Online Active Linear Regression via Thresholding
Authors: Carlos Riquelme, Ramesh Johari, Baosen Zhang
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations suggest the algorithm is remarkably robust: it provides significant benefits over passive random sampling in real-world datasets that exhibit high nonlinearity and high dimensionality significantly reducing both the mean and variance of the squared error. |
| Researcher Affiliation | Academia | Carlos Riquelme and Ramesh Johari Stanford University, {rikel, rjohari}@stanford.edu. Baosen Zhang Washington University, zhangbao@uw.edu. |
| Pseudocode | Yes | Algorithm 1 Thresholding Algorithm. Algorithm 1 b Adaptive Thresholding Algorithm. Algorithm 2 Sparse Thresholding Algorithm. |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the methodology described. |
| Open Datasets | Yes | We show the results of Algorithm 1b (online Σ estimation) with the simplest distributional assumption (Gaussian threshold, ξj = 1) versus random sampling on publicly available real-world datasets (UCI, (Lichman 2013)), measuring test squared prediction error. |
| Dataset Splits | No | In each one, we randomly split the dataset in training (n observations, random order), and test (rest of them). The paper mentions training and testing splits, but does not explicitly specify a validation split or its size/methodology. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper describes the use of Lasso estimator and OLS estimator, but does not provide specific software names with version numbers for dependencies. |
| Experiment Setup | No | The paper mentions algorithmic parameters like k1 = (2/3)k for recovery, and constants C, λ, and Γ within the algorithms. However, it lacks specific experimental setup details such as learning rates, batch sizes, optimizer types, or other common hyperparameters typically needed for reproducibility in machine learning experiments. |