Paper: Robust Model-Based Optimization for Challenging Fitness Landscapes (ICLR-2024)

Reviewer comment (Writing):
A clear description of the robustness concept needs to be provided for friendly reading.

Author response:
Thanks for your suggestion. We have now added the following sentence to the Introduction, where the central goal of the work is introduced: “ A robust algorithm should have consistent performance under varying separation and imbalance.”

Reviewer comment (Presentation):
A description of the organization of this paper as well as a Conclusion session should be added.

Author response:
Description of the organization of the paper has been added. The section previously titled “Discussion” is meant to be our conclusion section and has been re-titled following the reviewer’s suggestion.

Reviewer comment (Experiments):
What is the maximum dimensionality examined in the experiments?

Author response:
The input dimension changes from one to 2500 in our experiments. PPGVAE did well in all benchmarks. PINN benchmark has the maximum input dimension of 2500. For protein datasets, AAV has the largest input dimension with sequences of length 28 and encoding of each amino acid with a 20-dimensional vector. The dimension of the latent space ranges from 2 to 20 in our experiments. This is included in the Appendix Table A2.

Reviewer comment (Experiments):
How will the proposed method perform when increasing the dimensionality of an optimization problem?

Author response:
The input dimension changes from one to 2500 in our experiments. PPGVAE did well in all benchmarks. PINN benchmark has the maximum input dimension of 2500. For protein datasets, AAV has the largest input dimension with sequences of length 28 and encoding of each amino acid with a 20-dimensional vector. The dimension of the latent space ranges from 2 to 20 in our experiments. This is included in the Appendix Table A2.

Reviewer comment (Experiments):
How easily can PPGVAE be extended to prioritize or balance multiple properties simultaneously? Would this require a significant alteration to the existing framework?

Author response:
Thanks for this important question/suggestion. There are two ways to approach this. The simpler approach will be to combine multiple properties into a single property with a proper transformation. A second possible approach may be through modified training of PPGVAE: assign a subset of dimensions in the latent space to each property and enforce the relationship loss for each subset of latent dimensions (property). This idea will require future implementation and testing.