reviews: 
Comments: This work presents a generative approach for optimizing physics detector design in rare event scenarios by leveraging a Conditional Neural Process (CNP). By incorporating techniques like simple co-kriging, data augmentation, and multi-fidelity modeling, the authors demonstrate that their surrogate model effectively addresses the computational demands of rare event detection while maintaining the accuracy.
Strengths: The authors provide a solid background introduction to the NLDBD problem and offer clear, detailed explanations of the technical methods used in this work, including co-kriging and Conditional Neural Processes (CNP), in the appendix.
Weaknesses: Some of the critical weaknesses: 

- While the authors provide a solid introduction to NLDBD and its motivation, the paper lacks a literature review on rare event simulation/modeling. Rare event simulation is a well-established field in engineering, including techniques such as the first/second-order reliability methods (FORM/SORM) [1, 2, 3], polynomial-based surrogate modeling [4, 5], adaptive and sequential importance sampling [6, 7], and ensemble Kalman filters [8]. These approaches have also been extended to multi-fidelity settings, as seen in [9, 10, 11]. Although it’s not necessary to cite all related works, the authors should clarify why they focus on deep generative modeling, highlighting its advantages over traditional models.

- The computational results of the proposed method appear to lack a baseline, making it challenging to assess performance without a fair comparison.

- Some notations are not statistically rigorous. Specific comments on notations are provided in the questions section. Also, the variables $\phi$ and $\theta$ are inconsistently bolded, which can cause confusion. 

Overall, we find that this work falls below the acceptance threshold for ICLR. We encourage the authors to: (1) expand the literature review to include rare event simulation/probability estimation as outlined here, (2) refine the statistical notations, and (3) add baselines for experiments. With these adjustments, this work would be in a better shape.

[1] A. M. Hasofer, An exact and invarient first order reliability format, J. Eng. Mech. Div., Proc. ASCE 100 (1974) 111–121.

[2] A. Der Kiureghian, et al., First-and second-order reliability methods, Engineering design reliability handbook 14 (2005).

[3] B. Fiessler, H.-J. Neumann, R. Rackwitz, Quadratic limit states in structural reliability, Journal of the Engineering Mechanics Division 105 (1979) 661–676.

[4] J. Li, D. Xiu, Evaluation of failure probability via surrogate models, Journal of Computational Physics 229 (2010) 8966–8980.

[5] J. Li, J. Li, D. Xiu, An efficient surrogate-based method for computing rare failure probability, Journal of Computational Physics 230 (2011) 8683–8697.

[6] C. G. Bucher, Adaptive sampling—an iterative fast monte carlo procedure, Structural safety 5 (1988) 119–126.

[7] I. Papaioannou, C. Papadimitriou, D. Straub, Sequential importance sampling for structural reliability analysis, Structural safety 62 (2016) 66–75.

[8] F. Wagner, I. Papaioannou, E. Ullmann, The ensemble kalman filter for rare event estimation, SIAM/ASA Journal on Uncertainty Quantification 10 (2022) 317–349.

[9] B. Peherstorfer, T. Cui, Y. Marzouk, K. Willcox, Multifidelity importance sampling, Computer Methods in Applied Mechanics and Engineering 300 (2016) 490–509.

[10] B. Peherstorfer, B. Kramer, K. Willcox, Multifidelity preconditioning of the crossentropy method for rare event simulation and failure probability estimation, SIAM/ASA Journal on Uncertainty Quantification 6 (2018) 737–761.

[11] F. Wagner, J. Latz, I. Papaioannou, E. Ullmann, Multilevel sequential importance sampling for rare event estimation, SIAM Journal on Scientific Computing 42 (2020) A2062–A2087.
Recommendation: 5
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 3