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3,090 papers
Under a manifold model with the frame bundle structure, it is shown that it can accurately recover the parallel transport with landmark-constrained diffusion from a point cloud, and hence asymptotically LA-VDM converges to the connection Laplacian.
Numerical experiments demonstrate competitive accuracy and lower computational cost than expectation-maximization across a range of multidimensional settings.
This work introduces an RKHS localization method that learns a data-adaptive weight from covariates and reformulates the target conditional moment at the target point as a weighted unconditional moment, yielding a prediction-powered estimator and confidence interval that reduce variance when the predictor is informativ...
This work develops an assumption-lean partial identification framework that uses such measurements as weak shadow variables, defined as outcome-informative proxies that are conditionally independent of missingness given the true outcome and observed covariates.
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Under \(Q\)-function realizability and local regularity, it is established that soft control locally inherits the contraction of policy evaluation in a discounted-occupancy norm, and local contraction and finite-sample convergence with estimated ratios are established.
Combining occupancy-weighted FQE with fitted occupancy-ratio evaluation gives an end-to-end guarantee governed by the complexities and direct approximation errors of the value-function and occupancy-ratio classes, removing the need for Bellman completeness.
This tutorial review synthesizes recent developments on diffusion models for SBI, covering design choices for training, inference, and evaluation, and highlights opportunities created by various concepts such as guidance, score composition, flow matching, consistency models, and joint modeling.
We propose a novel test for assessing partial effects in Fr\'echet regression with responses lying on the Bures-Wasserstein manifold. Under the null hypothesis, we show that the statistic admits a degenerate V-statistic approximation whose limiting distribution is a weighted mixture of chi-squared random variables, wit...
This paper addresses the problem of optimally estimating the watermark proportion in mixed-source texts, and proposes efficient estimators for this class of methods, and derive minimax lower bounds for any measurable estimator based on pivotal statistics, showing that their estimators achieve these lower bounds.
Pairing the FVAE and FAE objectives with neural operator architectures that can be evaluated on any mesh enables new applications of autoencoders to inpainting, superresolution, and generative modelling of scientific data.
A fundamental lower bound on the number of samples required to correctly identify the number of components with high probability is proved, showing that this limit depends critically on the separation between component means and the total number of components.
PQMass provides a statistically rigorous method for assessing the performance of a single generative model or the comparison of multiple competing models and scales well to moderately high-dimensional data and thus obviates the need for feature extraction in practical applications.
General Science
Accelerating the search for novel antimicrobial peptides using Google Antigravity for Science.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
General Science
General Science
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