Statistical Learning
EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records Gyubok Lee
We present a new text-to-SQL dataset for electronic health records (EHRs).
A Standard Maximum Likelihood Estimation and Links to I
In the standard MLE setting [see, e.g., Murphy, 2012, Ch. 9] we are interested in learning the These two definitions are, however, essentially equivalent. Eq. (15) is a smooth objective that can be optimized with a (stochastic) gradient descent procedure. This section contains the proofs of the results relative to the perturb and map section (Section 3.2) and The proposition now follows from arguments made in Papandreou and Y uille [2011] Its moment generating function has the form E[exp(tX)] = Γ(1 τt). As mentioned in Johnson and Balakrishnan [p. Parts of the proof are inspired by a post on stackexchange Xi'an [2016].Theorem 1.
Reliable Graph Neural Networks via Robust Aggregation
This work is motivated by the observation that adversarially injected edges effectively can be viewed as additional samples to a node's neighborhood aggregation
Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction Supplementary Material
To get a finer assessment of the individual impacts of each sub-term of ClassNeRV stress (Equation 3), we perform an ablation study.
Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction
DR may be supervised by taking advantage of class information.