Semi-Parametric Inference with Density Ratio Model Fitted to Distributed Data using Alternating Direction Method of Multipliers
Bibliographic record
Abstract
With the sheer volume and complexity of modern day data sets, there has become a need for new techniques and methodologies to handle problems related to "big data". One such problem of interest arrises when data is distributed and stored in various locations. For reasons of confidentially, complexity or volume, we are unable to have access to the entire dataset in one centralized location. Alongside the presence of distributed data, we are also interested in carrying out inference using all of the data from each local storage unit. This thesis presents the application of the Alternating Direction Method of Multipliers (ADMM) algorithm to fit a semi-parametric Density Ratio Model (DRM) to a collection of distributed independent samples. The model parameters obtained using ADMM were found to be comparable to those fit from the same data present in a non-distributed setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".