Search Results for author: Tianyu Du

Found 16 papers, 11 papers with code

RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback

1 code implementation11 Mar 2024 Yanming Liu, Xinyue Peng, Xuhong Zhang, Weihao Liu, Jianwei Yin, Jiannan Cao, Tianyu Du

Large language models (LLMs) demonstrate exceptional performance in numerous tasks but still heavily rely on knowledge stored in their parameters.

Retrieval

ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis

1 code implementation11 Mar 2024 Yanming Liu, Xinyue Peng, Tianyu Du, Jianwei Yin, Weihao Liu, Xuhong Zhang

Large language models (LLMs) have achieved commendable accomplishments in various natural language processing tasks.

Question Answering

VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models

1 code implementation NeurIPS 2023 Ziyi Yin, Muchao Ye, Tianrong Zhang, Tianyu Du, Jinguo Zhu, Han Liu, Jinghui Chen, Ting Wang, Fenglong Ma

In this paper, we aim to investigate a new yet practical task to craft image and text perturbations using pre-trained VL models to attack black-box fine-tuned models on different downstream tasks.

Adversarial Robustness

ReMasker: Imputing Tabular Data with Masked Autoencoding

1 code implementation25 Sep 2023 Tianyu Du, Luca Melis, Ting Wang

We present ReMasker, a new method of imputing missing values in tabular data by extending the masked autoencoding framework.

Imputation

G$^2$uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering

no code implementations8 Jun 2023 Hao Yu, Chuan Ma, Meng Liu, Tianyu Du, Ming Ding, Tao Xiang, Shouling Ji, Xinwang Liu

Through empirical evaluation, comparing G$^2$uardFL with cutting-edge defenses, such as FLAME (USENIX Security 2022) [28] and DeepSight (NDSS 2022) [36], against various backdoor attacks including 3DFed (SP 2023) [20], our results demonstrate its significant effectiveness in mitigating backdoor attacks while having a negligible impact on the aggregated model's performance on benign samples (i. e., the primary task performance).

Anomaly Detection Clustering +2

On the Security Risks of Knowledge Graph Reasoning

1 code implementation3 May 2023 Zhaohan Xi, Tianyu Du, Changjiang Li, Ren Pang, Shouling Ji, Xiapu Luo, Xusheng Xiao, Fenglong Ma, Ting Wang

Knowledge graph reasoning (KGR) -- answering complex logical queries over large knowledge graphs -- represents an important artificial intelligence task, entailing a range of applications (e. g., cyber threat hunting).

Knowledge Graphs

RNN-Guard: Certified Robustness Against Multi-frame Attacks for Recurrent Neural Networks

no code implementations17 Apr 2023 Yunruo Zhang, Tianyu Du, Shouling Ji, Peng Tang, Shanqing Guo

In this paper, we propose the first certified defense against multi-frame attacks for RNNs called RNN-Guard.

Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with Python

1 code implementation4 Apr 2023 Tianyu Du, Ayush Kanodia, Susan Athey

$\texttt{torch-choice}$ provides a $\texttt{ChoiceDataset}$ data structure to manage databases flexibly and memory-efficiently.

An Embarrassingly Simple Backdoor Attack on Self-supervised Learning

3 code implementations ICCV 2023 Changjiang Li, Ren Pang, Zhaohan Xi, Tianyu Du, Shouling Ji, Yuan YAO, Ting Wang

As a new paradigm in machine learning, self-supervised learning (SSL) is capable of learning high-quality representations of complex data without relying on labels.

Adversarial Robustness Backdoor Attack +2

Reasoning over Multi-view Knowledge Graphs

no code implementations27 Sep 2022 Zhaohan Xi, Ren Pang, Changjiang Li, Tianyu Du, Shouling Ji, Fenglong Ma, Ting Wang

(ii) It supports complex logical queries with varying relation and view constraints (e. g., with complex topology and/or from multiple views); (iii) It scales up to KGs of large sizes (e. g., millions of facts) and fine-granular views (e. g., dozens of views); (iv) It generalizes to query structures and KG views that are unobserved during training.

Knowledge Graphs Representation Learning

ROLAND: Graph Learning Framework for Dynamic Graphs

2 code implementations15 Aug 2022 Jiaxuan You, Tianyu Du, Jure Leskovec

Finally, we propose a scalable and efficient training approach for dynamic GNNs via incremental training and meta-learning.

Graph Learning Graph Representation Learning +2

CAREER: A Foundation Model for Labor Sequence Data

1 code implementation16 Feb 2022 Keyon Vafa, Emil Palikot, Tianyu Du, Ayush Kanodia, Susan Athey, David M. Blei

We fit CAREER to a dataset of 24 million job sequences from resumes, and adjust it on small longitudinal survey datasets.

Language Modelling Transfer Learning

NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification

1 code implementation25 Dec 2021 Haibin Zheng, Zhiqing Chen, Tianyu Du, Xuhong Zhang, Yao Cheng, Shouling Ji, Jingyi Wang, Yue Yu, Jinyin Chen

To overcome the challenges, we propose NeuronFair, a new DNN fairness testing framework that differs from previous work in several key aspects: (1) interpretable - it quantitatively interprets DNNs' fairness violations for the biased decision; (2) effective - it uses the interpretation results to guide the generation of more diverse instances in less time; (3) generic - it can handle both structured and unstructured data.

Fairness

Enhancing Model Robustness By Incorporating Adversarial Knowledge Into Semantic Representation

no code implementations23 Feb 2021 Jinfeng Li, Tianyu Du, Xiangyu Liu, Rong Zhang, Hui Xue, Shouling Ji

Extensive experiments on two real-world tasks show that AdvGraph exhibits better performance compared with previous work: (i) effective - it significantly strengthens the model robustness even under the adaptive attacks setting without negative impact on model performance over legitimate input; (ii) generic - its key component, i. e., the representation of connotative adversarial knowledge is task-agnostic, which can be reused in any Chinese-based NLP models without retraining; and (iii) efficient - it is a light-weight defense with sub-linear computational complexity, which can guarantee the efficiency required in practical scenarios.

SirenAttack: Generating Adversarial Audio for End-to-End Acoustic Systems

no code implementations23 Jan 2019 Tianyu Du, Shouling Ji, Jinfeng Li, Qinchen Gu, Ting Wang, Raheem Beyah

Despite their immense popularity, deep learning-based acoustic systems are inherently vulnerable to adversarial attacks, wherein maliciously crafted audios trigger target systems to misbehave.

Cryptography and Security

TextBugger: Generating Adversarial Text Against Real-world Applications

1 code implementation13 Dec 2018 Jinfeng Li, Shouling Ji, Tianyu Du, Bo Li, Ting Wang

Deep Learning-based Text Understanding (DLTU) is the backbone technique behind various applications, including question answering, machine translation, and text classification.

Adversarial Text Machine Translation +6

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