arXiv:2507. 13263v4 Announce Type: replace-cross Abstract: Bayesian Optimization (BO) is a powerful tool for black-box optimization, but its application to high-dimensional permutation spaces is severely limited by the challenge of defining scalable representations.
arXiv:2503. 13051v3 Announce Type: replace Abstract: Sorting and permutation learning are key concepts in optimization and machine learning, especially when organizing high-dimensional data into meaningful spatial layouts.
The paper introduces a new Gaussian Process kernel, GP‑Perm, that incorporates permutation invariance for Bayesian Optimization tasks involving well placement in Carbon Capture and Storage (CCS) projects. It compares sets via a stable divergence between their empirical representations and can be combined with standard kernels for additional inputs. The authors also explore a Deep Kernel Learning model using a Deep Sets architecture as a learned invariant baseline, evaluating both approaches on eight use cases, including seven synthetic benchmarks and a realistic CCS case study in the Johansen formation.
arXiv:2606. 01111v1 Announce Type: new Abstract: Modern industrial recommender systems rely on thousands of heterogeneous features -- ranging from low-dimensional scalars (e.
arXiv:2608. 08344v1 Announce Type: cross Abstract: Permutation optimization arises whenever the components of a system are fixed but their ordering affects performance.
arXiv:2608. 12687v1 Announce Type: new Abstract: Bayesian optimization (BO) is a sample-efficient framework for analog circuit topology search, where evaluating each candidate topology can require costly simulation.
arXiv:2608.06912v2 Announce Type: replace Abstract: Selecting the top-$k$ elements is a fundamental operation for inducing sparsity in large-scale models and optimization problems, enabling robust ex...
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.
arXiv:2606. 29184v1 Announce Type: new Abstract: While Low-rank adaptation (LoRA) enables highly efficient fine-tuning by constraining task-specific updates to fixed low-rank subspaces, this rigid design limits representational flexibility and often results in overconfident predictions and miscalibrated uncertainty, especially in low-data regimes.
arXiv:2606. 08904v1 Announce Type: new Abstract: Macro placement is a fundamental step in modern chip physical design, playing a crucial role in determining the solution quality of high-dimensional combinatorial optimization problems.
The paper introduces HELLO, a hierarchical solver for large‑scale discrete optimal transport that reduces the problem to edge localization guided by dual potentials. HELLO uses a coarse‑to‑fine initialization across a recursive subsampling hierarchy and a refinement step that inserts the largest dual violators until a KKT residual tolerance is met, achieving linear memory usage. Experiments show that HELLO outperforms strong baselines by an order of magnitude in runtime while attaining lower transport objectives, and it scales to over a million samples in high‑dimensional settings, supporting various OT variants.
arXiv:2608. 04113v1 Announce Type: cross Abstract: Black-box optimization is a ubiquitous problem in science and engineering, often dealing with expensive objective functions with cheaper lower-fidelity proxies available.
arXiv:2510. 18315v2 Announce Type: replace-cross Abstract: We investigate how embedding dimension affects the emergence of an internal "world model" in a transformer trained with reinforcement learning to perform bubble-sort-style adjacent swaps.