2024

  1. Simultaneously Detecting Node and Edge Level Anomalies on Heterogeneous Attributed Graphs Rizal Fathony, Jenn Ng, Jia Chen Conference: The IEEE World Congress on Computational Intelligence (WCCI) 2024 Link PDF Slides Code ©
  2. Consistency Training with Learnable Data Augmentation for Graph Anomaly Detection with Limited Supervision Nan Chen, Zemin Liu, Bryan Hooi, Bingsheng He, Rizal Fathony, Jun Hu, Jia Chen Conference: International Conference on Learning Representations (ICLR) 2024 Link PDF
  3. Partitioning Message Passing for Graph Fraud Detection Wei Zhuo, Zemin Liu, Bryan Hooi, Bingsheng He, Guang Tan, Rizal Fathony, Jia Chen Conference: International Conference on Learning Representations (ICLR) 2024 Link PDF

2023

  1. Interaction-Focused Anomaly Detection on Bipartite Node-and-Edge-Attributed Graphs Rizal Fathony, Jenn Ng, Jia Chen Conference: International Joint Conference on Neural Networks (IJCNN) 2023 Link Blog PDF Slides Code
  2. Fairness for Robust Learning to Rank Omid Memarrast, Ashkan Rezaei, Rizal Fathony, and Brian Ziebart Conference: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2023 Link PDF

2022

  1. PropInit: Scalable Inductive Initialization for Heterogeneous Graph Neural Networks Soji Adeshina, Jian Zhang, Muhyun Kim, Min Chen, Rizal Fathony, Advitiya Vashisht, Jia Chen, George Karypis Conference: IEEE International Conference on Knowledge Graph (ICKG 2022) Link PDF

2021

  1. Fairness for Robust Learning to Rank Omid Memarrast, Ashkan Rezaei, Rizal Fathony, and Brian Ziebart Workshop: Algorithmic Fairness through the lens of Causality and Robustness
    @ NeurIPS 2021     Link
  2. Multiplicative Filter Networks Rizal Fathony*⬦, Anit Kumar Sahu*⬦, Devin Wilmott*⬦, and J. Zico Kolter Conference: International Conference on Learning Representations (ICLR) 2021 Link PDF Slides Code

2020

  1. AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning Rizal Fathony, and J. Zico Kolter Conference: International Conference on Artificial Intelligence and Statistics (AISTATS) 2020 Link PDF Slides Code (Julia) Code (Python)
  2. Fairness for Robust Log Loss Classification Ashkan Rezaei*, Rizal Fathony*, Omid Memarrast, and Brian Ziebart Conference: AAAI Conference on Artificial Intelligence (AAAI) 2020 Link

2019

  1. AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning Rizal Fathony, and J. Zico Kolter Preprint: arXiv preprint 2019 Link
  2. Fairness for Robust Log Loss Classification Ashkan Rezaei*, Rizal Fathony*, Omid Memarrast, and Brian Ziebart Workshop: NeurIPS 2019 Workshop on Machine Learning with Guarantees 2019 Link
  3. Distributionally Robust Graphical Models Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, and Brian Ziebart Workshop: The 3rd Workshop On Tractable Probabilistic Modeling @ ICML 2019
  4. Performance-Aligned Learning Algorithms with Statistical Guarantees Rizal Fathony PhD Thesis: University of Illinois at Chicago 2019 PDF Slides

2018

  1. Distributionally Robust Graphical Models Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, and Brian Ziebart Conference: Advances in Neural Information Processing Systems (NeurIPS) 2018 Link PDF Poster Code
  2. Consistent Robust Adversarial Prediction for General Multiclass Classification Rizal Fathony, Kaiser Asif, Anqi Liu, Mohammad Ali Bashiri, Wei Xing, Sima Behpour, Xinhua Zhang, and Brian D Ziebart Preprint: arXiv preprint 2018 Link
  3. Efficient and Consistent Adversarial Bipartite Matching Rizal Fathony*, Sima Behpour*, Xinhua Zhang, and Brian Ziebart Conference: International Conference on Machine Learning (ICML) 2018 Link PDF Poster Slides Code

2017

  1. Adversarial Surrogate Losses for Ordinal Regression Rizal Fathony, Mohammad Ali Bashiri, and Brian Ziebart Conference: Advances in Neural Information Processing Systems (NeurIPS) 2017 Link PDF Poster Code
  2. Kernel Robust Bias-Aware Prediction under Covariate Shift Anqi Liu, Rizal Fathony, and Brian D Ziebart Preprint: arXiv preprint 2017 Link

2016

  1. Adversarial Multiclass Classification: A Risk Minimization Perspective Rizal Fathony, Anqi Liu, Kaiser Asif, and Brian Ziebart Conference: Advances in Neural Information Processing Systems (NeurIPS) 2016 Link PDF Poster Code