59 citations · 454 across the 124 of their papers we have counts for
9 papers · 1 filter
Certifiable Deep Importance Sampling for Rare-Event Simulation of Black-Box Systems
Mansur Arief, Yuanlu Bai, Wenhao Ding +4
Rare-event simulation techniques, such as importance sampling (IS), constitute powerful tools to speed up challenging estimation of rare catastrophic events. These techniques often…
CausalAF: Causal Autoregressive Flow for Safety-Critical Driving Scenario Generation
Wenhao Ding, Haohong Lin, Bo Li +1
Generating safety-critical scenarios, which are crucial yet difficult to collect, provides an effective way to evaluate the robustness of autonomous driving systems. However, the d…
Over-Conservativeness of Variance-Based Efficiency Criteria and Probabilistic Efficiency in Rare-Event Simulation
Yuanlu Bai, Zhiyuan Huang, Henry Lam +1
In rare-event simulation, an importance sampling (IS) estimator is regarded as efficient if its relative error, namely the ratio between its standard deviation and mean, is suffici…
Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling
Mengdi Xu, Peide Huang, Fengpei Li +6
Evaluating rare but high-stakes events is one of the main challenges in obtaining reliable reinforcement learning policies, especially in large or infinite state/action spaces wher…
CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing
Fan Wu, Linyi Li, Zijian Huang +3
As reinforcement learning (RL) has achieved great success and been even adopted in safety-critical domains such as autonomous vehicles, a range of empirical studies have been condu…
Semantically Adversarial Scenario Generation with Explicit Knowledge Guidance
Wenhao Ding, Haohong Lin, Bo Li +1
Generating adversarial scenarios, which have the potential to fail autonomous driving systems, provides an effective way to improve robustness. Extending purely data-driven generat…
Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving
Hanjiang Hu, Zuxin Liu, Sharad Chitlangia +2
The past few years have witnessed an increasing interest in improving the perception performance of LiDARs on autonomous vehicles. While most of the existing works focus on develop…
Functional optimal transport: map estimation and domain adaptation for functional data
Jiacheng Zhu, Aritra Guha, Dat Do +3
We introduce a formulation of optimal transport problem for distributions on function spaces, where the stochastic map between functional domains can be partially represented in te…
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments
Baiming Chen, Zuxin Liu, Jiacheng Zhu +3
Safety is a critical concern when deploying reinforcement learning agents for realistic tasks. Recently, safe reinforcement learning algorithms have been developed to optimize the…