5 accepted papers
It is known that gradient based MCMC samplers for continuous spaces, such as Langevin Monte Carlo (LMC), can be derived as particle versions of a gradient flow that minimizes KL divergence on a Wasserstein manifold. The superior efficiency of such samplers has motivated several recent attempts to ge…
This paper presents a method for task-oriented path planning and collision avoidance for a group of heterogeneous holonomic mobile sensors. It is a generalization of the authors' prior work on diffusion-based path planning. The proposed variant allows one to plan paths in environments cluttered with…
We propose a memory-constrained partition-based method to extract symbolic representations of the belief state and its dynamics in order to solve planning problems in a partially observable Markov decision process (POMDP). Our K-means partitioning strategy uses a fixed number of symbols to represent…
Learning nonlinear dynamics from aggregate data is a challenging problem because the full trajectory of each individual is not available, namely, the individual observed at one time may not be observed at the next time point, or the identity of individual is unavailable. This is in sharp contrast to…
User behaviors in social networks are microscopic with fine grained temporal information. Predicting a macroscopic quantity based on users’ collective behaviors is an important problem. However, existing works are mainly problem-specific models for the microscopic behaviors and typically design appr…