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Found 5 papers, 1 papers with code
Variational Shapley Network: A Probabilistic Approach to Self-Explaining Shapley values with Uncertainty Quantification
Shapley values have emerged as a foundational tool in machine learning (ML) for elucidating model decision-making processes.
Maximum Likelihood Estimation of Flexible Survival Densities with Importance Sampling
We propose a survival analysis approach which eliminates the need to tune hyperparameters such as mixture assignments and bin sizes, reducing the burden on practitioners.
A Coreset-based, Tempered Variational Posterior for Accurate and Scalable Stochastic Gaussian Process Inference
We present a novel stochastic variational Gaussian process ($\mathcal{GP}$) inference method, based on a posterior over a learnable set of weighted pseudo input-output points (coresets).
What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization
Summarization of clinical narratives is a long-standing research problem.
Zero-Shot Clinical Acronym Expansion via Latent Meaning Cells
We introduce Latent Meaning Cells, a deep latent variable model which learns contextualized representations of words by combining local lexical context and metadata.
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