Tootfinder

GRADSTOP: Early Stopping of Gradient Descent via Posterior Sampling
Arash Jamshidi, Lauri Sepp\"al\"ainen, Katsiaryna Haitsiukevich, Hoang Phuc Hau Luu, Anton Bj\"orklund, Kai Puolam\"aki
https://arxiv.org/abs/2508.19028

GRADSTOP: Early Stopping of Gradient Descent via Posterior Sampling
Machine learning models are often learned by minimising a loss function on the training data using a gradient descent algorithm. These models often suffer from overfitting, leading to a decline in predictive performance on unseen data. A standard solution is early stopping using a hold-out validation set, which halts the minimisation when the validation loss stops decreasing. However, this hold-out set reduces the data available for training. This paper presents {\sc gradstop}, a novel stochast…

Adaptive generative moment matching networks for improved learning of dependence structures
Marius Hofert, Gan Yao
https://arxiv.org/abs/2508.21531 https://

Adaptive generative moment matching networks for improved learning of dependence structures
An adaptive bandwidth selection procedure for the mixture kernel in the maximum mean discrepancy (MMD) for fitting generative moment matching networks (GMMNs) is introduced, and its ability to improve the learning of copula random number generators is demonstrated. Based on the relative error of the training loss, the number of kernels is increased during training; additionally, the relative error of the validation loss is used as an early stopping criterion. While training time of such adaptiv…

Optimal Stopping for Sequential Bayesian Experimental Design
Chen Cheng, Xun Huan
https://arxiv.org/abs/2509.21734 https://arxiv.org/pdf/2509.21734

Optimal Stopping for Sequential Bayesian Experimental Design
In sequential Bayesian experimental design, the number of experiments is usually fixed in advance. In practice, however, campaigns may terminate early, raising the fundamental question: when should one stop? Threshold-based rules are simple to implement but inherently myopic, as they trigger termination based on a fixed criterion while ignoring the expected future information gain that additional experiments might provide. We develop a principled Bayesian framework for optimal stopping in seque…

The Hidden Cost of Defaults in Recommender System Evaluation
Hannah Berlin, Robin Svahn, Alan Said
https://arxiv.org/abs/2508.21180 https://arxiv.org/pdf/2…

The Hidden Cost of Defaults in Recommender System Evaluation
Hyperparameter optimization is critical for improving the performance of recommender systems, yet its implementation is often treated as a neutral or secondary concern. In this work, we shift focus from model benchmarking to auditing the behavior of RecBole, a widely used recommendation framework. We show that RecBole's internal defaults, particularly an undocumented early-stopping policy, can prematurely terminate Random Search and Bayesian Optimization. This limits search coverage in ways tha…

Early Stopping Chain-of-thoughts in Large Language Models
Minjia Mao, Bowen Yin, Yu Zhu, Xiao Fang
https://arxiv.org/abs/2509.14004 https://arxiv.org/pdf/2…

Early Stopping Chain-of-thoughts in Large Language Models
Reasoning large language models (LLMs) have demonstrated superior capacities in solving complicated problems by generating long chain-of-thoughts (CoT), but such a lengthy CoT incurs high inference costs. In this study, we introduce ES-CoT, an inference-time method that shortens CoT generation by detecting answer convergence and stopping early with minimal performance loss. At the end of each reasoning step, we prompt the LLM to output its current final answer, denoted as a step answer. We then…

Optimal Stopping in Latent Diffusion Models
Yu-Han Wu, Quentin Berthet, G\'erard Biau, Claire Boyer, Romuald Elie, Pierre Marion
https://arxiv.org/abs/2510.08409 https://

Optimal Stopping in Latent Diffusion Models
We identify and analyze a surprising phenomenon of Latent Diffusion Models (LDMs) where the final steps of the diffusion can degrade sample quality. In contrast to conventional arguments that justify early stopping for numerical stability, this phenomenon is intrinsic to the dimensionality reduction in LDMs. We provide a principled explanation by analyzing the interaction between latent dimension and stopping time. Under a Gaussian framework with linear autoencoders, we characterize the conditi…

An information metric for comparing and assessing informative interim decisions in sequential clinical trials
G. Caruso, W. F. Rosenberger, P. Mozgunov, N. Flournoy
https://arxiv.org/abs/2509.04904

An information metric for comparing and assessing informative interim decisions in sequential clinical trials
Group sequential designs enable interim analyses and potential early stopping for efficacy or futility. While these adaptations improve trial efficiency and ethical considerations, they also introduce bias into the adapted analyses. We demonstrate how failing to account for informative interim decisions in the analysis can substantially affect posterior estimates of the treatment effect, often resulting in overly optimistic credible intervals aligned with the stopping decision. Drawing on infor…

Is Repeated Bayesian Interim Analysis Consequence-Free?
Suyu Liu, Beibei Guo, Laura Thompson, Lei Nie, Ying Yuan
https://arxiv.org/abs/2508.07403 https://a…

Is Repeated Bayesian Interim Analysis Consequence-Free?
Interim analyses are commonly used in clinical trials to enable early stopping for efficacy, futility, or safety. While their impact on frequentist operating characteristics is well studied and broadly understood, the effect of repeated Bayesian interim analyses - when conducted without appropriate multiplicity adjustment - remains an area of active debate. In this article, we provide both theoretical justification and numerical evidence illustrating how such analyses affect key inferential pro…