Search Results for author: Xueru Zhang

Found 9 papers, 3 papers with code

Fair Sequential Selection Using Supervised Learning Models

1 code implementation NeurIPS 2021 Mohammad Mahdi Khalili, Xueru Zhang, Mahed Abroshan

This observation implies that the fairness notions used in classification problems are not suitable for a selection problem where the applicants compete for a limited number of positions.

Fairness

Cardiac Complication Risk Profiling for Cancer Survivors via Multi-View Multi-Task Learning

1 code implementation25 Sep 2021 Thai-Hoang Pham, Changchang Yin, Laxmi Mehta, Xueru Zhang, Ping Zhang

In particular, MuViTaNet complements patient representation by using a multi-view encoder to effectively extract information by considering clinical data as both sequences of clinical visits and sets of clinical features.

Multi-Task Learning

Improving Fairness and Privacy in Selection Problems

no code implementations7 Dec 2020 Mohammad Mahdi Khalili, Xueru Zhang, Mahed Abroshan, Somayeh Sojoudi

In this work, we study the possibility of using a differentially private exponential mechanism as a post-processing step to improve both fairness and privacy of supervised learning models.

Decision Making Fairness

How Do Fair Decisions Fare in Long-term Qualification?

1 code implementation NeurIPS 2020 Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellström, Kun Zhang, Cheng Zhang

Our results show that static fairness constraints can either promote equality or exacerbate disparity depending on the driving factor of qualification transitions and the effect of sensitive attributes on feature distributions.

Decision Making Fairness

Fairness in Learning-Based Sequential Decision Algorithms: A Survey

no code implementations14 Jan 2020 Xueru Zhang, Mingyan Liu

However, in practice most decision-making processes are of a sequential nature, where decisions made in the past may have an impact on future data.

Decision Making Fairness

Recycled ADMM: Improving the Privacy and Accuracy of Distributed Algorithms

no code implementations8 Oct 2019 Xueru Zhang, Mohammad Mahdi Khalili, Mingyan Liu

It can be shown that the privacy-accuracy tradeoff can be improved significantly compared with conventional ADMM.

Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness

no code implementations NeurIPS 2019 Xueru Zhang, Mohammad Mahdi Khalili, Cem Tekin, Mingyan Liu

Machine Learning (ML) models trained on data from multiple demographic groups can inherit representation disparity (Hashimoto et al., 2018) that may exist in the data: the model may be less favorable to groups contributing less to the training process; this in turn can degrade population retention in these groups over time, and exacerbate representation disparity in the long run.

Decision Making Fairness

Recycled ADMM: Improve Privacy and Accuracy with Less Computation in Distributed Algorithms

no code implementations7 Oct 2018 Xueru Zhang, Mohammad Mahdi Khalili, Mingyan Liu

Alternating direction method of multiplier (ADMM) is a powerful method to solve decentralized convex optimization problems.

Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms

no code implementations ICML 2018 Xueru Zhang, Mohammad Mahdi Khalili, Mingyan Liu

Alternating direction method of multiplier (ADMM) is a popular method used to design distributed versions of a machine learning algorithm, whereby local computations are performed on local data with the output exchanged among neighbors in an iterative fashion.

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