1 code implementation • 7 Dec 2023 • Hakan Inan, Kartikeya Upasani, Jianfeng Chi, Rashi Rungta, Krithika Iyer, Yuning Mao, Michael Tontchev, Qing Hu, Brian Fuller, Davide Testuggine, Madian Khabsa
We introduce Llama Guard, an LLM-based input-output safeguard model geared towards Human-AI conversation use cases.
no code implementations • 28 Oct 2023 • Kunlin Cai, Jinghuai Zhang, Will Shand, Zhiqing Hong, Guang Wang, Desheng Zhang, Jianfeng Chi, Yuan Tian
These attacks in our attack suite assume different adversary knowledge and aim to extract different types of sensitive information from mobility data, providing a holistic privacy risk assessment for POI recommendation models.
1 code implementation • 15 Jun 2023 • Xiaotian Han, Jianfeng Chi, Yu Chen, Qifan Wang, Han Zhao, Na Zou, Xia Hu
This paper introduces the Fair Fairness Benchmark (\textsf{FFB}), a benchmarking framework for in-processing group fairness methods.
1 code implementation • 20 Dec 2022 • Jianfeng Chi, Wasi Uddin Ahmad, Yuan Tian, Kai-Wei Chang
Privacy policies provide individuals with information about their rights and how their personal information is handled.
1 code implementation • 30 Jun 2022 • Kuan Li, Yang Liu, Xiang Ao, Jianfeng Chi, Jinghua Feng, Hao Yang, Qing He
However, both strategies are faced with some immediate problems: raw features cannot represent various properties of nodes (e. g., structure information), and representations learned by supervised GNN may suffer from the poor performance of the classifier on the poisoned graph.
1 code implementation • 23 May 2022 • Jianfeng Chi, William Shand, Yaodong Yu, Kai-Wei Chang, Han Zhao, Yuan Tian
Contrastive representation learning has gained much attention due to its superior performance in learning representations from both image and sequential data.
no code implementations • 19 Apr 2022 • Md Rizwan Parvez, Jianfeng Chi, Wasi Uddin Ahmad, Yuan Tian, Kai-Wei Chang
Prior studies in privacy policies frame the question answering (QA) task as identifying the most relevant text segment or a list of sentences from a policy document given a user query.
1 code implementation • 19 Nov 2021 • Jianfeng Chi, Jian Shen, Xinyi Dai, Weinan Zhang, Yuan Tian, Han Zhao
We first provide a decomposition theorem for return disparity, which decomposes the return disparity of any two MDPs sharing the same state and action spaces into the distance between group-wise reward functions, the discrepancy of group policies, and the discrepancy between state visitation distributions induced by the group policies.
1 code implementation • The Web Conference 2021 • Yang Liu1, Xiang Ao, Zidi Qin, Jianfeng Chi, Jinghua Feng, Hao Yang, Qing He
Graph-based fraud detection approaches have escalated lots of attention recently due to the abundant relational information of graph-structured data, which may be beneficial for the detection of fraudsters.
Ranked #4 on Fraud Detection on Amazon-Fraud
1 code implementation • 24 Feb 2021 • Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon, Han Zhao
With the widespread deployment of large-scale prediction systems in high-stakes domains, e. g., face recognition, criminal justice, etc., disparity in prediction accuracy between different demographic subgroups has called for fundamental understanding on the source of such disparity and algorithmic intervention to mitigate it.
1 code implementation • ACL 2021 • Wasi Uddin Ahmad, Jianfeng Chi, Tu Le, Thomas Norton, Yuan Tian, Kai-Wei Chang
We refer to predicting the privacy practice explained in a sentence as intent classification and identifying the text spans sharing specific information as slot filling.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Wasi Uddin Ahmad, Jianfeng Chi, Yuan Tian, Kai-Wei Chang
Prior studies in this domain frame the QA task as retrieving the most relevant text segment or a list of sentences from the policy document given a question.
no code implementations • 25 Sep 2019 • Han Zhao, Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon
With the prevalence of machine learning services, crowdsourced data containing sensitive information poses substantial privacy challenges.
1 code implementation • 19 Aug 2019 • Fnu Suya, Jianfeng Chi, David Evans, Yuan Tian
In a black-box setting, the adversary only has API access to the target model and each query is expensive.
Cryptography and Security
no code implementations • NeurIPS 2020 • Han Zhao, Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon
Meanwhile, it is clear that in general there is a tension between minimizing information leakage and maximizing task accuracy.
no code implementations • 7 Dec 2018 • Jianfeng Chi, Emmanuel Owusu, Xuwang Yin, Tong Yu, William Chan, Patrick Tague, Yuan Tian
We present a practical method for protecting data during the inference phase of deep learning based on bipartite topology threat modeling and an interactive adversarial deep network construction.