Kernel Representation Learning for Time Sequence: Algorithm, Theory, and Applications
Why this work is in the frame
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Bibliographic record
Abstract
Time sequences are essential in fields such as finance, healthcare, and environmental science, where understanding temporal dependencies and making accurate predictions are crucial. These sequences often exhibit complexities like nonlinearity, noise, and concept drift. Traditional models struggle to capture the intricate dynamics of multivariate and co-evolving sequences, particularly in contexts where relationships between variables shift unpredictably. This thesis introduces a range of Kernel Representation Learning (KRL) methodologies to address these challenges. We develop kernel self-representation learning to capture the temporal dependencies and hidden structures, while identifying concept drift in co-evolving sequences. Additionally, we explore theoretical connections between KRL and advanced deep-learning models. The proposed methods are validated through real-world applications, showing improvements in predictive accuracy, interpretability, and robustness.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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