The Jensen–Shannon divergence is a renowned bounded symmetrization of the
unbounded Kullback–Leibler divergence which measures the total Kullback–Leibler …

Efficient exploration is an unsolved problem in Reinforcement Learning which is usually
addressed by reactively rewarding the agent for fortuitously encountering novel situations …

In this paper, we present a unified framework for the rigid and nonrigid point set registration
problem in the presence of significant amounts of noise and outliers. The key idea of this …

We focus on the distribution regression problem: regressing to vector-valued outputs from
probability measures. Many important machine learning and statistical tasks fit into this …

In this paper, we extend the notion of Cauchy-Schwarz divergence to point processes and
establish that the Cauchy-Schwarz divergence between the probability densities of two …

In outdoor environments shadows are common. These typically strong visual features cause
considerable change in the appearance of a place, and therefore confound vision-based …

We focus on the distribution regression problem: regressing to a real-valued response from
a probability distribution. Although there exist a large number of similarity measures …

This paper describes a novel method for determining the extrinsic calibration parameters
between 2D and 3D LIDAR sensors with respect to a vehicle base frame. To recover the …

We consider the setting of an agent with a fixed body interacting with an unknown and
uncertain external world. We show that models trained to predict proprioceptive information …

The Kullback-Leibler (KL) divergence between two mixture models is a fundamental
primitive in many signal processing tasks. Since the KL divergence of mixtures does not …