Bibliographic record
Abstract
Sign-rank and discrepancy are two central notions in communication complexity. The seminal paper by Babai, Frankl, and Simon (FOCS'86) initiated an active line of research that investigates the gap between these two notions. In this article, we establish the strongest possible separation by constructing a boolean matrix whose sign-rank is only $3$, and yet its discrepancy is $2^{-\Omega(n)}$. We note that every matrix of sign-rank $2$ has discrepancy $n^{-O(1)}$. In connection with learning theory, our result implies the existence of Boolean matrices whose entries are represented by points and half-spaces in dimension $3$, and yet, the normalized margin of any such representation (angle between the half-spaces and the unit vectors representing the points), even in higher dimensions, is very small. In the context of communication complexity, our result in particular implies that there are boolean functions with $O(1)$ unbounded-error randomized communication complexity while having $\Omega(n)$ weakly unbounded-error randomized communication complexity. ------------------- A conference version of this paper appeared in the Proceedings of the 35th Computational Complexity Conference, 2020 (CCC'20).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".