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Found 6 papers, 3 papers with code
Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learning
In many machine learning applications, labeling datasets can be an arduous and time-consuming task.
On Robust Probabilistic Principal Component Analysis using Multivariate $t$-Distributions
Probabilistic principal component analysis (PPCA) is a probabilistic reformulation of principal component analysis (PCA), under the framework of a Gaussian latent variable model.
Conditional Density Estimation via Weighted Logistic Regressions
Compared to the conditional mean as a simple point estimator, the conditional density function is more informative to describe the distributions with multi-modality, asymmetry or heteroskedasticity.
Nonparametric Conditional Density Estimation In A Deep Learning Framework For Short-Term Forecasting
We propose a technique that simultaneously estimates the entire conditional distribution and flexibly allows for machine learning techniques to be incorporated.
Deep Distribution Regression
Due to their flexibility and predictive performance, machine-learning based regression methods have become an important tool for predictive modeling and forecasting.
Bayesian Regression Using a Prior on the Model Fit: The R2-D2 Shrinkage Prior
The proposed method compares favourably to previous approaches in terms of both concentration around the origin and tail behavior, which leads to improved performance both in posterior contraction and in empirical performance.
Methodology
