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3,686 papers
A finite-sample theory for continuous density ratio estimation and importance-weighted learning under target shift is established and the resulting regression estimator attains the minimax-optimal rates of standard kernel regression.
This work proposes to amortize the graph matching (node alignment) problem and showcases the efficiency of this approach on toy and real world SGP problems of increasing complexity including a novel Mass-spectra to Scaffold task that is introduced.
We study the uniform approximation of smooth scalar-valued functionals on an infinite-dimensional separable Hilbert space by deep ReLU neural networks. Writing the functional input as $X(t)=\sum_{d\geq1}\xi_d\nu_d(t)$, we quantify the importance of coordinate $d$ through $w_ds_d$, where $s_d$ bounds the magnitude of th...
Interference-adjusted weighted conformal prediction is developed that accounts for interference by constructing an observable upper bound on the ideal and unobserved conformal $p$-value under the target intervention.
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Two algorithms, MAGE and SPELL, suited for reconstruction and attribution respectively, are presented, that run on existing metagradient machinery at no extra cost and demonstrate strong performance over existing baselines across training scales and measurement budgets.
For constant-stepsize stochastic approximation (SA), the iterates converge in distribution to a stationary law that depends on the stepsize $\alpha.$ Steady-state convergence (SSC) concerns the limit of the scaled stationary distribution as $\alpha \downarrow 0.$ Existing SSC theory requires i.i.d. or additive noise an...
FUSHAP is proposed, a method that leverages partially-observed auxiliary sites to reduce the variance of a preliminary single-site Shapley estimate without imputation and reduces MSE on multi-site air quality and multi-center clinical data.
This paper introduces a novel framework for Regularized Multivariate Functional Principal Component Analysis (ReMFPCA) via Functional Singular Value Decomposition (SVD). The proposed method extends existing MFPCA approaches by incorporating a generalized functional SVD within a Hilbert space framework, enabling simulta...
This work proposes a paradigm shift from inversion-based inference to a direct matrix constraint framework, which is used to define a joint confidence region and extract marginal intervals via linear programming, deriving provably tighter bounds for importance weights while maintaining exact finite-sample validity.
Constraining reaction rate coefficients is a central challenge in the development of explicit atmospheric chemical mechanisms, particularly for autoxidation systems where many reaction pathways are only indirectly observed through high-resolution mass spectrometry. In this study, we evaluate rate-coefficient optimisati...
Multi-Source Randomly Localized Conformal Prediction is proposed, which builds on the local coverage properties of randomly localized conformal prediction and extends it to multiple sources through data-adaptive source selection and establishes finite-sample coverage bounds using an interpretable notion of envelope dis...
It is proved that an alternating Riemannian gradient scheme converges to a basin saddle point of the penalized DRO problem, recovering the linear and polynomial rates of the abstract theory with all constants explicit in terms of data dimension, loss moments, and the reference covariance.
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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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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