Linear meta-learning surrogates trained across chemical objectives and auxiliary properties adapt rapidly to new multi-objective molecular searches and outperform baselines by 78% in Pareto performance on spin-crossover complexes.
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URL https://projecteuclid.org/euclid.aos/1176345802
27 Pith papers cite this work. Polarity classification is still indexing.
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Direct fixed-weight solver for free-support Wasserstein medians relocates atoms using OT barycentric projections and inverse-distance weights, achieving monotone descent on smoothed objectives with fewer subproblems than nested Weiszfeld baselines.
Proposes a scale-calibrated median-of-means estimator for robust aggregation of distributed PCA estimates on the product of Euclidean space and Grassmann manifold.
A mixture model with adaptive KDE and per-image cross-validation raises estimated human fixation consistency by 5-15% median log-likelihood and up to 2 AUC points over fixed-bandwidth Gaussian baselines.
The profile maximum likelihood estimator for the location in anisotropic hyperbolic wrapped normal models is strongly consistent, asymptotically normal, and attains the Hájek-Le Cam minimax lower bound under squared geodesic loss.
Newton's recursive mixture estimator is a discrete gradient flow on the Fisher-Rao manifold of probability measures.
Reformulating local polynomial fitting with orthogonal Chebyshev polynomials yields two algorithms that cut memory use, improve scalability, and deliver orders-of-magnitude better numerical accuracy than Vandermonde-based methods for Savitzky-Golay filters.
A method called the degeneracy distillery uses symbolic transformations to flatten the Fisher information matrix globally from simulations alone, identifying independent parameter combinations and reducing neural posterior estimation simulation budgets by up to 10x.
Kolmogorov n-width theory plus PRESS statistics yield closed-form optimal spline resolution; KORE estimates bias/noise scales from two pilots and matches CV performance with far fewer fits.
Predictively consistent priors let complex Bayesian models match or beat the out-of-sample performance of selected simpler models across linear, logistic, and nonlinear examples without explicit selection.
Optimally weighted SLS and degree-2 PMM are the same population estimating equation for linear regression with conditionally homoskedastic non-Gaussian errors, sharing influence function and asymptotic variance c2 g2 / N.
Generative models for cosmological field-level inference can reproduce posterior means and cross-correlations yet fail to capture correct uncertainty geometry when validated against HMC reference samples.
Rectified AI priors, obtained by correcting AI-induced data laws before embedding them in techniques like Dirichlet process priors, reduce bias, improve credible interval coverage, and boost performance in tasks like skin disease classification.
Joint location-scale minimization for geometric medians on product manifolds degenerates to marginal medians, and three new scale-selection methods restore identifiability with asymptotic guarantees.
A Bayesian model for multi-feature contact matrices that uses tensor structures and contingency table theory to satisfy structural constraints and impute missing contact features, validated on simulations and US/German survey data.
On infinite bounded-degree graphs, divisible sandpiles with i.i.d. initial masses of mean μ stabilize almost surely if μ < 1 and masses have finite p-moment for p > 3, but explode if μ ≥ 1; the conditions are nearly sharp via counterexamples on other graphs.
Distributionally robust k-means minimizes worst-case squared distance over a Wasserstein-2 ball around the empirical distribution, yielding a tractable soft-clustering algorithm with monotonic block coordinate descent and local linear convergence.
A parity-augmented ANOVA decomposition is established for functions on the sphere using orthogonal bases to capture geometry-induced variable dependencies.
Sobolev-ball constraints on the hypothesis class achieve minimax rates for score estimation on the torus and, under conjecture, for generative modeling.
A multiscale optimization method using explicit protein backbone geometry reconstructs atomic models from cryo-EM data, showing improved RMSD and TM scores on three simulated datasets.
A review reframing density estimation as 'density evolution' across scales, linking kernel smoothing to heat flow, mixtures to compression, and topology to level sets, while stating three structural results on modes, Gaussian semigroups, and log-concavity.
A weighted K-means plus decision-tree pipeline learns multi-action policies from observational data and is applied to HCV treatment choices for HIV co-infected patients, finding a high-clearance subgroup and potential cost savings of CAN$3.6-4.9 million.
Adaptive GLM with MQLE and GP regression with UCB for dynamic insurance pricing, showing parameter convergence and regret analysis under delayed claims.
Established mathematical bottlenecks in representation, optimization, complexity, and high-dimensional learning aligned with the central disappointments of early AI research periods.
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Multiscale reconstruction of protein conformations from cryo-EM images
A multiscale optimization method using explicit protein backbone geometry reconstructs atomic models from cryo-EM data, showing improved RMSD and TM scores on three simulated datasets.