arXiv:2608.07795v1 Announce Type: new Abstract: Prediction intervals for multi-modal regression with tabular variables, text, images, or other input sources are difficult to calibrate when those sources…
arXiv:2608.08003v1 Announce Type: new Abstract: As machine learned models increase in complexity and expressive power, features of simpler models, such as interpretability and control over the…
arXiv:2608.08424v1 Announce Type: new Abstract: Conformal changepoint localization turns any score into a confidence set for the changepoint with finite-sample coverage. Coverage is universal; efficiency is…
arXiv:2608.08204v1 Announce Type: new Abstract: This work proposes deep nonparametric Instrumental variable quantile regression (IVQR), a two-stage estimator that combines conditional diffusion modeling with a kernel-smoothed…
arXiv:2608.08704v1 Announce Type: new Abstract: Kernel spectral clustering with a single bandwidth can be inadequate for data exhibiting multiple characteristic pairwise-distance scales, a problem particularly prevalent…
arXiv:2608.08588v1 Announce Type: new Abstract: Hamiltonian Monte Carlo (HMC) is a successful generic inference method in probabilistic programming, but in its ordinary formulation it needs gradients…
arXiv:2410.10523v3 Announce Type: replace Abstract: The aim of this book is to demonstrate the potential for ideas in machine learning to impact on the fields of…
arXiv:2601.14609v2 Announce Type: replace Abstract: Multi-site collaboration can power survival models that no single hospital could fit alone, but privacy rules and protected computing environments block…
arXiv:2509.03910v2 Announce Type: replace Abstract: We formulate inverse problems in a Bayesian framework and aim to train an invertible generative model that is capable of simulation…
arXiv:2604.03146v4 Announce Type: replace Abstract: We study high-dimensional convex empirical risk minimization (ERM) under general non-Gaussian data designs. By heuristically extending the Convex Gaussian Min-Max Theorem…
arXiv:2504.18587v2 Announce Type: replace-cross Abstract: Reinforcement learning has emerged as a powerful approach for improving the reasoning capabilities of large language models, as demonstrated by systems…
arXiv:2608.07537v1 Announce Type: new Abstract: In this study, we propose a framework that incorporates subjective evaluations provided by a Vision-Language Model (VLM) into the fitness evaluation…
arXiv:2608.08156v1 Announce Type: cross Abstract: In evolutionary algorithms powered by language models, the LLM acts as a single operator that simultaneously updates structural components (like control…
arXiv:2608.09888v1 Announce Type: new Abstract: We introduce BDH-CQ, a reasoning model that combines in-context learning with recurrent latent reasoning. Inputs presented at inference time continuously update…
arXiv:2608.09833v1 Announce Type: new Abstract: In this work, we introduce analytical replay experiments to the evolutionary computing community. Replay experiments originated in the context of laboratory…
arXiv:2605.21379v3 Announce Type: replace Abstract: In The Algebraic Mind (2001), Marcus held that any adequate cognitive architecture needs operations over variables, recursively structured representations, and an…
arXiv:2608.07544v1 Announce Type: new Abstract: Automated heuristic design (AHD) with large language models (LLMs) has produced strong heuristics for combinatorial optimization problems (COPs). Yet existing frameworks…
arXiv:2608.07545v1 Announce Type: new Abstract: An LLM agent's capability depends not only on model weights but on its harness: prompts, tools, skills, and control flow. Self-improvement…
arXiv:2608.07587v1 Announce Type: new Abstract: Particle swarm optimization (PSO) has been widely applied to solve complex optimization problems from real-world applications due to its efficient exploration…
arXiv:2608.08081v1 Announce Type: new Abstract: Large mixture-of-experts (MoE) language models with 26--120 billion parameters exceed the memory capacity of consumer devices through three simultaneous pressures: resident…
arXiv:2608.07754v1 Announce Type: new Abstract: State-space models (SSMs) provide a powerful theoretical framework to enable parallel training of recurrent networks. We expand on previous work adapting…
arXiv:2608.08898v1 Announce Type: new Abstract: Characterising optimisation problem instances is a fundamental part of understanding the behaviour and performance of different algorithms as well as providing…
arXiv:2608.08479v1 Announce Type: new Abstract: Spiking neural networks (SNNs) offer a promising pathway to energy-efficient AI and brain-inspired computing. However, their widespread adoption is hindered by…
arXiv:2603.28385v2 Announce Type: replace-cross Abstract: Maritime surveillance missions, such as search and rescue and environmental monitoring, rely on the efficient allocation of sensing assets over vast…