Upper confidence bound multi-armed bandits for partially observed Hawkes processes
Bibliographic record
Abstract
We consider the problem of estimating and ranking a set of self-excited point processes when an action must be taken to observe the events of each process. This situation arises in a number of real-world applications, for example, when crime goes unreported in some regions, or COVID-19 cases are undetected due to a lack of testing resources. Often times, such self-excited events may bear implicit causality. Therefore, we start with Hawkes Processes to model how one event triggers the other. In the scenario of undersampling, we propose Hawkes Process Multi-armed Bandits for learning such point processes to quickly learn the riskiest point processes, while carefully balancing exploitation of known (observed) point processes and exploration of unknown processes. By considering the cumulative number of events of each process as a reward, we derive an upper confidence bound on the counting process to inform actions in the form of which processes to observe in upcoming MAB rounds, based on the history of the partially observed point processes. We then derive a regret bound that scales logarithmically with the number of rounds of observation. We test our model on simulated datasets, crime report data in Vancouver and Los Angeles, and earthquake event data from Alaska, California, and worldwide. Our model outperforms several state-of-the-art MAB algorithms that can be adapted to non-stationary point process estimation across the datasets and performance metrics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".