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.
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.
1 code implementation • 8 Jun 2019 • Ruocheng Guo, Jundong Li, Huan Liu
In fact, an important fact ignored by the majority of previous work is that observational data can come with network information that can be utilized to infer hidden confounders.
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 • 23 Dec 2021 • Zhen Tan, Kaize Ding, Ruocheng Guo, Huan Liu
The ability to incrementally learn new classes is vital to all real-world artificial intelligence systems.
1 code implementation • 10 Jan 2022 • Jing Ma, Ruocheng Guo, Mengting Wan, Longqi Yang, Aidong Zhang, Jundong Li
In this framework, we generate counterfactuals corresponding to perturbations on each node's and their neighbors' sensitive attributes.
1 code implementation • 13 Jan 2022 • Jiayu Shang, Xubo Tang, Ruocheng Guo, Yanni Sun
In the real metagenomic data experiment, PhaMer improves the F1-score of phage detection by 27\%.
1 code implementation • 9 Aug 2018 • Vineeth Rakesh, Ruocheng Guo, Raha Moraffah, Nitin Agarwal, Huan Liu
Modeling spillover effects from observational data is an important problem in economics, business, and other fields of research.
1 code implementation • 28 Oct 2023 • Xiangyu Zhao, Maolin Wang, Xinjian Zhao, Jiansheng Li, Shucheng Zhou, Dawei Yin, Qing Li, Jiliang Tang, Ruocheng Guo
This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques.
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.
1 code implementation • 10 Feb 2023 • Qing Zhang, Xiaoying Zhang, Yang Liu, Hongning Wang, Min Gao, Jiheng Zhang, Ruocheng Guo
Confounding bias arises due to the presence of unmeasured variables (e. g., the socio-economic status of a user) that can affect both a user's exposure and feedback.
1 code implementation • 10 Aug 2023 • Yang Liu, Yuanshun Yao, Jean-Francois Ton, Xiaoying Zhang, Ruocheng Guo, Hao Cheng, Yegor Klochkov, Muhammad Faaiz Taufiq, Hang Li
However, a major challenge faced by practitioners is the lack of clear guidance on evaluating whether LLM outputs align with social norms, values, and regulations.
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.
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.
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.
no code implementations • 25 Dec 2017 • Ruocheng Guo, Hamidreza Alvari, Paulo Shakarian
High-order parametric models that include terms for feature interactions are applied to various data mining tasks, where ground truth depends on interactions of features.
no code implementations • 4 May 2019 • Elham Shaabani, Ruocheng Guo, Paulo Shakarian
The spread of harmful mis-information in social media is a pressing problem.
no code implementations • 22 Nov 2019 • Ghazaleh Beigi, Ahmadreza Mosallanezhad, Ruocheng Guo, Hamidreza Alvari, Alexander Nou, Huan Liu
The attacker seeks to infer users' private-attribute information according to their items list and recommendations.
no code implementations • 22 Dec 2019 • Ruocheng Guo, Jundong Li, Huan Liu
When such data comes with network information, the later can be potentially useful to correct hidden confounding bias.
no code implementations • 9 Mar 2020 • Raha Moraffah, Mansooreh Karami, Ruocheng Guo, Adrienne Raglin, Huan Liu
In this work, models that aim to answer causal questions are referred to as causal interpretable models.
no code implementations • 23 Apr 2020 • Ninghao Liu, Mengnan Du, Ruocheng Guo, Huan Liu, Xia Hu
In this paper, we review recent work on adversarial attacks and defenses, particularly from the perspective of machine learning interpretation.
no code implementations • 16 Jan 2021 • Ruocheng Guo, Pengchuan Zhang, Hao liu, Emre Kiciman
Nevertheless, we find that the performance of IRM can be dramatically degraded under \emph{strong $\Lambda$ spuriousness} -- when the spurious correlation between the spurious features and the class label is strong due to the strong causal influence of their common cause, the domain label, on both of them (see Fig.
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 • 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.
no code implementations • 29 Mar 2022 • Zhen Tan, Kaize Ding, Ruocheng Guo, Huan Liu
Graphs are present in many real-world applications, such as financial fraud detection, commercial recommendation, and social network analysis.
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 • 25 Apr 2022 • Muyang Li, Xiangyu Zhao, Chuan Lyu, Minghao Zhao, Runze Wu, Ruocheng Guo
In addition, most existing works assume that such sequential dependencies exist solely in the item embeddings, but neglect their existence among the item features.
no code implementations • 16 Oct 2022 • Jing Ma, Ruocheng Guo, Saumitra Mishra, Aidong Zhang, Jundong Li
Counterfactual explanations promote explainability in machine learning models by answering the question "how should an input instance be perturbed to obtain a desired predicted label?".
no code implementations • 31 Oct 2022 • Weijieying Ren, Lei Wang, Kunpeng Liu, Ruocheng Guo, Lim Ee Peng, Yanjie Fu
We present a gradient perspective to understand two negative impacts of popularity bias in recommendation model optimization: (i) the gradient direction of popular item embeddings is closer to that of positive interactions, and (ii) the magnitude of positive gradient for popular items are much greater than that of unpopular items.
