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.
Federated High-Dimensional Online Decision Making
Authors: Chi-Hua Wang, Wenjie Li, Guang Lin
TMLR 2023 | Venue PDF | LLM Run Details | Input Tokens: 21,839 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,887 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 | Experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real-world datasets. ... 4 Empirical results While the theoretical regret analysis (Theorem 3.7) provides worst-case guarantees for Fedego Lasso, we now examine their performance on a variety of tasks empirically. Examination is conducted using both synthetic and real-world data. |
| Researcher Affiliation | Academia | Chi-Hua Wang EMAIL Department of Statistics, University of California, Los Angles Wenjie Li EMAIL Department of Statistics, Purdue University Guang Lin EMAIL Department of Mathematics, Statistics, Purdue University School of Mechanical Engineering, Purdue University |
| Pseudocode | Yes | Algorithm 1 Fedego Lasso: client m ... Algorithm 2 Col Explore(x, t) ... Algorithm 3 Fed Screen(x, {ˆβk}K k=1, h) ... Algorithm 4 Ego Commit( ˆK(x), {ˆβk}K k=1) |
| Open Source Code | No | The paper mentions "We utilize the publicly available code for Pharm GKB at https://github.com/chuchro3/Warfarin for the processing of the dataset." However, this refers to third-party code for dataset processing, not the authors' own source code for the Fedego Lasso methodology. |
| Open Datasets | Yes | Experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real-world datasets (Pharm GKB, Medical MNIST)... F.2 Pharm GKB. We utilize the publicly available code for Pharm GKB at https://github.com/chuchro3/Warfarin for the processing of the dataset. ... F.3 Medical MNIST. The second real-world task is to collaborate classification of Medical MNIST images. |
| Dataset Splits | Yes | The dataset was randomly split into a training set and a testing set in the ratio of 9:1. The images are pre-processed with random rotation, random horizontal flipping, resizing, center cropping and normalization. |
| Hardware Specification | No | The paper describes the architecture of a neural network used for Medical MNIST but does not specify any hardware components like CPU, GPU models, or memory. |
| Software Dependencies | No | The paper describes training a fully-connected neural network and mentions using publicly available code for Pharm GKB processing, but it does not specify any software frameworks, libraries, or their version numbers (e.g., PyTorch, TensorFlow, scikit-learn versions). |
| Experiment Setup | Yes | The training batch size was 32. The optimizer was SGD with learning rate 0.01 and no momentum or weight decay. The neural network was trained on the training set for 10 epochs and evaluated on the testing set. |