b'Ding Zhao'

research

∙ 08/02/2022

GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction

There has been an increased interest in applying deep neural networks to...

Jiacheng Zhu, et al. ∙

research

∙ 07/19/2022

Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning

As a pivotal component to attaining generalizable solutions in human int...

Wenhao Ding, et al. ∙

research

∙ 06/27/2022

Prompting Decision Transformer for Few-Shot Policy Generalization

Humans can leverage prior experience and learn novel tasks from a handfu...

Mengdi Xu, et al. ∙

research

∙ 06/20/2022

SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles

As shown by recent studies, machine intelligence-enabled systems are vul...

Chejian Xu, et al. ∙

research

∙ 05/29/2022

On the Robustness of Safe Reinforcement Learning under Observational Perturbations

Safe reinforcement learning (RL) trains a policy to maximize the task re...

Zuxin Liu, et al. ∙

research

∙ 04/25/2022

Road Traffic Law Adaptive Decision-making for Self-Driving Vehicles

Self-driving vehicles have their own intelligence to drive on open roads...

Jiaxin Liu, et al. ∙

research

∙ 04/13/2022

A Unified Cascaded Encoder ASR Model for Dynamic Model Sizes

In this paper, we propose a dynamic cascaded encoder Automatic Speech Re...

Shaojin Ding, et al. ∙

research

∙ 04/07/2022

MHMS: Multimodal Hierarchical Multimedia Summarization

Multimedia summarization with multimodal output can play an essential ro...

Jielin Qiu, et al. ∙

research

∙ 04/04/2022

Test Against High-Dimensional Uncertainties: Accelerated Evaluation of Autonomous Vehicles with Deep Importance Sampling

Evaluating the performance of autonomous vehicles (AV) and their complex...

Mansur Arief, et al. ∙

research

∙ 03/19/2022

PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression

Heart rate variability (HRV) is a practical and noninvasive measure of a...

Jiacheng Zhu, et al. ∙

research

∙ 02/19/2022

Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training

Robust Reinforcement Learning (RL) focuses on improving performances und...

Peide Huang, et al. ∙

research

∙ 02/04/2022

A Survey on Safety-Critical Driving Scenario Generation – A Methodological Perspective

Autonomous driving systems have witnessed a significant development duri...

Wenhao Ding, et al. ∙

research

∙ 01/28/2022

Constrained Variational Policy Optimization for Safe Reinforcement Learning

Safe reinforcement learning (RL) aims to learn policies that satisfy cer...

Zuxin Liu, et al. ∙

research

∙ 01/25/2022

Optimal Transport based Data Augmentation for Heart Disease Diagnosis and Prediction

In this paper, we focus on a new method of data augmentation to solve th...

Jielin Qiu, et al. ∙

research

∙ 11/03/2021

Certifiable Deep Importance Sampling for Rare-Event Simulation of Black-Box Systems

Rare-event simulation techniques, such as importance sampling (IS), cons...

Mansur Arief, et al. ∙

research

∙ 10/26/2021

CausalAF: Causal Autoregressive Flow for Goal-Directed Safety-Critical Scenes Generation

Goal-directed generation, aiming for solving downstream tasks by generat...

Wenhao Ding, et al. ∙

research

∙ 10/25/2021

Over-Conservativeness of Variance-Based Efficiency Criteria and Probabilistic Efficiency in Rare-Event Simulation

In rare-event simulation, an importance sampling (IS) estimator is regar...

Yuanlu Bai, et al. ∙

research

∙ 06/19/2021

Accelerated Policy Evaluation: Learning Adversarial Environments with Adaptive Importance Sampling

The evaluation of rare but high-stakes events remains one of the main di...

Mengdi Xu, et al. ∙

research

∙ 06/17/2021

CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing

We present the first framework of Certifying Robust Policies for reinfor...

Fan Wu, et al. ∙

research

∙ 06/08/2021

Semantically Controllable Scene Generation with Guidance of Explicit Knowledge

Deep Generative Models (DGMs) are known for their superior capability in...

Wenhao Ding, et al. ∙

research

∙ 05/02/2021

Improving Perception via Sensor Placement: Designing Multi-LiDAR Systems for Autonomous Vehicles

Recent years have witnessed an increasing interest in improving the perc...

Sharad Chitlangia, et al. ∙

research

∙ 04/28/2021

Personalized Keyphrase Detection using Speaker and Environment Information

In this paper, we introduce a streaming keyphrase detection system that ...

Rajeev Rikhye, et al. ∙

research

∙ 02/07/2021

Functional Optimal Transport: Mapping Estimation and Domain Adaptation for Functional data

Optimal transport (OT) has generated much recent interest by its capabil...

Jiacheng Zhu, et al. ∙

research

∙ 01/02/2021

Context-Aware Safe Reinforcement Learning for Non-Stationary Environments

Safety is a critical concern when deploying reinforcement learning agent...

