The paper investigates nonlinear dimensionality reduction for Bayesian optimisation (BO) by transforming high‑dimensional black‑box optimisation problems into a sequence of low‑dimensional latent‑space BO (LSBO) tasks. It extends earlier linear embedding approaches by using variational autoencoders (VAEs), deep metric loss, and adaptive retraining to better capture nonlinear structure, and couples LSBO with sequential domain reduction (SDR‑LSBO) to progressively narrow search domains. Experiments on GPU‑accelerated BoTorch with Matérn‑5/2 Gaussian‑process surrogates show that VAE‑based LSBO outperforms adaptive linear embeddings, and the authors provide a theoretical analysis of latent‑space error versus representation gap under PAC‑Bayes conditions.
By Luo Long, Coralia Cartis, Paz Fink Shustin
arXiv:2607. 23751v1 Announce Type: new Abstract: The usefulness of a variational autoencoder (VAE) depends on two properties of its latent space that are hard to obtain together: high encoding capacity in the individual latent variables, and a low-dimensional, disentangled organization of those variables.
By Ye Shi
arXiv:2508. 20330v5 Announce Type: replace Abstract: Combinatorial optimization problems are ubiquitous in science and engineering.
By Zohair Shafi, Serdar Kadioglu
arXiv:2607. 01275v1 Announce Type: cross Abstract: Variational Autoencoders (VAEs) commonly assume a standard isotropic Gaussian prior over the latent space, an assumption that often fails to capture the true distribution of latent representations for complex datasets.
By Qijun Chen, Shaofan Li
arXiv:2605. 22876v2 Announce Type: replace Abstract: Existing neural solvers for Multi-Objective Combinatorial Optimization Problems (MOCOPs) commonly adopt decomposition-based strategies that scalarize a MOCOP into multiple subproblems associated with distinct weight vectors.
By Xuan Wu, Jinbiao Chen, Yang Li, Lijie Wen, Chunguo Wu, Yuanshu Li, Yubin Xiao, Chunyan Miao, You Zhou, Di Wang
arXiv:2606. 09664v1 Announce Type: new Abstract: Bayesian optimization (BO) is a central tool for sample-efficient design, and latent-space Bayesian optimization (LSBO) extends it to structured objects such as molecules and proteins.
By Tuan A. Vu, Harri L\"ahdesm\"aki, Julien Martinelli
Co-PiLOT is a latent optimization framework that maps candidate physical structures through a generative encoder-decoder, using the decoder as a learned validity prior and performing physics-informed black-box optimization in latent space. It is applied to the inverse design of magnesium alloy microstructure/texture, employing a vision transformer encoder paired with latent diffusion, diffusion transformer, and rectified-flow transformer decoders trained on an 80,000-sample EBSD dataset to produce a minimal bottleneck representation. The MERIDIAN optimizer, driven by a deep-kernel Gaussian process and failure-aware feasibility prediction, achieves the best target-driven objective score within 160 simulations, reducing relative target error by 3–22% compared to seven baseline methods.
By Mahish K. Guru, Mayank Nagar, Ayush vyas, Jan Bohlen, Roland Aydin, Noomane Ben Khalifa
arXiv:2603. 24567v2 Announce Type: replace-cross Abstract: Constrained optimization in high-dimensional black-box settings is difficult due to expensive evaluations, the lack of gradient information, and complex feasibility regions.
By Raju Chowdhury, Tanmay Sen, Biswabrata Pradhan
arXiv:2607. 11555v1 Announce Type: new Abstract: Learning neural set functions is pivotal to a wide range of important applications, including compound selection in AI-driven drug discovery and product recommendation.
By Yongquan Shi, Zijing Ou, Shiping Wang, Yatao Bian
Fenchel Tilt Flow Control (FTFC) is a new method for fine‑tuning pretrained generative models to arbitrary preference functions. It decouples utility optimization from model fitting by first learning reward and density‑ratio weights on pretrained samples, then freezing these weights to adjust a diffusion or flow model in a single importance‑weighted stage. The approach supports general f‑divergence penalties, achieves exact duality for concave utilities, and demonstrates up to 20× efficiency gains while outperforming baselines on image and molecule generation tasks.
By Maksim Bobrin, Maksim Zhdanov, Dmitry Dylov
arXiv:2602. 02241v2 Announce Type: replace Abstract: Entropic optimal transport (EOT) in continuous spaces with quadratic cost is a classical tool for solving the domain translation problem.
By Roman Dyachenko, Nikita Gushchin, Kirill Sokolov, Petr Mokrov, Evgeny Burnaev, Alexander Korotin
arXiv:2609. 22752v1 Announce Type: new Abstract: Diffusion models have demonstrated strong power in generative modeling tasks across multiple domains, exhibiting a remarkable capability of learning complex distributions from samples.
By Yang Hu, Na Li