Collaboratively Learning Linear Models with Structured Missing Data
Authors: Chen Cheng, Gary Cheng, John C. Duchi
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our method on US Census data. |
| Researcher Affiliation | Academia | Chen Cheng Stanford University chencheng@stanford.edu Gary Cheng Stanford University chenggar@stanford.edu John Duchi Stanford University jduchi@stanford.edu |
| Pseudocode | Yes | Algorithm 1: COLLAB algorithm |
| Open Source Code | Yes | The code can be found at https://github.com/garyxcheng/collab. |
| Open Datasets | Yes | We experiment on real US census data modiļ¬ed from the ACSTravel Time dataset from the folktables package [7]. |
| Dataset Splits | No | The paper mentions 'training data' and 'test data' but does not specify validation splits or percentages. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using the 'folktables package' but does not specify versions of any software dependencies used for implementation or training. |
| Experiment Setup | No | The paper describes the datasets, agents, number of features, number of trials, and methods compared, but does not specify concrete hyperparameter values or detailed training configurations (e.g., learning rates, batch sizes, optimizers). |