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Found 8 papers, 6 papers with code
Latent variable model for high-dimensional point process with structured missingness
Longitudinal data are important in numerous fields, such as healthcare, sociology and seismology, but real-world datasets present notable challenges for practitioners because they can be high-dimensional, contain structured missingness patterns, and measurement time points can be governed by an unknown stochastic process.
Estimating treatment effects from single-arm trials via latent-variable modeling
Compared to previous methods, our results show improved performance both for direct treatment effect estimation as well as for effect estimation via patient matching.
Evidential Turing Processes
A probabilistic classifier with reliable predictive uncertainties i) fits successfully to the target domain data, ii) provides calibrated class probabilities in difficult regions of the target domain (e. g.\ class overlap), and iii) accurately identifies queries coming out of the target domain and rejects them.
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
Neural Stochastic Differential Equations model a dynamical environment with neural nets assigned to their drift and diffusion terms.
Deep Active Learning with Adaptive Acquisition
As active learning is a scarce data regime, we bootstrap from a well-known heuristic that filters the bulk of data points on which all heuristics would agree, and learn a policy to warp the top portion of this ranking in the most beneficial way for the character of a specific data distribution.
Bayesian Evidential Deep Learning with PAC Regularization
We propose a novel method for closed-form predictive distribution modeling with neural nets.
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation
We propose a new Bayesian Neural Net formulation that affords variational inference for which the evidence lower bound is analytically tractable subject to a tight approximation.
Variational Bayesian Multiple Instance Learning With Gaussian Processes
Gaussian Processes (GPs) are effective Bayesian predictors.
