The Theory and Applications of the Stochastic Point Location Problem
Bibliographic record
Abstract
In this keynote talk, we will survey and explain the state-of-the-art concerning the Stochastic Search on the Line (SSL) problem, also synonymously known as the Stochastic Point Location (SPL) Problem. The SPL was introduced by Oommen in [10], and it has been studied and analyzed by numerous researchers during the last two decades. It involves determining an unknown “point” when all that the learning system stochastically knows is whether the current point that has been chosen is to the left or the right of the unknown point.In this talk, we will explain how the SPL is a fundamental problem in machine learning, optimization and control, and demonstrate that it is also central to the field of AI. The talk will survey the various automata-based and hierarchical techniques that have been used to solve it, including learning from a Stochastic Teacher or a Compulsive Liar, and in symmetric mechanisms. We will then describe how it is all-pervasive in a variety of application domains and discuss these applications.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".