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3,822 papers
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.
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.
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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.
The framework of generative flow networks (GFlowNets) is adopted to tackle two core problems in phylogenetics: parsimony-based and Bayesian phylogenetic inference and it is demonstrated that the amortized posterior sampler, PhyloGFN, produces diverse and high-quality evolutionary hypotheses on real benchmark datasets.
It is discovered that the energy benefits of quantum computing economies are contingent on large-scale computation, and quantum computing may represent a more sustainable pathway for the computing industry.
A new point process model for discrete-event data over networks is presented, built upon Hawkes' classic influence-kernel formulation to capture the effects of historical events on the occurrence of future events.
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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