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3,686 papers
The quadrature field is introduced, a set-equivariant network that maps an observation and its posterior samples to an $M$-node signed-weight quadrature in one forward pass and is validated on closed-form and on learned posteriors, where it improves on the Monte-Carlo estimate in median at every node count, often by or...
Unobserved confounding is a key challenge when estimating causal effects from a treatment on an outcome. In this work, we assume that we observe a single, potentially multi-dimensional proxy variable of the unobserved confounder and that we know the mechanism that generates the proxy from the confounder. Under an assum...
This work develops a mathematical framework based on probability and optimal transport that provides a mathematical characterization of the expressive power of Transformers for contextual relations and shows how the choice of normalization determines the probabilistic structure of the relations represented by attention...
This work introduces latent subspaces in the exponential parameter space of product manifolds of categorical distributions as a novel approach to learning low-dimensional representations of high-dimensional discrete data.
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This work considers a class of statistical inverse problems involving the estimation of a regression operator from a Polish space to a separable Hilbert space, where the target lies in a vector-valued reproducing kernel Hilbert space induced by an operator-valued kernel.
Federated Bayesian neural networks require fixing a prior on the model parameters, which is notoriously difficult, and misspecification of this prior can severely degrade accuracy and calibration. Motivated by the rapid progress of predictive models, the martingale posterior, also known as predictive Bayes, replaces th...
This monograph gives a statistical perspective on algorithm design and complexity for interactive decision making, building from multi-armed bandits through contextual and structured bandits to reinforcement learning with function approximation within a single, unified framework.
Recent literature has shown a strong connection between optimization and sampling. We develop the corresponding first-order theory for diffusion models. First, the SDE-based reverse-time flows of overdamped and underdamped Langevin diffusions contract relative Fisher divergences at explicit exponential rates whenever t...
A simulation-based inference framework for rough Heston (rHeston) calibration that learns the posterior distribution of the model parameters conditional on an IV surface and introduces Hellinger-SHAP, an information-theoretic explainability method for posterior inference.
Outliers are essential for evaluating and improving the robustness of machine learning systems, especially when future distributions may differ significantly from historical training data. In high-stakes applications, robustness often depends on rare cases that finite datasets fail to capture, making simple resampling...
These results identify conditions under which the resulting predictor exploits local geometry and attains the aggregate minimax rate, and derive an in-context generalization bound for near empirical risk minimizers over this class.
Bayesian TAGs are demonstrated to be well suited to quantifying uncertainty in equation discovery for dynamical systems and to fitting physics-informed models.
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
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Health & Bioscience
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Data Mining & Modeling
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· 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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