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 [1].
SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression
Authors: Steve Yadlowsky, Taedong Yun, Cory Y McLean, Alexander D'Amour
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate bηSLOE in simulated data, where ground truth parameter values are known. |
| Researcher Affiliation | Industry | Steve Yadlowsky EMAIL Taedong Yun EMAIL Cory Mc Lean EMAIL Alexander D Amour EMAIL Google Research, Brain Team Google Health |
| Pseudocode | No | The paper describes the mathematical derivation and approximation for SLOE but does not include a formal pseudocode or algorithm block. |
| Open Source Code | Yes | Code in Supplement |
| Open Datasets | Yes | The Heart Disease dataset (downloadable from the UCI Machine Learning Repository)... 136 training examples and 20 predictors (κ = 0.15). |
| Dataset Splits | No | The paper does not explicitly specify a separate 'validation' dataset split for hyperparameter tuning or model selection. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running experiments. |
| Software Dependencies | Yes | We implemented this estimator in Python, using scikit-learn to perform the MLE, and numpy / scipy [Harris et al., 2020, Virtanen et al., 2020] for the high dimensional adjustment and inference. |
| Experiment Setup | Yes | In our simulations, we use a data generating process parameterized by the sample size n, the aspect ratio κ, and signal strength γ2. We show the results of these coverage experiments, with n = 4000. |