Search Results for author: Ruocheng Guo

Found 12 papers, 2 papers with code

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 treatment into different causal pathways and quantify the causal effect in each pathway.

Causal Inference Latent Variable Models

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

no code implementations19 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 +1

Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation

no code implementations9 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.

Decision Making

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 Recommendation Systems

Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning

no code implementations22 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.

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.

Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects

no code implementations9 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.

Sparse Learning

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