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..
Meta-Learning Hypothesis Spaces for Sequential Decision-making
Authors: Parnian Kassraie, Jonas Rothfuss, Andreas Krause
ICML 2022 | Venue PDF | 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 <EMAIL>. |
| 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. |