Importance weighting is a convenient general way to adjust for draws from the wrong distribution, but the resulting ratio estimate can be noisy when the importance weights have a heavy right tail, as routinely occurs when there are aspects of the target distribution not well captured by the approximating distribution. More stable estimates can be obtained by truncating the importance ratios. He...

Exudates in retinal images are one of the early signs of the vision-threatening diabetic retinopathy and diabetic macular edema. Early diagnosis is very helpful in preventing the progression of the disease. In this work, we propose a fully automatic exudate segmentation method based on the state-of-the-art residual learning framework. With our proposed end-to-end architecture the training is do...

The ability to track a moving vehicle is of crucial importance in numerous applications. The task has been often approached by the importance sampling technique of particle filters due to its ability to model non-linear and non-Gaussian dynamics, of which a vehicle travelling on a road network is a good example. The performance of the particle filters method is strongly dependent on the choice ...

Abstract Off-policy evaluation is the problem of evaluating a decision-making policy using data collected under a different behaviour policy. While several methods are available for addressing off-policy evaluation, little work has been done on identifying the best methods. In this paper, we conduct an in-depth comparative study of several off-policy evaluation methods in non-bandit, finite-hor...

We study the problem of off-policy value evaluation in reinforcement learning (RL), where one aims to estimate the value of a new policy based on data collected by a different policy. This problem is often a critical step when applying RL to real-world problems. Despite its importance, existing general methods either have uncontrolled bias or suffer high variance. In this work, we extend the do...

I this paper, we propose a fast adaptive importance sampling method for the efficient simulation of buffer overflow probabilities in queueing networks. The method comprises three stages. First, we estimate the minimum cross-entropy tilting parameter for a small buffer level; next, we use this as a starting value for the estimation of the optimal tilting parameter for the actual (large) buffer l...

The ability to compute multinormal integrals to any required accuracy is a key issue for an efficient computation of failure probabilities, particular important in context with system reliability analysis. Hence in this paper, an accurate Importance Sampling procedure to compute multinormal integrals in high dimensions is presented. The novel method allows to sample exclusively in the failure d...

In this contribution, we present a simple importance sampling technique to considerably speed up Monte Carlo simulations for bit error rate estimation of orthogonal space-time block coded systems on spatially correlated Nakagami fading channels.

An importance sampling (IS) simulation technique, originally derived by Iltis for Bayesian equalizers, is extended to evaluate the lower-bound bit error rate of the Bayesian decision feedback equalizer (under the assumption of correct decisions being fed back. Using a geometric translation approach, it is shown that the two subsets of opposite-class channel states are always linearly separable....

This paper presents a method for estimating the probability μ of a union of J rare events. The method uses n samples, each of which picks one of the rare events at random, samples conditionally on that rare event happening and counts the total number of rare events that happen. We call it ALORE, for ‘at least one rare event’. The ALORE estimate is unbiased and has a coefficient of variation no ...