42 citations · 56 across the 12 of their papers we have counts for
14 papers
FINESSE: An Agent-Based Simulator and Benchmark Dataset for Multimodal Financial Event Sequences
Tyler Farnan, Benjamin Eng, Adam Abate +4
Machine learning research in financial services is limited by the scarcity of representative open-source datasets. Existing resources are often narrowly focused on a single modalit…
Multi-Label Node Classification with Label Influence Propagation
Yifei Sun, Zemin Liu, Bryan Hooi +4
Graphs are a complex and versatile data structure used across various domains, with possibly multi-label nodes playing a particularly crucial role. Examples include proteins in PPI…
Audited Conformal Prediction for Classification under Unknown Distribution Shift
Yanfei Zhou, Rizal Fathony, Nam H. Nguyen +1
We consider the problem of uncertainty quantification for a pretrained classification model deployed under unknown distribution shift. We propose Audited Conformal Prediction (ACP)…
The Hidden Bias of Process Reward Models:PRISM for Rewarding the Right Reasoning
Aakriti Agrawal, Souradip Chakraborty, Armin Saghafian +6
Process Reward Models (PRMs) improve credit assignment for reasoning by providing step-level feedback. However, we identify a hidden bias in PRMs caused by severe imbalance in step…
When Graph Tokens Sink: A Mechanistic Analysis of Graph Language Models
Ding Zhang, Runtao Zhou, Wenqing Zheng +3
Graph Language Models (GLMs) have become a promising direction for adapting Large Language Models (LLMs) to graph learning tasks. By transforming graph topology and node informatio…
Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Tasks
Rizal Fathony, Igor Melnyk, Owen Reinert +3
User event modeling plays a central role in many machine learning applications, with use cases spanning e-commerce, social media, finance, cybersecurity, and other domains. User ev…
Bridging the Divide: End-to-End Sequence-Graph Learning
Yuen Chen, Yulun Wu, Samuel Sharpe +5
Many real-world prediction tasks, particularly those involving entities such as customers or patients, involve both {sequential} and {relational} data. Each entity maintains its ow…
Partitioning Message Passing for Graph Fraud Detection
Wei Zhuo, Zemin Liu, Bryan Hooi +4
Label imbalance and homophily-heterophily mixture are the fundamental problems encountered when applying Graph Neural Networks (GNNs) to Graph Fraud Detection (GFD) tasks. Existing…
Fairness for Robust Learning to Rank
Omid Memarrast, Ashkan Rezaei, Rizal Fathony +1
While conventional ranking systems focus solely on maximizing the utility of the ranked items to users, fairness-aware ranking systems additionally try to balance the exposure for…
AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning
Rizal Fathony, J. Zico Kolter
We propose a method that enables practitioners to conveniently incorporate custom non-decomposable performance metrics into differentiable learning pipelines, notably those based u…
Fairness for Robust Log Loss Classification
Ashkan Rezaei, Rizal Fathony, Omid Memarrast +1
Developing classification methods with high accuracy that also avoid unfair treatment of different groups has become increasingly important for data-driven decision making in socia…
Consistent Robust Adversarial Prediction for General Multiclass Classification
Rizal Fathony, Kaiser Asif, Anqi Liu +5
We propose a robust adversarial prediction framework for general multiclass classification. Our method seeks predictive distributions that robustly optimize non-convex and non-cont…
Distributionally Robust Graphical Models
Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri +2
In many structured prediction problems, complex relationships between variables are compactly defined using graphical structures. The most prevalent graphical prediction methods---…
Kernel Robust Bias-Aware Prediction under Covariate Shift
Anqi Liu, Rizal Fathony, Brian D. Ziebart
Under covariate shift, training (source) data and testing (target) data differ in input space distribution, but share the same conditional label distribution. This poses a challeng…