arXiv:2512. 06143v2 Announce Type: replace Abstract: Despite a large corpus of recent work on scaling up Gaussian processes, a stubborn trade-off between computational speed, prediction and uncertainty quantification accuracy, and customizability persists.
By Marcus M. Noack, Mark D. Risser, Hengrui Luo, Vardaan Tekriwal, Ronald J. Pandolfi
arXiv:2603. 24002v3 Announce Type: replace Abstract: Physics-Informed Neural Networks (PINNs) for high-dimensional and high-order partial differential equations (PDEs) are primarily constrained by the $\mathcal{O}(d^k)$ spatial derivative complexity and the $\mathcal{O}(P)$ memory overhead of backpropagation (BP).
By Zhangyong Liang, Huanhuan Gao
arXiv:2511. 16340v2 Announce Type: replace Abstract: Efficient Gaussian process (GP) inference is critical for sequential decision-making tasks such as active learning, online prediction, and Bayesian optimization.
By Alan Yufei Dong, Jihao Andreas Lin, Jos\'e Miguel Hern\'andez-Lobato
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...
By Jakub Antczak, Joanna Wojciechowicz, Kamil Ksi\k{a}\.zek, Marcin Mazur, {\L}ukasz Struski, Jacek Tabor
GRADSOLVE is an open‑source JAX library that provides fast, exact reverse‑mode gradients for low‑dimensional ordinary differential equation (ODE) ensembles on NVIDIA GPUs. It records the accepted steps of an adaptive solver and differentiates a fixed‑step replay, yielding the exact discrete adjoint at a lower computational cost than traditional checkpointed methods. Benchmarks show that GRADSOLVE’s forward kernel is 2.8× faster than DiffEqGPU.jl, and its gradient computation is 5.6–14.1× faster than Diffrax’s checkpointed adjoint while maintaining matched forward‑state accuracy across multiple GPU generations.
By Alessio Spurio Mancini
The paper introduces Finite-Library Input-Warped Bayesian Optimization (FLIWBO), a method that selects input warps from a finite library to adapt the geometry used by Gaussian‑process Bayesian optimization. FLIWBO maintains high‑probability convergence guarantees while improving sample efficiency on problems where raw coordinates poorly match the objective’s geometry, such as log‑scaled hyperparameters or localized peaks. Experiments on synthetic benchmarks, Fashion‑MNIST hyperparameter tuning, and a 20‑dimensional multi‑agent system design demonstrate that FLIWBO‑UCB outperforms raw‑coordinate GP‑UCB and other methods with regret guarantees, especially under misspecified geometry.
By Edvin Ketabati Augustinsson, Robert A. Bridges