Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference
Authors: Luca Masserano, Alexander Shen, Michele Doro, Tommaso Dorigo, Rafael Izbicki, Ann B. Lee
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate its performance on two challenging scientific problems in biology and astroparticle physics with data from realistic mechanistic models. |
| Researcher Affiliation | Academia | 1Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, USA 2Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA 3Department of Physics and Astronomy, Universit a di Padova, Padova, Italy 4Istituto Nazionale di Fisica Nucleare, Sezione di Padova, Italy 5Lulea Techniska Universitet, Lulea, Sweden 6Universal Scientific Education and Research Network, Italy 7Department of Statistics, Universidade Federal de S ao Carlos, S ao Paulo, Brazil. |
| Pseudocode | Yes | Algorithm 1 Nuisance-aware prediction sets; Algorithm 2 Learning the Rejection Probability Function |
| Open Source Code | Yes | A flexible implementation of NAPS is available at https://github.com/lee-group-cmu/lf2i. |
| Open Datasets | Yes | We use data from the recently proposed sc Design3 simulator (Song et al., 2023), with reference data taken from the PBMC Systematic Comparative Analysis (Ding et al., 2019). |
| Dataset Splits | Yes | In total, we have available 80,000 samples which we divide into train (60%), calibration (35%) and test (5%) sets. |
| Hardware Specification | Yes | All computations were performed on a Mac Book Pro M1Pro with 16 GB of RAM. |
| Software Dependencies | No | The paper mentions using 'Cat Boost (Prokhorenkova et al., 2018)' and 'Sci Py (Virtanen et al., 2020)'. While SciPy 1.0 is implied by the citation, Cat Boost does not have a specific version number stated, and the question requires specific version numbers for multiple key components. |
| Experiment Setup | No | The paper describes the data splitting (e.g., 60% train, 35% calibration, 5% test) but does not provide specific hyperparameter values like learning rate, batch size, number of epochs, or optimizer settings used for training the models (e.g., CatBoost classifiers). |