Optimistic Competitive Analysis: Stochastic Models, Predictions, and Revocable Decisions
Bibliographic record
Abstract
In conventional online competitive analysis, it is assumed that online algorithms are dealing with anadversary who controls the input items and their arrival sequence, while the algorithms are making irrevocable decisions along the way. This approach is often pessimistic and results in worst-case performance guarantees that are rarely reflected in practice. In order to measure performance in more realistic settings, we consider various “relaxations” of the online model that assist the algorithm in three general ways: 1. Allowing the algorithm to alter some earlier decisions that seem to not be good in hindsight. 2. Restricting the adversary’s power to control the arrival sequence. 3. Removing some of the uncertainty of the input by having some information about the offline instance. We focus on two classical online problems: bipartite matching and interval selection. In the case ofbipartite matching, we introduce a new input model and analyze the performance of a natural greedy algorithm [27]. We also conduct an experimental analysis comparing state-of-the-art algorithms in the known i.i.d. setting and show that simple greedy algorithms might be very competitive in practice [32]. We consider the problem of interval selection with revocable acceptances in the adversarial model [30], the random-order model [31], as well as in a predictions setting [83]. We give improved bounds on the performance of some existing algorithms, and introduce new algorithms tailored to these specialized models. Moreover, most of the algorithms we study are conceptually simple, a desirable characteristic that often correlates with inherent efficiency and ease of implementation.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".