Search Results for author: Ziwei Zhu

Found 7 papers, 2 papers with code

Supervised Homogeneity Fusion: a Combinatorial Approach

no code implementations4 Jan 2022 Wen Wang, Shihao Wu, Ziwei Zhu, Ling Zhou, Peter X. -K. Song

Fusing regression coefficients into homogenous groups can unveil those coefficients that share a common value within each group.

Session-based Recommendation with Hypergraph Attention Networks

no code implementations28 Dec 2021 Jianling Wang, Kaize Ding, Ziwei Zhu, James Caverlee

Session-based recommender systems aim to improve recommendations in short-term sessions that can be found across many platforms.

Session-Based Recommendations

End-to-end Learning for Fair Ranking Systems

no code implementations21 Nov 2021 James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, Ziwei Zhu

The end-to-end SPOFR framework includes a constrained optimization sub-model and produces ranking policies that are guaranteed to satisfy fairness constraints while allowing for fine control of the fairness-utility tradeoff.

Fairness Learning-To-Rank

Fairness-aware Personalized Ranking Recommendation via Adversarial Learning

1 code implementation14 Mar 2021 Ziwei Zhu, Jianling Wang, James Caverlee

This is paper is an extended and reorganized version of our SIGIR 2020~\cite{zhu2020measuring} paper.

Fairness Frame +1

Learning Markov models via low-rank optimization

no code implementations28 Jun 2019 Ziwei Zhu, Xudong Li, Mengdi Wang, Anru Zhang

We show that one can estimate the full transition model accurately using a trajectory of length that is proportional to the total number of states.

Decision Making

Robust high dimensional factor models with applications to statistical machine learning

no code implementations12 Aug 2018 Jianqing Fan, Kaizheng Wang, Yiqiao Zhong, Ziwei Zhu

Factor models are a class of powerful statistical models that have been widely used to deal with dependent measurements that arise frequently from various applications from genomics and neuroscience to economics and finance.

Model Selection

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