Tecnologia (Italy) & University College of London (UK) EMAIL Carlo Ciliberto University College of London (UK) & Istituto Italiano di Tecnologia (Italy) EMAIL |
| 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. |