PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses
Authors: Adel Javanmard, Matthew Fahrbach, Vahab Mirrokni
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study Prior Boost through extensive experiments in Section 7. This includes a comparison with random bagging for linear and logistic regression tasks, as well as a careful exploration into label differential privacy with Laplace noise for different privacy budgets. |
| Researcher Affiliation | Collaboration | 1University of Southern California 2Google Research. |
| Pseudocode | Yes | We give pseudocode for Prior Boost in Algorithm 1 |
| Open Source Code | No | The paper does not provide an explicit statement or link to the open-source code for the methodology described. |
| Open Datasets | No | The paper describes generating synthetic datasets for its experiments rather than using a publicly available or open dataset. For example: "We start by generating a dataset (X, y) with X Rn d as follows. First, sample a ground truth model θ Nd(0, I). Next, generate a design matrix X of n i.i.d. feature vectors xi Nd(0, I) and get their responses y = Xθ +ε, where each εi N(0, σ2) is i.i.d. Gaussian noise with σ = 0.1." |
| Dataset Splits | No | The paper specifies the generation of a test set but does not explicitly mention distinct training/validation/test splits or their percentages for reproducibility. It discusses training data and test data without detailing a validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | Our experiments use Num Py (Harris et al., 2020) and scikit-learn s Logistic Regression (Pedregosa et al., 2011). No specific version numbers for these software components are provided. |
| Experiment Setup | Yes | To study the convergence of Prior Boost and PBPrefix, we set T = 256. Then we set n = T 4096 = 2^20 and d = 8. ... All three algorithms fit logistic regression models with binary cross-entropy loss and L2 regularization penalty λ = 10. |