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08/24/2023
Federated Learning of Causal Effects from Incomplete Observational Data
Decentralized and incomplete data sources are prevalent in real-world ap...
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01/01/2023
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects
We propose a new causal inference framework to learn causal effects from...
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06/28/2021
Exact simulation of extrinsic stress-release processes
We present a new and straightforward algorithm that simulates exact samp...
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05/31/2021
Federated Estimation of Causal Effects from Observational Data
Many modern applications collect data that comes in federated spirit, wi...
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08/07/2020
Bayesian causal inference for count potential outcomes
The literature for count modeling provides useful tools to conduct causa...
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05/28/2019
Generative Parameter Sampler For Scalable Uncertainty Quantification
Uncertainty quantification has been a core of the statistical machine le...
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03/13/2018
Simulation and Calibration of a Fully Bayesian Marked Multidimensional Hawkes Process with Dissimilar Decays
We propose a simulation method for multidimensional Hawkes processes bas...
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03/12/2018
Variational Inference for Gaussian Process with Panel Count Data
We present the first framework for Gaussian-process-modulated Poisson pr...
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08/21/2017
A Tutorial on Hawkes Processes for Events in Social Media
This chapter provides an accessible introduction for point processes, an...
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09/22/2016