Rizal Fathony - RankMe

23 papers A* 7A 1B 2Journal 9Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Bridging the Divide: End-to-End Sequence-Graph Learning. CoRR Yuen Chen, Yulun Wu, Samuel Sharpe, Igor Melnyk, Nam H. Nguyen, Furong Huang, C. Bayan Bruss, Rizal Fathony
2025	J	`jnl`	Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Tasks. CoRR Rizal Fathony, Igor Melnyk, Owen Reinert, Nam H. Nguyen, Daniele Rosa, C. Bayan Bruss
2025	A*	`conf`	Multi-Label Node Classification with Label Influence Propagation. ICLR Yifei Sun, Zemin Liu, Bryan Hooi, Yang Yang, Rizal Fathony, Jia Chen, Bingsheng He
2024	A*	`conf`	Consistency Training with Learnable Data Augmentation for Graph Anomaly Detection with Limited Supervision. ICLR Nan Chen, Zemin Liu, Bryan Hooi, Bingsheng He, Rizal Fathony, Jun Hu, Jia Chen
2024	A*	`conf`	Partitioning Message Passing for Graph Fraud Detection. ICLR Wei Zhuo, Zemin Liu, Bryan Hooi, Bingsheng He, Guang Tan, Rizal Fathony, Jia Chen
2024	J	`jnl`	Partitioning Message Passing for Graph Fraud Detection. CoRR Wei Zhuo, Zemin Liu, Bryan Hooi, Bingsheng He, Guang Tan, Rizal Fathony, Jia Chen
2024	B	`conf`	Simultaneously Detecting Node and Edge Level Anomalies on Heterogeneous Attributed Graphs. IJCNN Rizal Fathony, Jenn Ng, Jia Chen
2023	—	`conf`	Fairness for Robust Learning to Rank. PAKDD (1) Omid Memarrast, Ashkan Rezaei, Rizal Fathony, Brian D. Ziebart
2023	B	`conf`	Interaction-Focused Anomaly Detection on Bipartite Node-and-Edge-Attributed Graphs. IJCNN Rizal Fathony, Jenn Ng, Jia Chen
2022	—	`conf`	PropInit: Scalable Inductive Initialization for Heterogeneous Graph Neural Networks. ICKG Soji Adeshina, Jian Zhang, Muhyun Kim, Min Chen, Rizal Fathony, Advitiya Vashisht, Jia Chen, George Karypis
2021	J	`jnl`	Fairness for Robust Learning to Rank. CoRR Omid Memarrast, Ashkan Rezaei, Rizal Fathony, Brian D. Ziebart
2021	A*	`conf`	Multiplicative Filter Networks. ICLR Rizal Fathony, Anit Kumar Sahu, Devin Willmott, J. Zico Kolter
2020	A	`conf`	AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning. AISTATS Rizal Fathony, J. Zico Kolter
2020	A*	`conf`	Fairness for Robust Log Loss Classification. AAAI Ashkan Rezaei, Rizal Fathony, Omid Memarrast, Brian D. Ziebart
2019	J	`jnl`	AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning. CoRR Rizal Fathony, J. Zico Kolter
2019	J	`jnl`	Fair Logistic Regression: An Adversarial Perspective. CoRR Ashkan Rezaei, Rizal Fathony, Omid Memarrast, Brian D. Ziebart
2018	J	`jnl`	Consistent Robust Adversarial Prediction for General Multiclass Classification. CoRR Rizal Fathony, Kaiser Asif, Anqi Liu, Mohammad Ali Bashiri, Wei Xing, Sima Behpour, Xinhua Zhang, Brian D. Ziebart
2018	A*	`conf`	Distributionally Robust Graphical Models. NeurIPS Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian D. Ziebart
2018	J	`jnl`	Distributionally Robust Graphical Models. CoRR Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian D. Ziebart
2018	A*	`conf`	Efficient and Consistent Adversarial Bipartite Matching. ICML Rizal Fathony, Sima Behpour, Xinhua Zhang, Brian D. Ziebart
2017	—	`conf`	Adversarial Surrogate Losses for Ordinal Regression. NIPS Rizal Fathony, Mohammad Ali Bashiri, Brian D. Ziebart
2017	J	`jnl`	Kernel Robust Bias-Aware Prediction under Covariate Shift. CoRR Anqi Liu, Rizal Fathony, Brian D. Ziebart
2016	—	`conf`	Adversarial Multiclass Classification: A Risk Minimization Perspective. NIPS Rizal Fathony, Anqi Liu, Kaiser Asif, Brian D. Ziebart