27 citations · 95 across the 53 of their papers we have counts for
18 papers · 1 filter
Adaptive Ensemble Selection for Noisy Labels on Tabular Data
Faizaan Ali, Inwon Kang, Oshani Seneviratne
Incorrect or corrupted labels in tabular datasets can significantly degrade supervised learning performance, particularly when mislabeling is subtle and not easily detectable from…
When Connected Does Not Mean Similar: Charting the Homophily Boundary of SNAP-KG for Streaming Entity Integration
Jui-Chien Lin, Oshani Seneviratne
SNAP-KG is a framework for assigning newly arriving entities to semantic communities in a growing knowledge graph (KG) using only their raw features, with no graph access and no re…
SNAP-KG: Streaming Node Assignment via Projection for Knowledge Graph Entity Integration
Jui-Chien Lin, Mohammad Mohammadi Amiri, Oshani Seneviratne
Knowledge graph (KG) construction pipelines must continuously integrate newly arriving entities into a growing graph. Unlike inserting triples between existing nodes, a newly arriv…
SemStruct: Contextualizing Semantic Embeddings with Structural Information for Schema Matching
Inwon Kang, Kavitha Srinivas, Nandana Mihindukulasooriya +4
Schema matching is a fundamental step in integrating heterogeneous data sources. While Pre-trained Language Models (PLMs) have revolutionized this task by capturing linguistic sema…
From Risk to Rescue: An Agentic Survival Analysis Framework for Liquidation Prevention
Fernando Spadea, Oshani Seneviratne
Decentralized Finance (DeFi) lending protocols like Aave v3 rely on over-collateralization to secure loans, yet users frequently face liquidation due to volatile market conditions.…
Benchmarking Temporal Web3 Intelligence: Lessons from the FinSurvival 2025 Challenge
Oshani Seneviratne, Fernando Spadea, Adrien Pavao +2
Temporal Web analytics increasingly relies on large-scale, longitudinal data to understand how users, content, and systems evolve over time. A rapidly growing frontier is the \emph…
Federated Personal Knowledge Graph Completion with Lightweight Large Language Models for Personalized Recommendations
Fernando Spadea, Oshani Seneviratne
Personalized recommendation increasingly relies on private user data, motivating approaches that can adapt to individuals without centralizing their information. We present Federat…
Measuring Privacy Risks and Tradeoffs in Financial Synthetic Data Generation
Michael Zuo, Inwon Kang, Stacy Patterson +1
We explore the privacy-utility tradeoff of synthetic data generation schemes on tabular financial datasets, a domain characterized by high regulatory risk and severe class imbalanc…
Parallel and Multi-Stage Knowledge Graph Retrieval for Behaviorally Aligned Financial Asset Recommendations
Fernando Spadea, Oshani Seneviratne
Large language models (LLMs) show promise for personalized financial recommendations but are hampered by context limits, hallucinations, and a lack of behavioral grounding. Our pri…
Curriculum-Guided Reinforcement Learning for Synthesizing Gas-Efficient Financial Derivatives Contracts
Maruf Ahmed Mridul, Oshani Seneviratne
Smart contract-based automation of financial derivatives offers substantial efficiency gains, but its real-world adoption is constrained by the complexity of translating financial…
Federated Fine-Tuning of Large Language Models: Kahneman-Tversky vs. Direct Preference Optimization
Fernando Spadea, Oshani Seneviratne
We evaluate Kahneman-Tversky Optimization (KTO) as a fine-tuning method for large language models (LLMs) in federated learning (FL) settings, comparing it against Direct Preference…
Blockchain-based Framework for Scalable and Incentivized Federated Learning
Bijun Wu, Oshani Seneviratne
Federated Learning (FL) enables collaborative model training without sharing raw data, preserving privacy while harnessing distributed datasets. However, traditional FL systems oft…
On Learning Representations for Tabular Data Distillation
Inwon Kang, Parikshit Ram, Yi Zhou +2
Dataset distillation generates a small set of information-rich instances from a large dataset, resulting in reduced storage requirements, privacy or copyright risks, and computatio…
Predicting Depression and Anxiety: A Multi-Layer Perceptron for Analyzing the Mental Health Impact of COVID-19
David Fong, Tianshu Chu, Matthew Heflin +2
We introduce a multi-layer perceptron (MLP) called the COVID-19 Depression and Anxiety Predictor (CoDAP) to predict mental health trends, particularly anxiety and depression, durin…
PredictChain: Empowering Collaboration and Data Accessibility for AI in a Decentralized Blockchain-based Marketplace
Matthew T. Pisano, Connor J. Patterson, Oshani Seneviratne
Limited access to computing resources and training data poses significant challenges for individuals and groups aiming to train and utilize predictive machine learning models. Alth…
MentalHealthAI: Utilizing Personal Health Device Data to Optimize Psychiatry Treatment
Manan Shukla, Oshani Seneviratne
Mental health disorders remain a significant challenge in modern healthcare, with diagnosis and treatment often relying on subjective patient descriptions and past medical history.…
Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes
Shruthi Chari, Prasant Acharya, Daniel M. Gruen +8
Medical experts may use Artificial Intelligence (AI) systems with greater trust if these are supported by contextual explanations that let the practitioner connect system inference…
Leveraging Clinical Context for User-Centered Explainability: A Diabetes Use Case
Shruthi Chari, Prithwish Chakraborty, Mohamed Ghalwash +7
Academic advances of AI models in high-precision domains, like healthcare, need to be made explainable in order to enhance real-world adoption. Our past studies and ongoing interac…