Search Results for author: Lu Cheng

Found 16 papers, 5 papers with code

Toward Understanding Bias Correlations for Mitigation in NLP

no code implementations24 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.

Fairness Word Embeddings

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

Human Instance Segmentation and Tracking via Data Association and Single-stage Detector

no code implementations31 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.

Human Instance Segmentation Semantic Segmentation +3

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.

Causal Inference

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

A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation

no code implementations9 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.

Misinformation Recommendation Systems

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

Joint Content-Context Analysis of Scientific Publications: Identifying Opportunities for Collaboration in Cognitive Science

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).

Community Detection

Mitigating Bias in Session-based Cyberbullying Detection: A Non-Compromising Approach

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.

Analysis of Legal Documents via Non-negative Matrix Factorization Methods

no code implementations28 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.

Causal Learning for Socially Responsible AI

no code implementations25 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.

Fairness

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

Socially Responsible AI Algorithms: Issues, Purposes, and Challenges

no code implementations1 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.

Fairness

Improving Cyberbully Detection with User Interaction

1 code implementation1 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.

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

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.

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