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Found 7 papers, 0 papers with code
Guided Discrete Diffusion for Electronic Health Record Generation
Electronic health records (EHRs) are a pivotal data source that enables numerous applications in computational medicine, e. g., disease progression prediction, clinical trial design, and health economics and outcomes research.
Temporal Motifs for Financial Networks: A Study on Mercari, JPMC, and Venmo Platforms
For the Venmo network, we investigate the interplay between financial and social relations on three tasks: friendship prediction, vendor identification, and analysis of temporal cycles.
Generative Models with Information-Theoretic Protection Against Membership Inference Attacks
Deep generative models, such as Generative Adversarial Networks (GANs), synthesize diverse high-fidelity data samples by estimating the underlying distribution of high dimensional data.
Bayesian Consensus: Consensus Estimates from Miscalibrated Instruments under Heteroscedastic Noise
We consider the problem of aggregating predictions or measurements from a set of human forecasters, models, sensors or other instruments which may be subject to bias or miscalibration and random heteroscedastic noise.
Heuristics for Link Prediction in Multiplex Networks
Through experiments with simulated and real world scientific collaboration, transportation and global trade networks, we demonstrate that the proposed heuristics show increased performance with the richness of connection type correlation structure and significantly outperform their baseline heuristics for ordinary networks with a single connection type.
Nonlinear directed acyclic structure learning with weakly additive noise models
This results in a more computationally efficient approach that is useful for arbitrary distributions even when additive noise models are invertible.
Integrating Locally Learned Causal Structures with Overlapping Variables
In many domains, data are distributed among datasets that share only some variables; other recorded variables may occur in only one dataset.
