1 code implementation • 17 Mar 2024 • Anique Tahir, Lu Cheng, Huan Liu
The scaling of Large Language Models (LLMs) for retrieval-based tasks, particularly in Retrieval Augmented Generation (RAG), faces significant memory constraints, especially when fine-tuning extensive prompt sequences.
no code implementations • 6 Mar 2024 • Bohan Jiang, Lu Cheng, Zhen Tan, Ruocheng Guo, Huan Liu
News media has been utilized as a political tool to stray from facts, presenting biased claims without evidence.
no code implementations • 2 Mar 2024 • Jiayuan Su, Jing Luo, Hongwei Wang, Lu Cheng
This study aims to address the pervasive challenge of quantifying uncertainty in large language models (LLMs) without logit-access.
no code implementations • 24 Feb 2024 • Qian Ma, Hongliang Chi, Hengrui Zhang, Kay Liu, Zhiwei Zhang, Lu Cheng, Suhang Wang, Philip S. Yu, Yao Ma
The rise of self-supervised learning, which operates without the need for labeled data, has garnered significant interest within the graph learning community.
1 code implementation • 21 Feb 2024 • Zhen Tan, Alimohammad Beigi, Song Wang, Ruocheng Guo, Amrita Bhattacharjee, Bohan Jiang, Mansooreh Karami, Jundong Li, Lu Cheng, Huan Liu
Furthermore, the paper includes an in-depth taxonomy of methodologies employing LLMs for data annotation, a comprehensive review of learning strategies for models incorporating LLM-generated annotations, and a detailed discussion on primary challenges and limitations associated with using LLMs for data annotation.
no code implementations • 8 Feb 2024 • Tianyi Zhao, Liangliang Zhang, Yao Ma, Lu Cheng
In the rapidly evolving landscape of artificial intelligence, multimodal learning systems (MMLS) have gained traction for their ability to process and integrate information from diverse modality inputs.
no code implementations • 15 Nov 2023 • Yueqing Liang, Lu Cheng, Ali Payani, Kai Shu
This work investigates the potential of undermining both fairness and detection performance in abusive language detection.
1 code implementation • 8 Nov 2023 • Zhen Tan, Lu Cheng, Song Wang, Yuan Bo, Jundong Li, Huan Liu
Pretrained language models (PLMs) have made significant strides in various natural language processing tasks.
no code implementations • 19 Oct 2023 • Hua Tang, Lu Cheng, Ninghao Liu, Mengnan Du
While the accuracy-fairness trade-off has been frequently observed in the literature of fair machine learning, rigorous theoretical analyses have been scarce.
no code implementations • 26 Sep 2023 • Nayoung Kim, David Mosallanezhad, Lu Cheng, Michelle V. Mancenido, Huan Liu
We also propose a modified self-supervised contrastive learning as a component of STANCE-C3 to prevent overfitting for the existing domain and target and enable cross-target stance detection.
no code implementations • 28 Aug 2023 • Song Wang, Jing Ma, Lu Cheng, Jundong Li
These auxiliary sets contain several labeled training samples that can enhance the model performance regarding fairness in meta-test tasks, thereby allowing for the transfer of learned useful fairness-oriented knowledge to meta-test tasks.
no code implementations • 25 Aug 2023 • Tianyi Zhao, Hui Hu, Lu Cheng
Graph Neural Networks (GNNs) are powerful tools for learning representations on graphs, such as social networks.
no code implementations • 20 Jun 2023 • Kenya S. Andrews, Bhuvani Shah, Lu Cheng
To illustrate this, we use real-world medical data to determine whether medical records exhibit words that could lead to testimonial injustice, employ fairness metrics (e. g. demographic parity, differential intersectional fairness, and subgroup fairness) to assess the severity to which subgroups are experiencing testimonial injustice, and analyze how the intersectionality of demographic features (e. g. gender and race) make a difference in uncovering testimonial injustice.
no code implementations • 11 May 2023 • Usman Gohar, Lu Cheng
The widespread adoption of Machine Learning systems, especially in more decision-critical applications such as criminal sentencing and bank loans, has led to increased concerns about fairness implications.
no code implementations • 19 Apr 2023 • Zhiwei Wei, Yi Xiao, Wenjia Xu, Mi Shu, Lu Cheng, Yang Wang, Chunbo Liu
To improve efficiency and effectiveness, we integrate multi-scale data using a knowledge graph, focusing on the recognition of C-shaped building patterns.
1 code implementation • 7 Apr 2023 • Anique Tahir, Lu Cheng, Huan Liu
We then propose a principled model to improve fairness when aleatoric uncertainty is high and improve utility elsewhere.
1 code implementation • 24 Dec 2022 • Ujun Jeong, Kaize Ding, Lu Cheng, Ruocheng Guo, Kai Shu, Huan Liu
Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society.
Ranked #1 on Graph Classification on UPFD-POL
1 code implementation • 9 Nov 2022 • Anique Tahir, Lu Cheng, Ruocheng Guo, Huan Liu
Machine learning algorithms typically assume that the training and test samples come from the same distributions, i. e., in-distribution.
