Efficient Adaptive Algorithms and Minimax Bounds for Zero-Delay Lossy Source Coding
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Bibliographic record
Abstract
Zero-delay lossy source coding schemes are considered for both individual sequences and random sources. Performance is measured by the distortion redundancy, which is defined as the difference between the normalized cumulative mean squared distortion of the scheme and the normalized cumulative distortion of the best scalar quantizer of the same rate that is matched to the entire sequence to be encoded. By improving and generalizing a scheme of Linder and Lugosi, Weissman and Merhav showed the existence of a randomized scheme that, for any bounded individual sequence of length n, achieves a distortion redundancy O(n/sup -1/3/logn). However, both schemes have prohibitive complexity (both space and time), which makes practical implementation infeasible. In this paper, we present an algorithm that computes Weissman and Merhav's scheme efficiently. In particular, we introduce an algorithm with encoding complexity O(n/sup 4/3/) and distortion redundancy O(n/sup -1/3/logn). The complexity can be made linear in the sequence length n at the price of increasing the distortion redundancy to O(n/sup -1/4//spl radic/logn). We also consider the problem of minimax distortion redundancy in zero-delay lossy coding of random sources. By introducing a simplistic scheme and proving a lower bound, we show that for the class of bounded memoryless sources, the minimax expected distortion redundancy is upper and lower bounded by constant multiples of n/sup -1/2/.
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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 | 0.001 | 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