42 citations · 56 across the 12 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…