1 code implementation • 14 Sep 2022 • Junxuan Huang, Yatong An, Lu Cheng, Bai Chen, Junsong Yuan, Chunming Qiao
Adversarial contrastive learning (ACL) is considered an effective way to improve the robustness of pre-trained models.
1 code implementation • COLING 2022 • Lu Cheng, Nayoung Kim, Huan Liu
Therefore, this work studies biases associated with multiple social categories: joint biases induced by the union of different categories and intersectional biases that do not overlap with the biases of the constituent categories.
no code implementations • 24 May 2022 • Lu Cheng, Suyu Ge, Huan Liu
In particular, we examine bias mitigation in two common NLP tasks -- toxicity detection and word embeddings -- on three social identities, i. e., race, gender, and religion.
no code implementations • 14 Apr 2022 • Paras Sheth, Ruocheng Guo, Lu Cheng, Huan Liu, K. Selçuk Candan
Aside from the user conformity, aspects of confounding such as item popularity present in the network information is also captured in our method with the aid of \textit{causal disentanglement} which unravels the learned representations into independent factors that are responsible for (a) modeling the exposure of an item to the user, (b) predicting the ratings, and (c) controlling the hidden confounders.
no code implementations • 31 Mar 2022 • Lu Cheng, Mingbo Zhao
To tracking the instance across the video, we have adopted data association strategy for matching the same instance in the video sequence, where we jointly learn target instance appearances and their affinities in a pair of video frames in an end-to-end fashion.
no code implementations • 7 Feb 2022 • Lu Cheng, Ruocheng Guo, Raha Moraffah, Paras Sheth, K. Selcuk Candan, Huan Liu
To bridge from conventional causal inference (i. e., based on statistical methods) to causal learning with big data (i. e., the intersection of causal inference and machine learning), in this survey, we review commonly-used datasets, evaluation methods, and measures for causal learning using an evaluation pipeline similar to conventional machine learning.
1 code implementation • 19 Dec 2021 • Lu Cheng, Ruocheng Guo, Huan Liu
This work empirically examines the causal effects of user-generated online reviews on a granular level: we consider multiple aspects, e. g., the Food and Service of a restaurant.
no code implementations • 9 Dec 2021 • Faisal Alatawi, Lu Cheng, Anique Tahir, Mansooreh Karami, Bohan Jiang, Tyler Black, Huan Liu
These mechanisms could be manifested in two forms: (1) the bias of social media's recommender systems and (2) internal biases such as confirmation bias and homophily.
1 code implementation • 4 Oct 2021 • Lu Cheng, Ruocheng Guo, Kasim Selcuk Candan, Huan Liu
Online review systems are the primary means through which many businesses seek to build the brand and spread their messages.
no code implementations • NeurIPS Workshop AI4Scien 2021 • Lu Cheng, Girish Ganesan, William He, Daniel Silverston, Harlin Lee, Jacob Gates Foster
This work studies publications in the field of cognitive science and utilizes mathematical techniques to connect the analysis of the papers' content (abstracts) to the context (citation, journals).
1 code implementation • ACL 2021 • Lu Cheng, Ahmadreza Mosallanezhad, Yasin Silva, Deborah Hall, Huan Liu
The element of repetition in cyberbullying behavior has directed recent computational studies toward detecting cyberbullying based on a social media session.
no code implementations • 28 Apr 2021 • Ryan Budahazy, Lu Cheng, Yihuan Huang, Andrew Johnson, Pengyu Li, Joshua Vendrow, Zhoutong Wu, Denali Molitor, Elizaveta Rebrova, Deanna Needell
The California Innocence Project (CIP), a clinical law school program aiming to free wrongfully convicted prisoners, evaluates thousands of mails containing new requests for assistance and corresponding case files.
no code implementations • 25 Apr 2021 • Lu Cheng, Ahmadreza Mosallanezhad, Paras Sheth, Huan Liu
The goal of this survey is to bring forefront the potentials and promises of CL for SRAI.
no code implementations • 21 Feb 2021 • Lu Cheng, Ruocheng Guo, Huan Liu
An important problem in causal inference is to break down the total effect of a treatment on an outcome into different causal pathways and to quantify the causal effect in each pathway.
no code implementations • 1 Jan 2021 • Lu Cheng, Kush R. Varshney, Huan Liu
In this survey, we provide a systematic framework of Socially Responsible AI Algorithms that aims to examine the subjects of AI indifference and the need for socially responsible AI algorithms, define the objectives, and introduce the means by which we may achieve these objectives.
1 code implementation • 1 Nov 2020 • Suyu Ge, Lu Cheng, Huan Liu
Cyberbullying, identified as intended and repeated online bullying behavior, has become increasingly prevalent in the past few decades.
1 code implementation • 19 Aug 2020 • Lu Cheng, Ruocheng Guo, Huan Liu
Second, short-term outcomes are often directly used as the proxy of the primary outcome, i. e., the surrogate.
3 code implementations • 25 Sep 2018 • Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, Huan Liu
This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and relations.