arXiv:2605.27991v3 Announce Type: replace Abstract: Gradient-flow optimization is usually viewed as an algorithmic procedure for minimizing empirical loss, with training duration selected by validation or heuristic…
arXiv:2607.01545v1 Announce Type: cross Abstract: The inverse scattering transform (IST) provides the standard theoretical framework for deriving soliton dynamics. Traditionally, such derivations have been of an…
arXiv:2209.04942v2 Announce Type: replace Abstract: Problem definition: This paper studies the problem of estimating consumer preferences from bundle sales data. Product bundling is a widely used…
arXiv:2607.01492v1 Announce Type: cross Abstract: Recent work has established a fundamental trilemma between Byzantine robustness, local differential privacy (LDP), and optimization error in distributed learning. We…
arXiv:2607.02328v1 Announce Type: cross Abstract: For training-data-based model risk prediction, $K$-fold cross-validation~(CV) is widely used to mitigate the well-known over-optimism of the empirical risk and is…
arXiv:2607.01487v1 Announce Type: cross Abstract: We propose a scaling law that takes into account model size and training data while explicitly splitting the latter into training…
arXiv:2607.02173v1 Announce Type: cross Abstract: Prediction under label shift becomes nonstandard when responses are censored. In a two-sided censored Gaussian model, latent values below $L$ and…
arXiv:2607.01417v1 Announce Type: cross Abstract: Conditional inference trees (CIT) and conditional inference forests (CIF) reduce split-selection bias by testing features before choosing split thresholds, but repeated…
arXiv:2603.27631v2 Announce Type: replace-cross Abstract: Self-supervised pre-training, where large corpora of unlabeled data are used to learn representations for downstream fine-tuning, has become a cornerstone of…
arXiv:2603.02196v3 Announce Type: replace-cross Abstract: An agent must try new behaviors to explore and improve. In high-stakes environments, an agent that violates safety constraints may cause…
arXiv:2607.01945v1 Announce Type: new Abstract: The classical $k$-means clustering cannot be directly used to incomplete data, and existing $k$-means-based clustering for missing data primarily focus on…
arXiv:2607.01959v1 Announce Type: new Abstract: We propose a model agnostic methodology to measure lag relevance in machine learning forecasting models applied to univariate time series. Particularly,…
arXiv:2607.02003v1 Announce Type: new Abstract: Although neural networks are remarkably effective, their underlying optimization principles remain theoretically elusive, often characterized by non-convex landscapes and stochastic heuristics.…
arXiv:2607.02212v1 Announce Type: new Abstract: Aqueous solubility is a key property in early-stage drug discovery, but most predictive models merge physicochemical descriptors and molecular graph information…
arXiv:2607.01741v1 Announce Type: new Abstract: Reinforcement Learning (RL) is a sequential decision-making framework in which an agent learns optimal policies through interaction with an environment by…
arXiv:2607.02247v1 Announce Type: cross Abstract: The aggregation with exponential weights (AEW) estimator is not fully understood in the basic setting of model selection aggregation with squared…
arXiv:2606.21199v2 Announce Type: replace Abstract: We introduce a semi-parametric framework for nonlinear system identification, which decouples discrepancy functions from physics-based components. Orthogonal Gaussian process regression balances…
arXiv:2512.07541v3 Announce Type: replace Abstract: Inspired by graph-based methodologies, we introduce a novel graph-spanning algorithm designed to identify changes in both offline and online data across…
arXiv:2510.27066v3 Announce Type: replace-cross Abstract: Weather extremes pose major societal risks, especially in a changing climate, but due to their rarity, they are difficult to study…
arXiv:2607.01483v1 Announce Type: new Abstract: The ''Computational Life'' paper (Ag"uera y Arcas et al., 2024) argues that paired interactions in a computational soup are an effective…
arXiv:2607.01539v1 Announce Type: new Abstract: This paper studies whether the Metabolic Multi-Agent Optimizer (MMAO) can act as a credible outer-loop optimizer for classification model selection. We…
arXiv:2607.01551v1 Announce Type: new Abstract: This paper studies the Metabolic Multi-Agent Optimizer (MMAO) at the framework level rather than at the implementation or benchmark level. The…
arXiv:2607.02082v1 Announce Type: new Abstract: Wave Function Collapse (WFC) is a widely used procedural content generation method that learns local adjacency constraints from example inputs to…
arXiv:2607.02122v1 Announce Type: new Abstract: Electronic neurons are a keystone for construction of the spiking neural networks which have numerous applications in neuroprosthetics, artificial memory, intensive…