MARS: Meta-learning as Score Matching in the Function Space
Authors: Krunoslav Lehman Pavasovic, Jonas Rothfuss, Andreas Krause
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | 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 klehman@ethz.ch Jonas Rothfuss ETH Zurich Switzerland rojonas@ethz.ch Andreas Krause ETH Zurich Switzerland krausea@ethz.ch |
| 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. |