Conformalization of Sparse Generalized Linear Models
Authors: Etash Kumar Guha, Eugene Ndiaye, Xiaoming Huo
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show how our path-following algorithm accurately approximates conformal prediction sets and illustrate its performance using synthetic and real data examples. and 6. Numerical Experiments Our central claim is twofold. Our method efficiently and accurately generates the homotopy over general loss functions. Our method also efficiently and accurately generates conformal sets over general loss functions. We demonstrate these two claims over different datasets and loss functions. |
| Researcher Affiliation | Collaboration | 1College of Computing, Georgia Institute of Technology, Atlanta, GA, USA 2 Apple (Work partly done while at Georgia Tech) 3H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA. |
| Pseudocode | Yes | Algorithm 1 Full Homotopy Generation and Algorithm 2 Conformal Set Generation |
| Open Source Code | Yes | For reproducibility, our implementation is at github.com/Etash Guha/sparse_conformal. |
| Open Datasets | Yes | The first three are real datasets sourced from (Pedregosa et al., 2011). The Diabetes dataset is a regression dataset with 20 features and 442 samples. Additionally, we use the well-known regression dataset from (H., 1991) denoted as Friedman1, which has 10 features and 100 samples. We also use the multivariate dataset denoted Friedman2 from (Breiman, 1996), which has 100 samples and 4 features. |
| Dataset Splits | No | The paper describes the datasets used (Diabetes, Friedman1, Friedman2, Synthetic) but does not provide explicit details on how these datasets were split into training, validation, and test sets, nor does it mention cross-validation strategies or predefined splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Proximal Gradient Descent for Lasso Loss and CVXPY for Robust and Asymmetric as Primal Correctors' and cites SKGLM, but it does not provide specific version numbers for any software libraries or dependencies. |
| Experiment Setup | No | The paper mentions 'For our experiments, we used α = 0.1' but does not provide a comprehensive set of specific experimental setup details such as learning rates, batch sizes, optimizer settings, or other hyperparameters needed for full reproducibility. |