Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Collaboratively Learning Linear Models with Structured Missing Data
Authors: Chen Cheng, Gary Cheng, John C. Duchi
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 16,983 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,524 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our method on US Census data. |
| Researcher Affiliation | Academia | Chen Cheng Stanford University EMAIL Gary Cheng Stanford University EMAIL John Duchi Stanford University EMAIL |
| 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 modified 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). |