10 research outputs found
Compositional trend filtering
Trend filtering is known as the technique for detecting piecewise linear trends in univariate time series. This technique is extended to the setting of compositional data, which are multivariate data where only the relative information is of importance. According to this, we formulate the problem and present a procedure how to efficiently solve it. To show the usefulness of this method, we consider the number of COVID-19 infections in several European countries in a chosen time period
Compositional trend filtering
Trend filtering is known as the technique for detecting piecewise linear
trends in univariate time series. This technique is extended to the setting
of compositional data, which are multivariate data where only the relative
information is of importance. According to this, we formulate the problem
and present a procedure how to efficiently solve it. To show the usefulness
of this method, we consider the number of COVID-19 infections in several
European countries in a chosen time period
Chasing Fairness in Graphs: A GNN Architecture Perspective
There has been significant progress in improving the performance of graph
neural networks (GNNs) through enhancements in graph data, model architecture
design, and training strategies. For fairness in graphs, recent studies achieve
fair representations and predictions through either graph data pre-processing
(e.g., node feature masking, and topology rewiring) or fair training strategies
(e.g., regularization, adversarial debiasing, and fair contrastive learning).
How to achieve fairness in graphs from the model architecture perspective is
less explored. More importantly, GNNs exhibit worse fairness performance
compared to multilayer perception since their model architecture (i.e.,
neighbor aggregation) amplifies biases. To this end, we aim to achieve fairness
via a new GNN architecture. We propose \textsf{F}air \textsf{M}essage
\textsf{P}assing (FMP) designed within a unified optimization framework for
GNNs. Notably, FMP \textit{explicitly} renders sensitive attribute usage in
\textit{forward propagation} for node classification task using cross-entropy
loss without data pre-processing. In FMP, the aggregation is first adopted to
utilize neighbors' information and then the bias mitigation step explicitly
pushes demographic group node presentation centers together. In this way, FMP
scheme can aggregate useful information from neighbors and mitigate bias to
achieve better fairness and prediction tradeoff performance. Experiments on
node classification tasks demonstrate that the proposed FMP outperforms several
baselines in terms of fairness and accuracy on three real-world datasets. The
code is available in {\url{https://github.com/zhimengj0326/FMP}}.Comment: Accepted by AAAI Conference on Artificial Intelligence (AAAI) 2024.
arXiv admin note: substantial text overlap with arXiv:2202.0418