AI RESEARCH
Efficient Conditioning Why Pseudo Observation Batch Bayesian Optimization Works When It Does not
arXiv CS.LG
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ArXi:2605.18819v1 Announce Type: new 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. We identify efficient conditioning as the key surrogate property the ability to update predictions in closed form when data is augmented.