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..
MARS: Meta-learning as Score Matching in the Function Space
Authors: Krunoslav Lehman Pavasovic, Jonas Rothfuss, Andreas Krause
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we demonstrate that our proposed approach, called Meta-learning via Attention-based Regularised Score estimation (MARS), consistently outperforms previous meta-learners in predictive accuracy and yields significant improvements in the quality of uncertainty estimates. 6 EXPERIMENTS |
| Researcher Affiliation | Academia | Krunoslav Lehman Pavasovic ETH Zurich Switzerland EMAIL Jonas Rothfuss ETH Zurich Switzerland EMAIL Andreas Krause ETH Zurich Switzerland EMAIL |
| Pseudocode | Yes | Algorithm 1 MARS: Meta-Learning the Data-Generating Process Score |
| Open Source Code | Yes | The code scripts for reproducing the experimental results are provided in our repository2. 2https://github.com/krunolp/mars |
| Open Datasets | Yes | Swiss FEL (Milne et al., 2017), Physio Net (Silva et al., 2012), Intel Berkeley Lab temperature sensor dataset (Berkeley-Sensor) (Madden, 2004) |
| Dataset Splits | Yes | From the remaining patients, we used 100 patients for meta-training and 500 patients for meta-validation and meta-testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like Jax (Bradbury et al., 2018), Haiku (Hennigan et al., 2020), GP-Jax (Pinder & Dodd, 2022), scikit-learn (Pedregosa et al., 2011), and TensorFlow Distributions (Dillon et al., 2017), but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We fix the number of particles to L = 10 and perform 10000 f SVGD update steps. |