Baiming Chen, et al. ∙

research

∙ 10/15/2020

Safe Model-based Reinforcement Learning with Robust Cross-Entropy Method

This paper studies the safe reinforcement learning (RL) problem without ...

Zuxin Liu, et al. ∙

research

∙ 10/10/2020

Rare-Event Simulation for Neural Network and Random Forest Predictors

We study rare-event simulation for a class of problems where the target ...

Yuanlu Bai, et al. ∙

research

∙ 09/16/2020

Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation

Existing neural network-based autonomous systems are shown to be vulnera...

Wenhao Ding, et al. ∙

research

∙ 07/30/2020

MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments

Multi-agent navigation in dynamic environments is of great industrial va...

Zuxin Liu, et al. ∙

research

∙ 06/28/2020

Deep Probabilistic Accelerated Evaluation: A Certifiable Rare-Event Simulation Methodology for Black-Box Autonomy

Evaluating the reliability of intelligent physical systems against rare ...

Mansur Arief, et al. ∙

research

∙ 06/19/2020

Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes

Continuously learning to solve unseen tasks with limited experience has ...

Mengdi Xu, et al. ∙

research

∙ 06/18/2020

Robust Unsupervised Learning of Temporal Dynamic Interactions

Robust representation learning of temporal dynamic interactions is an im...

Aritra Guha, et al. ∙

research

∙ 05/16/2020

Dynamic Sparsity Neural Networks for Automatic Speech Recognition

In automatic speech recognition (ASR), model pruning is a widely adopted...

Zhaofeng Wu, et al. ∙

research

∙ 05/11/2020

Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive Environments

Action and observation delays exist prevalently in the real-world cyber-...

Baiming Chen, et al. ∙

research

∙ 05/11/2020

Delay-Aware Model-Based Reinforcement Learning for Continuous Control

Action delays degrade the performance of reinforcement learning in many ...

Baiming Chen, et al. ∙

research

∙ 03/28/2020

A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency

Thus far, end-to-end (E2E) models have not been shown to outperform stat...

Tara N. Sainath, et al. ∙

research

∙ 03/02/2020

Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method

Long-tail and rare event problems become crucial when autonomous driving...

Wenhao Ding, et al. ∙

research

∙ 10/17/2019

Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process

Predicting surrounding vehicle behaviors are critical to autonomous vehi...

Jiacheng Zhu, et al. ∙

research

∙ 10/17/2019

Recurrent Attentive Neural Process for Sequential Data

Neural processes (NPs) learn stochastic processes and predict the distri...

Shenghao Qin, et al. ∙

research

∙ 10/08/2019

Multi-Vehicle Interaction Scenarios Generation with Interpretable Traffic Primitives and Gaussian Process Regression

Generating multi-vehicle interaction scenarios can benefit motion planni...

Weiyang Zhang, et al. ∙

research

∙ 09/19/2019

How to Evaluate Proving Grounds for Self-Driving? A Quantitative Approach

Proving ground has been a critical component in testing and validation f...

Rui Chen, et al. ∙

research

∙ 09/19/2019

How to Evaluate Self-Driving Testing Ground? A Quantitative Approach

Testing ground has been a critical component in testing and validation f...

Rui Chen, et al. ∙

research

∙ 09/17/2019

CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarios

Naturalistic driving trajectories are crucial for the performance of aut...

Wenhao Ding, et al. ∙

research

∙ 09/14/2019

Active Learning for Risk-Sensitive Inverse Reinforcement Learning

One typical assumption in inverse reinforcement learning (IRL) is that h...

Rui Chen, et al. ∙

research

∙ 07/17/2019

A General Framework of Learning Multi-Vehicle Interaction Patterns from Videos

Semantic learning and understanding of multi-vehicle interaction pattern...

Chengyuan Zhang, et al. ∙

research

∙ 06/25/2019

Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field

Autonomous vehicles (AV) are expected to navigate in complex traffic sce...

Yaohui Guo, et al. ∙

research

∙ 04/19/2019

Assessing Modeling Variability in Autonomous Vehicle Accelerated Evaluation

Safety evaluation of autonomous vehicles is extensively studied recently...

Zhiyuan Huang, et al. ∙

research

∙ 11/15/2018

Streaming End-to-end Speech Recognition For Mobile Devices

End-to-end (E2E) models, which directly predict output character sequenc...

Yanzhang He, et al. ∙

research

∙ 09/16/2018

Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach

Considering its reliability to provide accurate 3D views along with prec...

Zuxin Liu, et al. ∙

research

∙ 09/15/2018

Multi-Vehicle Trajectories Generation for Vehicle-to-Vehicle Encounters

Generating multi-vehicle trajectories analogous to these in real world c...

Wenhao Ding, et al. ∙

research

∙ 09/09/2018

Synthesis of Different Autonomous Vehicles Test Approaches

Currently, the most prevalent way to evaluate an autonomous vehicle is t...

Zhiyuan Huang, et al. ∙

Ding Zhao

Featured Co-authors