Active Heteroscedastic Regression
Authors: Kamalika Chaudhuri, Prateek Jain, Nagarajan Natarajan
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conclude the paper presenting simulations supporting our theoretical bounds as well as experiments on real-world data. |
| Researcher Affiliation | Collaboration | Kamalika Chaudhuri 1 Prateek Jain 2 Nagarajan Natarajan 2 ... 1University of California, San Diego 2Microsoft Research, India. |
| Pseudocode | Yes | Algorithm 1 Passive Regression With Noise Oracle; Algorithm 2 Active Regression With Noise Oracle; Algorithm 3 Least Squares with Estimated Weights; Algorithm 4 Active Regression |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | We also study the performance of the algorithms on two real-world datasets from UCI: (1) WINE QUALITY with m = 6500 and d = 11, and (2) MSD (a subset of the million song dataset) with m = 515345 and d = 90. |
| Dataset Splits | No | For each dataset, we create a 70-30 train-test split, and learn the best linear regressor using ordinary least squares, which forms our β . |
| 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 | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers). |
| Experiment Setup | No | The paper describes the synthetic data generation process and data splitting for real-world datasets, but it does not specify concrete hyperparameter values or detailed training configurations for the models (e.g., learning rates, batch sizes, optimizers). |