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3,686 papers
The results highlight the potential of MICE-based methods in creating high-fidelity tabular data while offering lower implementation complexity compared to deep generative models.
We study the design of interventions for causal discovery in simple structural causal models whose causal graphs are directed mixed graphs (DMGs) that may contain directed cycles and bidirected edges representing latent confounding. In such case, observational conditional-independence (CI) information may not identify...
This work proposes Adaptively Truncated Signature-based Logistic Regression (ATSLR), a semi-parametric classification framework that integrates path-signature representations with a data-driven procedure for selecting the signature truncation order via penalized empirical risk minimization and highlights the practical...
We propose a novel nonparametric regression method that models complex input-output relationships as the sum of convex and concave components. The method-Identifiable Convex-Concave Nonparametric Least Squares (ICCNLS)-decomposes the target function into additive shape-constrained components, each represented via sub-g...
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This work revisits decorrelation and regularization by presenting a systematic analysis of out-of-sample mean-squared error (MSE) for different SNR scenarios and finds that, for both bagging and random forests, bias can be significantly reduced in the presence of correlated covariates.
A novel framework for free energy estimation that leverages learned transports implemented via stochastic interpolants and provides consistent, minimum-variance estimators based on escorted Jarzynski equality and controlled Crooks theorem, alongside variational upper and lower bounds on free energy differences.
A classical assumption for analyzing stochastic gradient algorithms where the squared norm of the stochastic subgradient is allowed to grow as fast as the squared norm of the optimization variable is revisited.
We investigate the generalization performance of online stochastic gradient descent (SGD) for overparameterized two-layer neural networks under the Neural Tangent Kernel (NTK) regime. Leveraging the NTK approximation together with the convergence theory of stochastic approximation in reproducing kernel Hilbert spaces (...
This work provides a refined analysis for inexact Halpern iteration that relaxes the required inexactness level to improve some state-of-the-art complexity results even for constrained stochastic convex-concave min-max problems.
The analysis separates fine-scale comparisons from the regional information needed to allocate their samples at low regret, which separates fine-scale comparisons from the regional information needed to allocate their samples at low regret.
Experimental results show that the proposed hyperbolic manifold constrained tabular neural network is effective, and HTNN ranks first among 36 methods on 200 classification datasets and third among 34 methods on 100 regression datasets.
This work analyses contraction in an observable predictive-law quotient that identifies hidden states producing the same future sequence of emitted Gaussian laws producing the same future sequence of emitted Gaussian laws.
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
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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