223 citations · 758 across the 42 of their papers we have counts for
17 papers · 1 filter
Learning for Counterfactual Fairness from Observational Data
Jing Ma, Ruocheng Guo, Aidong Zhang +1
Fairness-aware machine learning has attracted a surge of attention in many domains, such as online advertising, personalized recommendation, and social media analysis in web applic…
Virtual Node Tuning for Few-shot Node Classification
Zhen Tan, Ruocheng Guo, Kaize Ding +1
Few-shot Node Classification (FSNC) is a challenge in graph representation learning where only a few labeled nodes per class are available for training. To tackle this issue, meta-…
Inference-time Stochastic Ranking with Risk Control
Ruocheng Guo, Jean-François Ton, Yang Liu +1
Learning to Rank (LTR) methods are vital in online economies, affecting users and item providers. Fairness in LTR models is crucial to allocate exposure proportionally to item rele…
Debiasing Recommendation by Learning Identifiable Latent Confounders
Qing Zhang, Xiaoying Zhang, Yang Liu +4
Recommendation systems aim to predict users' feedback on items not exposed to them. Confounding bias arises due to the presence of unmeasured variables (e.g., the socio-economic st…
Distributional Shift Adaptation using Domain-Specific Features
Anique Tahir, Lu Cheng, Ruocheng Guo +1
Machine learning algorithms typically assume that the training and test samples come from the same distributions, i.e., in-distribution. However, in open-world scenarios, streaming…
CLEAR: Generative Counterfactual Explanations on Graphs
Jing Ma, Ruocheng Guo, Saumitra Mishra +2
Counterfactual explanations promote explainability in machine learning models by answering the question "how should an input instance be perturbed to obtain a desired predicted lab…
Supervised Graph Contrastive Learning for Few-shot Node Classification
Zhen Tan, Kaize Ding, Ruocheng Guo +1
Graphs are present in many real-world applications, such as financial fraud detection, commercial recommendation, and social network analysis. But given the high cost of graph anno…
Evaluation Methods and Measures for Causal Learning Algorithms
Lu Cheng, Ruocheng Guo, Raha Moraffah +3
The convenient access to copious multi-faceted data has encouraged machine learning researchers to reconsider correlation-based learning and embrace the opportunity of causality-ba…
Learning Fair Node Representations with Graph Counterfactual Fairness
Jing Ma, Ruocheng Guo, Mengting Wan +3
Fair machine learning aims to mitigate the biases of model predictions against certain subpopulations regarding sensitive attributes such as race and gender. Among the many existin…
Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies
Lu Cheng, Ruocheng Guo, Huan Liu
Online reviews enable consumers to engage with companies and provide important feedback. Due to the complexity of the high-dimensional text, these reviews are often simplified as a…
Graph Few-shot Class-incremental Learning
Zhen Tan, Kaize Ding, Ruocheng Guo +1
The ability to incrementally learn new classes is vital to all real-world artificial intelligence systems. A large portion of high-impact applications like social media, recommenda…
Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication
Lu Cheng, Ruocheng Guo, Kasim Selcuk Candan +1
Online review systems are the primary means through which many businesses seek to build the brand and spread their messages. Prior research studying the effects of online reviews h…
Out-of-distribution Prediction with Invariant Risk Minimization: The Limitation and An Effective Fix
Ruocheng Guo, Pengchuan Zhang, Hao Liu +1
This work considers the out-of-distribution (OOD) prediction problem where (1)~the training data are from multiple domains and (2)~the test domain is unseen in the training. DNNs f…
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation
Raha Moraffah, Mansooreh Karami, Ruocheng Guo +2
Machine learning models have had discernible achievements in a myriad of applications. However, most of these models are black-boxes, and it is obscure how the decisions are made b…
Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data
Ruocheng Guo, Jundong Li, Huan Liu
Counterfactual evaluation of novel treatment assignment functions (e.g., advertising algorithms and recommender systems) is one of the most crucial causal inference problems for pr…
Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects
Vineeth Rakesh, Ruocheng Guo, Raha Moraffah +2
Modeling spillover effects from observational data is an important problem in economics, business, and other fields of research. % It helps us infer the causality between two seemi…
Strongly Hierarchical Factorization Machines and ANOVA Kernel Regression
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. However,…