Rizal Fathony

According to our database, Rizal Fathony authored at least 16 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Simultaneously Detecting Node and Edge Level Anomalies on Heterogeneous Attributed Graphs.

Rizal Fathony, Jenn Ng, Jia Chen

Proceedings of the International Joint Conference on Neural Networks, 2024

Partitioning Message Passing for Graph Fraud Detection.

Wei Zhuo, Zemin Liu, Bryan Hooi, Bingsheng He, Guang Tan, Rizal Fathony, Jia Chen

Proceedings of the Twelfth International Conference on Learning Representations, 2024

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Fairness for Robust Learning to Rank.

Omid Memarrast, Ashkan Rezaei, Rizal Fathony, Brian D. Ziebart

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2023

Interaction-Focused Anomaly Detection on Bipartite Node-and-Edge-Attributed Graphs.

Rizal Fathony, Jenn Ng, Jia Chen

Proceedings of the International Joint Conference on Neural Networks, 2023

2022

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

Proceedings of the IEEE International Conference on Knowledge Graph, 2022

2021

Multiplicative Filter Networks.

Rizal Fathony, Anit Kumar Sahu, Devin Willmott, J. Zico Kolter

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning.

Rizal Fathony, J. Zico Kolter

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Fairness for Robust Log Loss Classification.

Ashkan Rezaei, Rizal Fathony, Omid Memarrast, Brian D. Ziebart

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Fair Logistic Regression: An Adversarial Perspective.

Ashkan Rezaei, Rizal Fathony, Omid Memarrast, Brian D. Ziebart

CoRR, 2019

2018

Consistent Robust Adversarial Prediction for General Multiclass Classification.

Rizal Fathony, Kaiser Asif, Anqi Liu, Mohammad Ali Bashiri, Wei Xing, Sima Behpour, Xinhua Zhang, Brian D. Ziebart

CoRR, 2018

Distributionally Robust Graphical Models.

Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian D. Ziebart

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Efficient and Consistent Adversarial Bipartite Matching.

Rizal Fathony, Sima Behpour, Xinhua Zhang, Brian D. Ziebart

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Kernel Robust Bias-Aware Prediction under Covariate Shift.

Anqi Liu, Rizal Fathony, Brian D. Ziebart

CoRR, 2017

Adversarial Surrogate Losses for Ordinal Regression.

Rizal Fathony, Mohammad Ali Bashiri, Brian D. Ziebart

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Adversarial Multiclass Classification: A Risk Minimization Perspective.

Rizal Fathony, Anqi Liu, Kaiser Asif, Brian D. Ziebart

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016