7 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…
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…
PersonaLedger: Generating Realistic Financial Transactions with Persona Conditioned LLMs and Rule Grounded Feedback
Dehao Yuan, Tyler Farnan, Stefan Tesliuc +8
Strict privacy regulations limit access to real transaction data, slowing open research in financial AI. Synthetic data can bridge this gap, but existing generators do not jointly…
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…
Leveraging AI for Enhanced Software Effort Estimation: A Comprehensive Study and Framework Proposal
Nhi Tran, Tan Tran, Nam Nguyen
This paper presents an extensive study on the application of AI techniques for software effort estimation in the past five years from 2017 to 2023. By overcoming the limitations of…