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Found 7 papers, 0 papers with code
Regression with Sensor Data Containing Incomplete Observations
This leads to a bias toward lower values in labels and the resultant learning because labels may have lower values due to incomplete observations, even if the actual magnitude of the phenomenon was high.
Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction
The MET of non-communicable diseases like diabetes is highly correlated to cumulative health conditions, more specifically, how much time the patient spent with specific health conditions in the past.
Variational Inference for Discriminative Learning with Generative Modeling of Feature Incompletion
We are concerned with the problem of distributional prediction with incomplete features: The goal is to estimate the distribution of target variables given feature vectors with some of the elements missing.
Regression from Upper One-side Labeled Data
We then derive a learning algorithm in an unbiased and consistent manner to ordinary regression that is learned from data labeled correctly in both upper- and lower-side cases.
Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data
To deal with this difficulty, we formulate the task as a weakly supervised learning.
Time-Discounting Convolution for Event Sequences with Ambiguous Timestamps
This paper proposes a method for modeling event sequences with ambiguous timestamps, a time-discounting convolution.
Consistent Nonparametric Different-Feature Selection via the Sparsest $k$-Subgraph Problem
We show that the proposed method is computationally efficient and does not require any extra computation for model selection.
