Technology
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
SQ Lower Bounds for Learning Mixtures of Linear Classifiers
We study the problem of learning mixtures of linear classifiers under Gaussian covariates.
ProvableRepresentationLearningforImitation withContrastiveFourierFeatures
Inthiswork,wefocuson imitation learning, where the aim is to learn how to act in the environment to match the behavior of some unknown targetpolicy [25].
Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks
For deep linear networks, these kernels satisfy a set of algebraic matrix equations. For nonlinear networks, we provide an alternating sampling procedure to self-consistently solve for the kernel order parameters.
