Adaptive KappaSharp: Condition-Number Shaping for Preferential Bayesian Optimization

arXiv:2608.07859 · cs.LG, stat.ML · Submitted 2026-08-08 · Read on arXiv

Ketong Shao, Jialu Wang, Xuekai Pei, Ali Mesbah

cs.LG, stat.ML

Submitted: 2026-08-08

Updated: 2026-08-11

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 36× in this paper · explained in 2 episodes
• Bayesian Optimization — 13× in this paper · explained in 3 episodes
• Pairwise Comparisons — 13× in this paper · explained in 2 episodes
• Acquisition Function — 12× in this paper · explained in 1 episode
• Augmentation — 11× in this paper · explained in 2 episodes
• Monotonicity — 6× in this paper · explained in 2 episodes
• Regularization — 6× in this paper · explained in 3 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Calibration — 4× in this paper · explained in 9 episodes
• In International Conference on Machine Learning — 4× in this paper
• Expected Utility of Best Option — 2× in this paper
• Fisher Information — 2× in this paper · explained in 4 episodes
• Regret — 2× in this paper · explained in 4 episodes
• Regret Bound — 2× in this paper · explained in 2 episodes
• Regret Bounds — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 6 episodes
• Surrogate Modeling — 2× in this paper · explained in 2 episodes
• Bayesian Optimization (BO) — 1× in this paper · explained in 5 episodes
• Catastrophic Forgetting — 1× in this paper · explained in 9 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Continual Learning — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Initial Data — 1× in this paper · explained in 2 episodes
• Preconditioner — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Preferential Bayesian optimisation with Skew Gaussian Processes
• A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

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