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Efficient Conditioning Why Pseudo Observation Batch Bayesian Optimization Works When It Does not

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arXiv:2605. 18819v1 Announce Type: new Abstract: Constant Liar (CL), Kriging Believer (KB), and fantasy models are widely used for batch selection in parallel Bayesian Optimization, yet a unified theory explaining their effectiveness and conditions under which they fail has been lacking.

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Authors: Kumbha Nagaswetha, Rabi Pathak
Released: 2026-05-20 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Efficient Conditioning Why Pseudo Observation Batch Bayesian Optimization Works When It Does not overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "Follow-up to #354: cascading chunk-binding — does A→B, B→C, C→D propagate the full chain on a recipient trained only to emit A?". Matching terms: under, rate, does, full, trained, test, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2605.18819

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