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3,686 papers
This work builds a score-calibrated robustness framework that converts any fixed point predictor into a decision-relevant uncertainty representation through distribution-free conformal calibration, and shows that conformal scores endow fixed black-box predictors with an interpretable uncertainty scale for downstream de...
Comparing probability measures in machine learning trades transport geometry against computational cost: Wasserstein distances encode the geometry of $\mathbb R^d$ but require solving a transport problem, while kernel discrepancies are cheap to evaluate yet depend delicately on their test class. We study the Toscani--F...
A novel Bayesian VFL framework for multivariate high-dimensional reduced-rank regression, termed BayesVFLReg, which enables precise coefficient estimation while safeguarding both feature and response privacy and is computationally scalable for large, high-dimensional datasets and facilitates efficient variable selectio...
The central limit theorem and consistency of the fully transcript-based sandwich covariance and intrinsic Wald region under local differential privacy are proved and the predicted decrease in estimation error and near-nominal coverage under moderate privacy is supported.
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Reinforcement learning with verifiable rewards with verifiable rewards often improves pass@1 while falling behind its base model at larger sampling budgets, a crossover read as evidence that RLVR only sharpens existing capability is identified.
This tutorial provides a unified view of the emerging area of bilinear learning and control. Using linear systems as a benchmark, it explains what fundamentally changes in the bilinear settings, how recent theory addresses finite-sample learning and control, and how these ideas connect to broader themes in nonlinear co...
Linear probing is the standard instrument for detecting social biases in the hidden representations of large language models. Yet reported probe accuracies come almost exclusively from \emph{counterfactual} evaluations in which every input carries an explicit demographic marker. Once only a fraction $\alpha$ of inputs...
Large language models increasingly rely on long chain-of-thought (CoT) trajectories for complex reasoning, but autoregressive generation brings substantial memory and inference costs. Latent reasoning models (LRMs) offer a more efficient alternative by compressing intermediate reasoning into a small number of continuou...
Decode-time alignment methods steer a frozen language model by scoring candidate continuations with an external reward and selecting the maximiser. We argue that this shared design is a single degenerate point in a much larger space. We introduce Swiss-Knife, a framework for externalised multi-objective alignment in wh...
Hierarchical data is ubiquitous in the empirical sciences and is most commonly analyzed with generalized linear mixed-effects models (GLMMs). Bayesian inference for GLMMs yields calibrated uncertainty but requires MCMC; the No-U-Turn Sampler (NUTS) is the gold standard but is slow and must restart from scratch for ever...
Combining predictions from different models can improve performance at machine learning tasks, but the training of the individual models and the rule used to combine them are typically chosen separately, and by ad hoc means. Recent advances in distributional optimisation (i.e. where the optimisation occurs over the set...
This work introduces the Iterative Atom Refinement algorithm, a simple procedure for recovering individual dictionary atoms, and presents a rigorous convergence theory of IAR, showing that a small initial advantage of one atom is amplified until that atom is isolated.
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Health & Bioscience
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Data Mining & Modeling
GPT-Lab
· gpt-lab.eu
Nov 3, 2026
Is your company’s AI sovereign? A practical guide for CIOs on AI vendor lock-in, EU Data Act switching, cloud sovereignty, and testing your exit plan before you need it. The post Sovereignty Is the Option to Leave appeared first on GPT-Lab.
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