arXiv research

A new HRL method learns hierarchical policies using mutual information maximization.

Improved SAC with AWMP for better control tasks.

A novel approach for safe offline RL using latent safety constraints.

Paper tackles offline meta-reinforcement learning with a new algorithm.

AWR simplifies RL with simple supervised learning.

DG improves policy gradients by weighting actions with a sigmoid of advantage and surprisal.

New method learns policies without limiting to Gaussian distributions.

AWAC combines offline and online data to accelerate RL learning.

Paper proposes a method to improve off-policy reinforcement learning in batch settings.

Importance sampling is often used in machine learning when training and testing data come from different distributions. In this paper we propose a new variant of importance sampling that can reduce the variance of importance sampling-based estimates by orders of magnitude when the supports of the training and testing d…

Paper proposes a method to estimate variance reduction in DNN training using importance sampling.

Importance sampling is widely used in machine learning and statistics, but its power is limited by the restriction of using simple proposals for which the importance weights can be tractably calculated. We address this problem by studying black-box importance sampling methods that calculate importance weights for sampl…

FIS-GAN uses importance sampling in GANs to speed up training.

New method improves Bayesian cross-validation.

The paper analyzes and improves privacy in machine learning through importance sampling.

The paper explains how importance sampling can be used for optimization of rare events.

Paper introduces a new sampling method combining Consistency Models with importance sampling.

Uniform sampling of training data has been commonly used in traditional stochastic optimization algorithms such as Proximal Stochastic Gradient Descent (prox-SGD) and Proximal Stochastic Dual Coordinate Ascent (prox-SDCA). Although uniform sampling can guarantee that the sampled stochastic quantity is an unbiased estim…

Low-rank MPPCA improves importance sampling in high dimensions.

Framework improves gradient estimation for faster training convergence.

Importance-weighting is a popular and well-researched technique for dealing with sample selection bias and covariate shift. It has desirable characteristics such as unbiasedness, consistency and low computational complexity. However, weighting can have a detrimental effect on an estimator as well. In this work, we empi…

TP-AIS improves sampling efficiency over existing methods.

New IS methods fail to reduce variance in long-horizon MDPs.

Enhances statistical mechanics solving using VANs with MCMC or importance sampling.

BR-SNIS reduces bias in self-normalized IS without increasing variance.

We develop a new method to estimate failure probabilities in complex systems.

We present a new method for conducting Monte Carlo inference in graphical models which combines explicit search with generalized importance sampling. The idea is to reduce the variance of importance sampling by searching for significant points in the target distribution. We prove that it is possible to introduce search…

The paper improves importance sampling and MCMC methods for complex distributions.

New MCMC method corrects bias without extra cost.

SSFI identifies key features affecting a single data point's prediction.

Importance sampling has become an important tool for the computation of tail-based risk measures. Since such quantities are often determined mainly by rare events standard Monte Carlo can be inefficient and importance sampling provides a way to speed up computations. This paper considers moderate deviations for the wei…

We consider the problem of off-policy evaluation in Markov decision processes. Off-policy evaluation is the task of evaluating the expected return of one policy with data generated by a different, behavior policy. Importance sampling is a technique for off-policy evaluation that re-weights off-policy returns to account…

Importance weighting is a general way to adjust Monte Carlo integration to account for draws from the wrong distribution, but the resulting estimate can be highly variable when the importance ratios have a heavy right tail. This routinely occurs when there are aspects of the target distribution that are not well captur…

Adaptive importance sampling for estimating point process statistics.

AIS algorithm balances exploration and exploitation for efficient sampling.

Minibatching is a very well studied and highly popular technique in supervised learning, used by practitioners due to its ability to accelerate training through better utilization of parallel processing power and reduction of stochastic variance. Another popular technique is importance sampling -- a strategy for prefer…

This paper balances bias and variance in adaptive importance sampling using mirror descent.

Proposes SOVR loss to improve adversarial robustness by increasing logit margins.

Improves off-policy RL stability with RIS.

BIS uses bandits to efficiently sample from expensive-to-evaluate densities.

Extends importance sampling to nonlinear models using adjoint operators.

MultiVerse uses importance sampling for efficient causal reasoning in probabilistic programming.

A recently introduced Importance Sampling strategy based on a least squares optimization is applied to the Monte Carlo simulation of Libor Market Models. Such Least Squares Importance Sampling (LSIS) allows the automatic optimization of the sampling distribution within a trial class by means of a quick presimulation al…

Paper proposes an algorithm to optimize CVaR using retrospective approximation and importance sampling.

Paper proposes a new method for learning deep generative models using annealed importance sampling.

A new resampling strategy, Importance Resampling, improves sample efficiency and reduces variance in off-policy prediction.

Avare improves optimization and sampling with adaptive importance sampling.

IWeS selects examples by entropy-based importance sampling for subset selection.