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respectively. The top and middle rows show objective values versus the input designs, and the bottom row shows the Pareto solutions of the two objectives. At each iteration, observed points and Pareto solutions are marked with circles and stars, respectively. Starting with four initial observations att= 1, if an observed point is Pareto-"},{"citing_arxiv_id":"2605.07863","ref_index":13,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"ADKO: Agentic Decentralized Knowledge Optimization","primary_cat":"cs.LG","submitted_at":"2026-05-08T15:23:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ADKO is a decentralized framework where agents share compact GP-derived tokens and LM insights to achieve collaborative Bayesian optimization with a decomposed regret bound that includes compression and approximation losses.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[11] Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V Le, Denny Zhou, and Xinyun Chen. Large language models as optimizers. InThe Twelfth International Conference on Learning Representations, 2023. [12] Tennison Liu, Nicolás Astorga, Nabeel Seedat, and Mihaela van der Schaar. Large language models to enhance bayesian optimization.arXiv preprint arXiv:2402.03921, 2024. [13] Eric Brochu, Vlad M Cora, and Nando De Freitas. A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning.arXiv preprint arXiv:1012.2599, 2010. [14] Peter Auer. Using confidence bounds for exploitation-exploration trade-offs.Journal of machine learning research, 3(Nov):397-422, 2002."},{"citing_arxiv_id":"2604.27243","ref_index":2,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Estimating Decision Uncertainty from Preference Uncertainty: Application to Ground Vehicle Design","primary_cat":"stat.AP","submitted_at":"2026-04-29T22:30:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Preference uncertainty is modeled as random variables that induce a distribution over Pareto-optimal designs, analyzed via Sobol' indices, Shapley values, and Fréchet variance to assess decision stability in ground vehicle design.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.20288","ref_index":201,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Generative Augmentation of Imbalanced Flight Records for Flight Diversion Prediction: A Multi-objective Optimisation Framework","primary_cat":"cs.LG","submitted_at":"2026-04-22T07:35:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Hyperparameter-optimized generative models augment scarce flight diversion records and substantially improve prediction accuracy over real data alone.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.20210","ref_index":6,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Vibrotactile Preference Learning: Uncertainty-Aware Preference Learning for Personalized Vibration Feedback","primary_cat":"cs.HC","submitted_at":"2026-04-22T05:51:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"VPL learns individualized vibrotactile preferences efficiently via uncertainty-aware Gaussian process models and active query selection in a 13-participant user study on an Xbox controller.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.20125","ref_index":23,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Autonomous operation of the DIAG0 diagnostic line for 6D phase-space monitoring at LCLS-II","primary_cat":"physics.acc-ph","submitted_at":"2026-04-22T02:45:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"First autonomous 6D phase-space tomography system at LCLS-II achieves real-time beam reconstructions every 5-10 minutes via ML control and generative analysis.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.00855","ref_index":12,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"An Efficient Spatial Branch-and-Bound Algorithm for Global Optimization of Gaussian Process Posterior Mean Functions","primary_cat":"math.OC","submitted_at":"2026-04-21T01:13:54+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"PALM-Mean combines sign-aware piecewise-linear relaxations of locally important kernel terms with closed-form analytic bounds on the rest inside a reduced-space branch-and-bound framework, yielding valid lower bounds and ε-global convergence for GP posterior mean optimization.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":": Efficient global optimization of expensive black-box functions. Journal of Global Optimization13(4), 455-492 (1998) https: //doi.org/10.1023/A:1008306431147 [11] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., Freitas, N.: Taking the human out of the loop: A review of bayesian optimization. Proceedings of the IEEE 104(1), 148-175 (2016) https://doi.org/10.1109/JPROC.2015.2494218 [12] Brochu, E., Cora, V.M., Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierar- chical Reinforcement Learning (2010). https://doi.org/10.48550/arXiv.1012.2599 . https://doi.org/10.48550/arXiv.1012.2599 [13] Paulson, J.A., Tsay, C.: Bayesian optimization as a flexible and efficient design"},{"citing_arxiv_id":"2604.15837","ref_index":7,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Stein Variational Black-Box Combinatorial Optimization","primary_cat":"cs.AI","submitted_at":"2026-04-17T08:40:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Integrating Stein variational gradient descent into EDAs introduces repulsion among particles to jointly explore multiple optima in discrete black-box optimization, with 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references.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2507.10253","ref_index":30,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Optimization of a cosmic muon tomography scanner for cargo border control inspection","primary_cat":"physics.ins-det","submitted_at":"2025-07-14T13:23:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Optimization study for muon tomography cargo scanner using TomOpt Bayesian optimization and GEANT4 simulations to enhance border security detection.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2504.03943","ref_index":1,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Multi-Variable Batch Bayesian Optimization in Materials Research: Synthetic Data Analysis of Noise Sensitivity and Problem Landscape 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In this approach, we wish to compute the posterior distribution on f(x) marginalizing over all possible values of the hyperparameters, P (f(x) =y|f(x1:n)) = ∫ P (f(x) =y|f(x1:n),η )P (η|f(x1:n))dη (5) This integral is typically intractable, but we can approximate it through sampling: P (f(x) =y|f(x1:n))≈ 1 J J∑ j=1 P (f(x) =y|f(x1:n),η = ˆηj) (6) where (ˆηj :j = 1,...,J ) are sampled fromP (η|f(x1:n)) via an MCMC method, e.g., slice sampling (Neal, 2003). MAP estimation can be seen as an approximation to fully Bayesian inference: if we approximate the posterior P (η|f(x1:n)) by a point mass at the η that maximizes the posterior density, then inference with the MAP recovers (5). 6 4 Acquisition Functions"}],"limit":50,"offset":0}