no code implementations • 11 Mar 2023 • Muyang Li, Zijian Zhang, Xiangyu Zhao, Wanyu Wang, Minghao Zhao, Runze Wu, Ruocheng Guo
Sequential recommender systems aim to predict users' next interested item given their historical interactions.
no code implementations • 25 May 2023 • Xiaohui Chen, Jiankai Sun, Taiqing Wang, Ruocheng Guo, Li-Ping Liu, Aonan Zhang
Most subsampling methods are model-based and often require a pre-trained pilot model to measure data importance via e. g. sample hardness.
no code implementations • 5 Jun 2023 • Maolin Wang, Yaoming Zhen, Yu Pan, Yao Zhao, Chenyi Zhuang, Zenglin Xu, Ruocheng Guo, Xiangyu Zhao
THNN is a faithful hypergraph modeling framework through high-order outer product feature message passing and is a natural tensor extension of the adjacency-matrix-based graph neural networks.
no code implementations • 9 Jun 2023 • Zhen Tan, Ruocheng Guo, Kaize Ding, Huan Liu
Our approach utilizes a pretrained graph transformer as the encoder and injects virtual nodes as soft prompts in the embedding space, which can be optimized with few-shot labels in novel classes to modulate node embeddings for each specific FSNC task.
no code implementations • 12 Jun 2023 • Ruocheng Guo, Jean-François Ton, Yang Liu
Learning to Rank (LTR) methods are vital in online economies, affecting users and item providers.
no code implementations • 17 Jul 2023 • Jing Ma, Ruocheng Guo, Aidong Zhang, Jundong Li
A prerequisite for existing methods to achieve counterfactual fairness is the prior human knowledge of the causal model for the data.
no code implementations • 9 Oct 2023 • Yegor Klochkov, Jean-Francois Ton, Ruocheng Guo, Yang Liu, Hang Li
We address the problem of concept removal in deep neural networks, aiming to learn representations that do not encode certain specified concepts (e. g., gender etc.)
no code implementations • 9 Oct 2023 • Tongxin Yin, Jean-François Ton, Ruocheng Guo, Yuanshun Yao, Mingyan Liu, Yang Liu
To generalize the abstaining decisions to test samples, we then train a surrogate model to learn the abstaining decisions based on the IP solutions in an end-to-end manner.
no code implementations • 2 Nov 2023 • Song Wang, Zhen Tan, Ruocheng Guo, Jundong Li
Adopting a two-stage paradigm of pretraining followed by fine-tuning, Pretrained Language Models (PLMs) have achieved substantial advancements in the field of natural language processing.
no code implementations • 17 Nov 2023 • Maolin Wang, Dun Zeng, Zenglin Xu, Ruocheng Guo, Xiangyu Zhao
To address these issues, we propose a novel method, i. e., Federated Latent Embedding Sharing Tensor factorization (FLEST), which is a novel approach using federated tensor factorization for KG completion.
no code implementations • 10 Dec 2023 • Maolin Wang, Yao Zhao, Jiajia Liu, Jingdong Chen, Chenyi Zhuang, Jinjie Gu, Ruocheng Guo, Xiangyu Zhao
In our research, we constructed a dataset, the Multimodal Advertisement Audition Dataset (MAAD), from real-world scenarios within Alipay, and conducted experiments to validate the reliability of our proposed strategy.
no code implementations • 1 Feb 2024 • Sheng Zhang, Maolin Wang, Yao Zhao, Chenyi Zhuang, Jinjie Gu, Ruocheng Guo, Xiangyu Zhao, Zijian Zhang, Hongzhi Yin
Our research addresses the computational and resource inefficiencies that current Sequential Recommender Systems (SRSs) suffer from.
no code implementations • 1 Feb 2024 • Maolin Wang, Yu Pan, Zenglin Xu, Ruocheng Guo, Xiangyu Zhao, Wanyu Wang, Yiqi Wang, Zitao Liu, Langming Liu
Our contributions encompass the introduction of a pioneering CDF-based TPP model, the development of a methodology for incorporating past event information into future event prediction, and empirical validation of CuFun's effectiveness through extensive experimentation on synthetic and real-world datasets.
no code implementations • 14 Feb 2024 • Chen Wang, Fangxin Wang, Ruocheng Guo, Yueqing Liang, Kay Liu, Philip S. Yu
Recognizing the critical role of confidence in aligning training objectives with evaluation metrics, we propose CPFT, a versatile framework that enhances recommendation confidence by integrating Conformal Prediction (CP)-based losses with CE loss during fine-tuning.
no code implementations • 16 Feb 2024 • Xinjian Zhao, Liang Zhang, Yang Liu, Ruocheng Guo, Xiangyu Zhao
To address this challenge, we propose an innovative framework: Adversarial Curriculum Graph Contrastive Learning (ACGCL), which capitalizes on the merits of pair-wise augmentation to engender graph-level positive and negative samples with controllable similarity, alongside subgraph contrastive learning to discern effective graph patterns therein.
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.