Search Results for author: Robert E. Tillman

Found 7 papers, 0 papers with code

Guided Discrete Diffusion for Electronic Health Record Generation

no code implementations18 Apr 2024 Jun Han, Zixiang Chen, YongQian Li, Yiwen Kou, Eran Halperin, Robert E. Tillman, Quanquan Gu

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.

Attribute Data Augmentation

Temporal Motifs for Financial Networks: A Study on Mercari, JPMC, and Venmo Platforms

no code implementations18 Jan 2023 Penghang Liu, Rupam Acharyya, Robert E. Tillman, Shunya Kimura, Naoki Masuda, Ahmet Erdem Sarıyüce

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.

Fraud Detection

Generative Models with Information-Theoretic Protection Against Membership Inference Attacks

no code implementations31 May 2022 Parisa Hassanzadeh, Robert E. Tillman

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

no code implementations14 Apr 2020 Chirag Nagpal, Robert E. Tillman, Prashant Reddy, Manuela Veloso

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.

Bayesian Inference

Heuristics for Link Prediction in Multiplex Networks

no code implementations9 Apr 2020 Robert E. Tillman, Vamsi K. Potluru, Jiahao Chen, Prashant Reddy, Manuela Veloso

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.

Link Prediction Vocal Bursts Type Prediction

Nonlinear directed acyclic structure learning with weakly additive noise models

no code implementations NeurIPS 2009 Arthur Gretton, Peter Spirtes, Robert E. Tillman

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

no code implementations NeurIPS 2008 David Danks, Clark Glymour, Robert E. Tillman

In many domains, data are distributed among datasets that share only some variables; other recorded variables may occur in only one dataset.

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