Uncertainty
Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net
The lasso and elastic net linear regression models impose a double-exponential prior distribution on the model parameters to achieve regression shrinkage and variable selection, allowing the inference of robust models from large data sets.
AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling
Bichuan Guo, Yuxing Han, Jiangtao Wen
Neural Information Processing Systems http://nips.cc/
Credal Learning Theory
Statistical learning theory is the foundation of machine learning, providing theoretical bounds for the risk of models learned from a (single) training set, assumed to issue from an unknown probability distribution.
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We study whether and how can we model a joint distributionp(x,z)using two conditional modelsp(x|z) and q(z|x) that form a cycle.
On the Generative Utility of Cyclic Conditionals Chang Liu 1, Haoyue T ang 2, Tao Qin
Work done during an internship at Microsoft Research Asia.
Learning nonlinear level sets for dimensionality reduction in function approximation
Guannan Zhang, Jiaxin Zhang, Jacob Hinkle
Neural Information Processing Systems http://nips.cc/
Canonical normalizing flows for manifold learning
The embedding of such a manifold into the high-dimensional space of the data is achieved via learnable invertible transformations.
GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series
Modeling real-world multidimensional time series can be particularly challenging when these are sporadically observed ( i.e., sampling is irregular both in time