Meta-Learning Hypothesis Spaces for Sequential Decision-making
Authors: Parnian Kassraie, Jonas Rothfuss, Andreas Krause
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also empirically evaluate the effectiveness of our approach on a Bayesian optimization task.In this section, we provide experiments to quantitatively illustrate our theoretical contribution. |
| Researcher Affiliation | Academia | 1ETH Zurich, Switzerland. Correspondence to: Parnian Kassraie <pkassraie@ethz.ch>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions data from the Open ML platform and the use of the CELER solver, but does not provide a link to the open-source code for the META-KEL methodology described. |
| Open Datasets | Yes | The Open ML platform (Bischl et al., 2017) enables access to data from hyper-parameter tuning of GLMNET on 38 different classification tasks. The hyper-parameter evaluations are available under a Creative Commons BY 4.0 license and can be downloaded here4. |
| Dataset Splits | Yes | We randomly split the available tasks (i.e. train/test evaluations on a specific dataset) into a set of meta-train and meta-test tasks. We split these datasets into a meta-dataset with m = 25 and leave the rest as test tasks. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'CELER, a fast solver for the group Lasso (Massias et al., 2018)' but does not provide a specific version number for it or other software dependencies. |
| Experiment Setup | Yes | We set p = 20 and s = |Jk | = 5. ... We add Gaussian noise with standard deviation σ = 0.01 to all data points. ... For all experiments we set n = m = 50 unless stated otherwise. ... We set λ = 0.03, such that it satisfies the condition of Theorem 4.3. |