no code implementations • 18 May 2024 • Marianne Arriola, Weishen Pan, Manqi Zhou, Qiannan Zhang, Chang Su, Fei Wang
Joint analysis of multi-omic single-cell data across cohorts has significantly enhanced the comprehensive analysis of cellular processes.
no code implementations • 25 Oct 2023 • Jacqueline Maasch, Weishen Pan, Shantanu Gupta, Volodymyr Kuleshov, Kyra Gan, Fei Wang
Causal discovery is crucial for causal inference in observational studies: it can enable the identification of valid adjustment sets (VAS) for unbiased effect estimation.
1 code implementation • 27 Jul 2023 • Sen Cui, Weishen Pan, ChangShui Zhang, Fei Wang
xOrder consistently achieves a better balance between the algorithm utility and ranking fairness on a variety of datasets with different metrics.
no code implementations • 14 Jun 2023 • Yingheng Wang, Yair Schiff, Aaron Gokaslan, Weishen Pan, Fei Wang, Christopher De Sa, Volodymyr Kuleshov
While diffusion models excel at generating high-quality samples, their latent variables typically lack semantic meaning and are not suitable for representation learning.
no code implementations • 10 May 2023 • Suraj Rajendran, Weishen Pan, Mert R. Sabuncu, Yong Chen, Jiayu Zhou, Fei Wang
By offering a more comprehensive approach to healthcare data integration, patchwork learning has the potential to revolutionize the clinical applicability of ML models.
no code implementations • 13 Sep 2021 • Weishen Pan, Sen Cui, Hongyi Wen, Kun Chen, ChangShui Zhang, Fei Wang
We empirically validated the existence of such user feedback-loop bias in real world recommendation systems and compared the performance of our method with the baseline models that are either without de-biasing or with propensity scores estimated by other methods.
1 code implementation • NeurIPS 2021 • Sen Cui, Weishen Pan, Jian Liang, ChangShui Zhang, Fei Wang
In this paper, we propose an FL framework to jointly consider performance consistency and algorithmic fairness across different local clients (data sources).
1 code implementation • 18 Aug 2021 • Sen Cui, Jian Liang, Weishen Pan, Kun Chen, ChangShui Zhang, Fei Wang
Federated learning (FL) refers to the paradigm of learning models over a collaborative research network involving multiple clients without sacrificing privacy.
no code implementations • 11 Aug 2021 • Weishen Pan, Sen Cui, Jiang Bian, ChangShui Zhang, Fei Wang
Algorithmic fairness has aroused considerable interests in data mining and machine learning communities recently.
no code implementations • 29 May 2021 • Weishen Pan, ChangShui Zhang
As machine learning algorithms getting adopted in an ever-increasing number of applications, interpretation has emerged as a crucial desideratum.
no code implementations • 1 Jan 2021 • Weishen Pan, Sen Cui, ChangShui Zhang
In this paper, we focus on the unsupervised learning of disentanglement in a general setting which the generative factors may be correlated.
1 code implementation • 15 Jun 2020 • Sen Cui, Weishen Pan, Chang-Shui Zhang, Fei Wang
Bipartite ranking, which aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data, is widely adopted in various applications where sample prioritization is needed.
no code implementations • 28 Feb 2017 • Ziang Yan, Jian Liang, Weishen Pan, Jin Li, Chang-Shui Zhang
Object detection when provided image-level labels instead of instance-level labels (i. e., bounding boxes) during training is an important problem in computer vision, since large scale image datasets with instance-level labels are extremely costly to obtain.