Search Results for author: Ruocheng Guo

Found 45 papers, 14 papers with code

Conformal Counterfactual Inference under Hidden Confounding

no code implementations20 May 2024 Zonghao Chen, Ruocheng Guo, Jean-François Ton, Yang Liu

Personalized decision making requires the knowledge of potential outcomes under different treatments, and confidence intervals about the potential outcomes further enrich this decision-making process and improve its reliability in high-stakes scenarios.

Conformal Prediction counterfactual +3

Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media

no code implementations6 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.

Causal Inference

Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation

no code implementations16 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.

Contrastive Learning Graph Representation Learning

Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction

no code implementations14 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.

Conformal Prediction Model Selection +1

Cumulative Distribution Function based General Temporal Point Processes

no code implementations1 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.

Information Retrieval Point Processes +1

Large Multimodal Model Compression via Efficient Pruning and Distillation at AntGroup

no code implementations10 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.

Model Compression

Federated Knowledge Graph Completion via Latent Embedding Sharing and Tensor Factorization

no code implementations17 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.

Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidance

no code implementations2 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.

Embedding in Recommender Systems: A Survey

1 code implementation28 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.

AutoML Collaborative Filtering +3

Fair Classifiers that Abstain without Harm

no code implementations9 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.

Decision Making Fairness

Deep Concept Removal

no code implementations9 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.)

Attribute Out-of-Distribution Generalization

Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment

1 code implementation10 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.

Fairness Models Alignment

Learning for Counterfactual Fairness from Observational Data

no code implementations17 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.

Attribute Causal Discovery +4

Inference-time Stochastic Ranking with Risk Control

no code implementations12 Jun 2023 Ruocheng Guo, Jean-François Ton, Yang Liu, Hang Li

Widely used deterministic LTR models can lead to unfair exposure distribution, especially when items with the same relevance receive slightly different ranking scores.

Fairness Learning-To-Rank

Virtual Node Tuning for Few-shot Node Classification

no code implementations9 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.

Classification Graph Representation Learning +2

Tensorized Hypergraph Neural Networks

no code implementations5 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.

Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems

no code implementations25 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.

Recommendation Systems

AutoMLP: Automated MLP for Sequential Recommendations

no code implementations11 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.

Recommendation Systems

Debiasing Recommendation by Learning Identifiable Latent Confounders

1 code implementation10 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.

Causal Inference counterfactual +1

Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks

1 code implementation24 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.

Fake News Detection

Distributional Shift Adaptation using Domain-Specific Features

1 code implementation9 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.

Mitigating Popularity Bias in Recommendation with Unbalanced Interactions: A Gradient Perspective

no code implementations31 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.

Model Optimization Recommendation Systems

CLEAR: Generative Counterfactual Explanations on Graphs

no code implementations16 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?".

counterfactual Counterfactual Explanation +1

MLP4Rec: A Pure MLP Architecture for Sequential Recommendations

no code implementations25 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.

Recommendation Systems

Causal Disentanglement with Network Information for Debiased Recommendations

no code implementations14 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.

Causal Inference Disentanglement +1

Supervised Graph Contrastive Learning for Few-shot Node Classification

no code implementations29 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.

Classification Contrastive Learning +4

Evaluation Methods and Measures for Causal Learning Algorithms

no code implementations7 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.

Benchmarking BIG-bench Machine Learning +1

Accurate identification of bacteriophages from metagenomic data using Transformer

1 code implementation13 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\%.

Language Modelling

Learning Fair Node Representations with Graph Counterfactual Fairness

1 code implementation10 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.

Attribute counterfactual +2

Graph Few-shot Class-incremental Learning

1 code implementation23 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.

Few-Shot Class-Incremental Learning Incremental Learning +2

Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies

1 code implementation19 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.

Causal Inference

Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication

1 code implementation4 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.

Causal Inference

Causal Mediation Analysis with Hidden Confounders

no code implementations21 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.

Causal Inference Fairness

Out-of-distribution Prediction with Invariant Risk Minimization: The Limitation and An Effective Fix

no code implementations16 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.

Long-Term Effect Estimation with Surrogate Representation

1 code implementation19 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.

Causal Inference

Adversarial Attacks and Defenses: An Interpretation Perspective

no code implementations23 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.

Adversarial Attack Adversarial Defense +2

Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data

no code implementations22 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.

Causal Inference counterfactual +1

Learning Individual Causal Effects from Networked Observational Data

1 code implementation8 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.

Causal Inference

A Survey of Learning Causality with Data: Problems and Methods

3 code implementations25 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.

BIG-bench Machine Learning

Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects

1 code implementation9 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.

Variational Inference

Strongly Hierarchical Factorization Machines and ANOVA Kernel Regression

no code implementations25 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.

regression Sparse Learning

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