Search Results for author: Xingyu Zhao

Found 33 papers, 16 papers with code

Formal Specification, Assessment, and Enforcement of Fairness for Generative AIs

1 code implementation25 Apr 2024 Chih-Hong Cheng, Changshun Wu, Harald Ruess, Xingyu Zhao, Saddek Bensalem

Reinforcing or even exacerbating societal biases and inequalities will increase significantly as generative AI increasingly produces useful artifacts, from text to images and beyond, for the real world.


ProTIP: Probabilistic Robustness Verification on Text-to-Image Diffusion Models against Stochastic Perturbation

1 code implementation23 Feb 2024 Yi Zhang, Yun Tang, Wenjie Ruan, Xiaowei Huang, Siddartha Khastgir, Paul Jennings, Xingyu Zhao

Text-to-Image (T2I) Diffusion Models (DMs) have shown impressive abilities in generating high-quality images based on simple text descriptions.

Instance-Level Safety-Aware Fidelity of Synthetic Data and Its Calibration

1 code implementation10 Feb 2024 Chih-Hong Cheng, Paul Stöckel, Xingyu Zhao

Modeling and calibrating the fidelity of synthetic data is paramount in shaping the future of safe and reliable self-driving technology by offering a cost-effective and scalable alternative to real-world data collection.

Building Guardrails for Large Language Models

no code implementations2 Feb 2024 Yi Dong, Ronghui Mu, Gaojie Jin, Yi Qi, Jinwei Hu, Xingyu Zhao, Jie Meng, Wenjie Ruan, Xiaowei Huang

As Large Language Models (LLMs) become more integrated into our daily lives, it is crucial to identify and mitigate their risks, especially when the risks can have profound impacts on human users and societies.

ReRoGCRL: Representation-based Robustness in Goal-Conditioned Reinforcement Learning

1 code implementation12 Dec 2023 Xiangyu Yin, Sihao Wu, Jiaxu Liu, Meng Fang, Xingyu Zhao, Xiaowei Huang, Wenjie Ruan

Then, to mitigate the vulnerability of existing GCRL algorithms, we introduce Adversarial Representation Tactics, which combines Semi-Contrastive Adversarial Augmentation with Sensitivity-Aware Regularizer to improve the adversarial robustness of the underlying RL agent against various types of perturbations.

Adversarial Robustness reinforcement-learning

SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraints

2 code implementations5 Dec 2023 Xianping Ma, Qianqian Wu, Xingyu Zhao, Xiaokang Zhang, Man-on Pun, Bo Huang

Furthermore, the boundary loss capitalizes on the distinctive features of SGB by directing the model's attention to the boundary information of the object.

Model Optimization Novel Concepts +4

Scalable Label Distribution Learning for Multi-Label Classification

1 code implementation28 Nov 2023 Xingyu Zhao, Yuexuan An, Lei Qi, Xin Geng

Most existing MLC methods are based on the assumption that the correlation of two labels in each label pair is symmetric, which is violated in many real-world scenarios.

Classification Multi-Label Classification

Deep-learning-based decomposition of overlapping-sparse images: application at the vertex of neutrino interactions

1 code implementation30 Oct 2023 Saúl Alonso-Monsalve, Davide Sgalaberna, Xingyu Zhao, Adrien Molines, Clark McGrew, André Rubbia

Image decomposition plays a crucial role in various computer vision tasks, enabling the analysis and manipulation of visual content at a fundamental level.

What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety Critical Systems

no code implementations20 Jul 2023 Saddek Bensalem, Chih-Hong Cheng, Wei Huang, Xiaowei Huang, Changshun Wu, Xingyu Zhao

Machine learning has made remarkable advancements, but confidently utilising learning-enabled components in safety-critical domains still poses challenges.

Risk Controlled Image Retrieval

no code implementations14 Jul 2023 Kaiwen Cai, Chris Xiaoxuan Lu, Xingyu Zhao, Xiaowei Huang

Most image retrieval research focuses on improving predictive performance, ignoring scenarios where the reliability of the prediction is also crucial.

Image Retrieval Model Selection +2

A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation

no code implementations19 May 2023 Xiaowei Huang, Wenjie Ruan, Wei Huang, Gaojie Jin, Yi Dong, Changshun Wu, Saddek Bensalem, Ronghui Mu, Yi Qi, Xingyu Zhao, Kaiwen Cai, Yanghao Zhang, Sihao Wu, Peipei Xu, Dengyu Wu, Andre Freitas, Mustafa A. Mustafa

Large Language Models (LLMs) have exploded a new heatwave of AI for their ability to engage end-users in human-level conversations with detailed and articulate answers across many knowledge domains.

Bayesian Learning for the Robust Verification of Autonomous Robots

1 code implementation15 Mar 2023 Xingyu Zhao, Simos Gerasimou, Radu Calinescu, Calum Imrie, Valentin Robu, David Flynn

Autonomous robots used in infrastructure inspection, space exploration and other critical missions operate in highly dynamic environments.

Decentralised and Cooperative Control of Multi-Robot Systems through Distributed Optimisation

no code implementations3 Feb 2023 Yi Dong, Zhongguo Li, Xingyu Zhao, Zhengtao Ding, Xiaowei Huang

Then, based on the distributed optimisation algorithm, an output regulation method is utilised to solve the optimal coordination problem for general linear dynamic systems.

