Africa
Symplectic Spectrum Gaussian Processes: Learning Hamiltonians from Noisy and Sparse Data
Recent works have parameterized the Hamiltonian by machine learning models (e.g., neural networks), allowing Hamiltonian dynamics to be
Model-Based Imitation Learning for Urban Driving
MILE is action-conditioned, allowing us to model how other agents respond to ego-actions. We show that our model can predict plausible and diverse futures from latent states and actions over long time horizons.
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
Multivariate time-series forecasting plays a crucial role in many real-world applications.
Biologically plausible solutions for spiking networks with efficient coding
To inform the understanding of the function and dynamics of biological networks in the brain, however, the mathematical models have to be informed by biology and obey the same constraints as biological networks.
Scheduling jobs with stochastic holding costs
This question is motivated by several applications.
CO-Optimal Transport
COOT leads to performance improvements over the state-of-the-art methods.
Support Recovery of Sparse Signals from a Mixture of Linear Measurements
Both of these problems have also received attention recently.