Network Latency Estimation With Leverage Sampling for Personal Devices: An Adaptive Tensor Completion Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, end-to-end network latency estimation has attracted much attention because of its significance for network performance evaluation. Given the widespread use of personal devices, latency estimation from partially observed samples becomes more complicated due to unstable communication conditions, while measuring the latencies between all nodes in a large-scale network is infeasible and costly. Hence, reducing the measurement cost becomes critical for the latency estimation of personal device network. In this paper, we propose an adaptive sampling scheme based on leverage scores to reduce the measurement cost while achieving high estimation accuracy. Furthermore, we provide theoretical analysis to characterize the performance bounds of the proposed scheme in terms of sampling complexity and estimation error. Finally, we demonstrate the efficiency of the proposed scheme by conducting extensive simulations on both synthetic and real datasets. The results show that the proposed scheme is able to not only improve the estimation accuracy of network latency but also reduce the sample budget compared to the state-of-the-art approaches.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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