Robust Adaptive Nonlinear KF Under Hierarchically Gaussian Outliers
Why this work is in the frame
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Bibliographic record
Abstract
Standard state estimation techniques are designed under the assumption that the system is perfectly known, which does not typically hold in practice. Under model mismatch the filter performance is significantly degraded, reason why robust estimators are relevant. In this contribution we address the nonlinear filtering problem under outliers, for which a skewed Gaussian scale mixture distribution is considered to obtain a flexible description that allows for a conditionally Gaussian representation. A variational Bayesian approach is used to approximate the joint posterior distribution of the states and latent variables, designing a robust nonlinear filter, where the skewness parameters are estimated by online expectation-maximization. An illustrative navigation example is provided to show the new filter’s advantages and limitations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 it