Artificial intelligence for improved fitting of trajectories of elementary particles in inhomogeneous dense materials immersed in a magnetic field

no code implementations9 Nov 2022 Saúl Alonso-Monsalve, Davide Sgalaberna, Xingyu Zhao, Clark McGrew, André Rubbia

In this article, we use artificial intelligence algorithms to show how to enhance the resolution of the elementary particle track fitting in inhomogeneous dense detectors, such as plastic scintillators.

Reachability Verification Based Reliability Assessment for Deep Reinforcement Learning Controlled Robotics and Autonomous Systems

no code implementations26 Oct 2022 Yi Dong, Xingyu Zhao, Sen Wang, Xiaowei Huang

Deep Reinforcement Learning (DRL) has achieved impressive performance in robotics and autonomous systems (RAS).

SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability

1 code implementation ICCV 2023 Wei Huang, Xingyu Zhao, Gaojie Jin, Xiaowei Huang

Finally, we demonstrate two applications of our methods: ranking robust XAI methods and selecting training schemes to improve both classification and interpretation robustness.

Explainable Artificial Intelligence (XAI)

Short-term Load Forecasting with Distributed Long Short-Term Memory

no code implementations1 Aug 2022 Yi Dong, Yang Chen, Xingyu Zhao, Xiaowei Huang

With the employment of smart meters, massive data on consumer behaviour can be collected by retailers.

Load Forecasting

Hierarchical Distribution-Aware Testing of Deep Learning

1 code implementation17 May 2022 Wei Huang, Xingyu Zhao, Alec Banks, Victoria Cox, Xiaowei Huang

In this paper, we propose a new robustness testing approach for detecting AEs that considers both the feature level distribution and the pixel level distribution, capturing the perceptual quality of adversarial perturbations.

Adversarial Robustness Data Compression

Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance

no code implementations30 Nov 2021 Yi Dong, Wei Huang, Vibhav Bharti, Victoria Cox, Alec Banks, Sen Wang, Xingyu Zhao, Sven Schewe, Xiaowei Huang

The increasing use of Machine Learning (ML) components embedded in autonomous systems -- so-called Learning-Enabled Systems (LESs) -- has resulted in the pressing need to assure their functional safety.

Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking

1 code implementation14 Sep 2021 Yi Dong, Xingyu Zhao, Xiaowei Huang

While Deep Reinforcement Learning (DRL) provides transformational capabilities to the control of Robotics and Autonomous Systems (RAS), the black-box nature of DRL and uncertain deployment environments of RAS pose new challenges on its dependability.

Reinforcement Learning (RL)

Detecting Operational Adversarial Examples for Reliable Deep Learning

no code implementations13 Apr 2021 Xingyu Zhao, Wei Huang, Sven Schewe, Yi Dong, Xiaowei Huang

The utilisation of Deep Learning (DL) raises new challenges regarding its dependability in critical applications.

Joint Active and Passive Beamforming for Intelligent Reflecting Surface Aided Multiuser MIMO Communications

no code implementations25 Jan 2021 Xingyu Zhao, Tian Lin, Yu Zhu

This letter investigates the joint active and passive beamforming optimization for intelligent reflecting surface (IRS) aided multiuser multiple-input multiple-output systems with the objective of maximizing the weighted sum-rate.

Information Theory Information Theory

BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations

2 code implementations5 Dec 2020 Xingyu Zhao, Wei Huang, Xiaowei Huang, Valentin Robu, David Flynn

Given the pressing need for assuring algorithmic transparency, Explainable AI (XAI) has emerged as one of the key areas of AI research.

Explainable Artificial Intelligence (XAI)

Embedding and Extraction of Knowledge in Tree Ensemble Classifiers

2 code implementations16 Oct 2020 Wei Huang, Xingyu Zhao, Xiaowei Huang

Whilst, as the increasing use of machine learning models in security-critical applications, the embedding and extraction of malicious knowledge are equivalent to the notorious backdoor attack and its defence, respectively.

Backdoor Attack BIG-bench Machine Learning

A Safety Framework for Critical Systems Utilising Deep Neural Networks

no code implementations7 Mar 2020 Xingyu Zhao, Alec Banks, James Sharp, Valentin Robu, David Flynn, Michael Fisher, Xiaowei Huang

Increasingly sophisticated mathematical modelling processes from Machine Learning are being used to analyse complex data.

Coverage Guided Testing for Recurrent Neural Networks

1 code implementation5 Nov 2019 Wei Huang, Youcheng Sun, Xingyu Zhao, James Sharp, Wenjie Ruan, Jie Meng, Xiaowei Huang

The test metrics and test case generation algorithm are implemented into a tool TestRNN, which is then evaluated on a set of LSTM benchmarks.

Defect Detection Drug Discovery +3

Probabilistic Model Checking of Robots Deployed in Extreme Environments

no code implementations10 Dec 2018 Xingyu Zhao, Valentin Robu, David Flynn, Fateme Dinmohammadi, Michael Fisher, Matt Webster

Robots are increasingly used to carry out critical missions in extreme environments that are hazardous for humans.

Bayesian Inference

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