OpenFrontiers
← Back to the index
OP-11926Status unassessedLearning theory

Bayesian optimization with failed function evaluations

Bayesian optimization with failed function evaluations. Current research status is unassessed. The dated source poses this question; inclusion does not certify that it remains open today.

Editorial MSC2020 mapping of Bayesian optimization with failed function evaluations. https://jmlr.org/papers/volume25/23-0269/23-0269.pdf#page=50 Not an author-supplied code.

Source question · editorial summaryAs of 2024; current status unassessedSource & evidence ↓

Problem statement

Develop principled Bayesian optimization methods for invalid evaluations caused by hardware or downstream failures, including failures poorly modeled as deterministic input constraints.

Lean 4

Not yet attached

No Lean statement or proof is attached yet. Have a formalization? Share its source to add it to this problem.

Background

Editorial question summary. Consult the linked source for the complete model, assumptions and discussion.

Progress & source assessment

Current research status is unassessed. The dated source poses this question; inclusion does not certify that it remains open today.

References
  1. [1]Zi Wang; George E. Dahl; Kevin Swersky; Chansoo Lee; Zachary Nado; Justin Gilmer; Jasper Snoek; Zoubin Ghahramani. JMLR — Pre-trained Gaussian Processes for Bayesian Optimization (2024).; Bayesian optimization with failed function evaluations linkCC BY 4.0; attributed editorial adaptation of the named section authors.

Recorded progress

Add yours →

Nothing recorded yet. A pointer to a paper the entry is missing counts.

Machine attempts

0 recorded

Attempts run against the statement below and are recorded in full, including the failures. The point is to stop the next model — and the next person — from re-walking a road that is already known to end. Each attempt costs 25 credits.

No machine attempts recorded.

Sign in to run an attempt. New accounts start with enough credits for ten.

Discussion

0 notes

Notes are for clarifying the statement, flagging a missing reference, or recording why an approach fails. Claimed solutions belong in a submission, where they can be reviewed.

No notes yet.

Sign in to add a note.