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Found 9 papers, 3 papers with code
Translating Natural Language Queries to SQL Using the T5 Model
This paper presents the development process of a natural language to SQL model using the T5 model as the basis.
Federated Causal Inference from Observational Data
In this article, we propose a framework to estimate causal effects from decentralized data sources.
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects
We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources in a federated setting.
Federated Estimation of Causal Effects from Observational Data
Many modern applications collect data that comes in federated spirit, with data kept locally and undisclosed.
Generative Parameter Sampler For Scalable Uncertainty Quantification
Uncertainty quantification has been a core of the statistical machine learning, but its computational bottleneck has been a serious challenge for both Bayesians and frequentists.
Simulation and Calibration of a Fully Bayesian Marked Multidimensional Hawkes Process with Dissimilar Decays
We propose a simulation method for multidimensional Hawkes processes based on superposition theory of point processes.
Variational Inference for Gaussian Process with Panel Count Data
We present the first framework for Gaussian-process-modulated Poisson processes when the temporal data appear in the form of panel counts.
A Tutorial on Hawkes Processes for Events in Social Media
This chapter provides an accessible introduction for point processes, and especially Hawkes processes, for modeling discrete, inter-dependent events over continuous time.
Hawkes Processes with Stochastic Excitations
We propose an extension to Hawkes processes by treating the levels of self-excitation as a stochastic differential equation.
