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28 papers · 1 filter
Expert-Driven Survival Machines: Improving Stratification and Interpretability in Multiple Clinical Cohorts
Farica Zhuang, Zixuan Wen, Christos Davatzikos +1
Survival prediction plays a central role for healthcare providers and clinical researchers. Accurate risk stratification enables early intervention and improved patient management.…
Towards symbolic regression for interpretable clinical decision scores
Guilherme Seidyo Imai Aldeia, Joseph D. Romano, Fabricio Olivetti de Franca +2
Medical decision-making makes frequent use of algorithms that combine risk equations with rules, providing clear and standardized treatment pathways. Symbolic regression (SR) tradi…
ClauseLens: Clause-Grounded, CVaR-Constrained Reinforcement Learning for Trustworthy Reinsurance Pricing
Stella C. Dong, James R. Finlay
Reinsurance treaty pricing must satisfy stringent regulatory standards, yet current quoting practices remain opaque and difficult to audit. We introduce ClauseLens, a clause-ground…
Prospective Learning in Retrospect
Yuxin Bai, Cecelia Shuai, Ashwin De Silva +3
In most real-world applications of artificial intelligence, the distributions of the data and the goals of the learners tend to change over time. The Probably Approximately Correct…
Composite Bayesian Optimization In Function Spaces Using NEON -- Neural Epistemic Operator Networks
Leonardo Ferreira Guilhoto, Paris Perdikaris
Operator learning is a rising field of scientific computing where inputs or outputs of a machine learning model are functions defined in infinite-dimensional spaces. In this paper,…
Reversible and irreversible bracket-based dynamics for deep graph neural networks
Anthony Gruber, Kookjin Lee, Nathaniel Trask
Recent works have shown that physics-inspired architectures allow the training of deep graph neural networks (GNNs) without oversmoothing. The role of these physics is unclear, how…