Hyperimpute: Generalized iterative imputation with automatic model selection
Consider the problem of imputing missing values in a dataset. One the one hand,
conventional approaches using iterative imputation benefit from the simplicity and …
conventional approaches using iterative imputation benefit from the simplicity and …
Leveraging variational autoencoders for multiple data imputation
Missing data persists as a major barrier to data analysis across numerous applications.
Recently, deep generative models have been used for imputation of missing data, motivated …
Recently, deep generative models have been used for imputation of missing data, motivated …
Active acquisition for multimodal temporal data: A challenging decision-making task
We introduce a challenging decision-making task that we call active acquisition for
multimodal temporal data (A2MT). In many real-world scenarios, input features are not …
multimodal temporal data (A2MT). In many real-world scenarios, input features are not …
Interpolation of missing swaption volatility data using variational autoencoders
I Richert, R Buch - Behaviormetrika, 2024 - Springer
Albeit of crucial interest for financial researchers, market-implied volatility data of European
swaptions often exhibit large portions of missing quotes due to illiquidity of the underlying …
swaptions often exhibit large portions of missing quotes due to illiquidity of the underlying …
Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information
Knowing which features of a multivariate time series to measure and when is a key task in
medicine, wearables, and robotics. Better acquisition policies can reduce costs while …
medicine, wearables, and robotics. Better acquisition policies can reduce costs while …