Interactive information complexity and its applications
Bibliographic record
Abstract
Given a two-party Boolean function f : {0, 1} n × {0, 1} n → {0, 1} that maps input (x, y) to f (x, y), communication complexity studies how many communicated bits must be exchanged between two players, Alice who knows only x, and Bob who knows only y, in order for them to jointly compute f (x, y).Since Andrew Yao defined the communication model in the late 1970s, communication complexity has steadily developed without the influence of information theory, which was founded by Claude Shannon in the late 1940s to study coding theory.However, with the introduction of information theory into communication complexity, in recent decades, a new research topic in computational complexity theory has emerged: information complexity.Within Yao's communication model, information complexity studies how much information a protocol reveals about the players' input.Allowing for a degree of error > 0 when computing a Boolean function potentially requires less information revealed.For example, any Boolean-valued function can be computed with an error 1/2 by a random guess, that has essentially no communication and reveals no information about the inputs.This thesis studies how information complexity changes as one allows for different errors when computing a Boolean function.The two main questions studied are:(1). (small error) How much information can be saved by allowing a small error > 0, as compared to cases when no error is allowed at all?(2). (large error) How much information must be revealed in order to have an error of at most 1/2 -?We systematically study these two questions for arbitrary functions, obtaining virtually complete answers for both.For Question (1), we show that at least Ω(h( )) and at most i I wish to sincerely thank my supervisor, Hamed Hatami, for giving me the opportunity to work with him, and for constant support to both my academic growth and my personal life.There were some difficult times during my PhD study.I would not be able to finish my study without the support and good advice from Hamed.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".