Neural Information Processing Systems http://nips.cc/

Recent attempts to achieve fairness in predictive models focus on the balance between fairness and accuracy.

Neural Information Processing Systems http://nips.cc/

In order to build more useful AI systems, a natural inclination is to try to make them more agentic . But while agents built from language models are touted as the next big advance [Wang et al., 2024],

Whereas Perdomo et al. care only about predictive accuracy, we care also about the quality of the actual outcomes10 associated with decisions, and study the tradeoff between decision improvement and predictive accuracy. Consider,forexample, mortgage13 buyers, ICU patients, orfirst-time medical consultation (e.g., oncology,cardiology,psychiatry,screening tests). Thisisacausalproblem,and18 as such, requires assumptions that ensure causal validity (in our work this comes in through our use of propensity19 scoresforreweightingevidence). Undesired outcomes, as in the policing example, are the result of30 sample bias indata (missing observations) andoftheinappropriate useofpredictivetools fordecision making.

Modern engineering and scientific workflows frequently require simultaneous prediction across related tasks and fidelity levels [1-6]. In such contexts, some outputs are scarce and expensive to obtain, while others are cheaper and more abundant. Multi-task Gaussian processes (MTGPs), also known as multi-output Gaussian processes, offer a principled Bayesian framework to exploit inter-task correlations, enabling knowledge sharing that improves predictive accuracy and reduces the demand for large high-fidelity datasets [7-9]. Over decades of development, MTGPs have been applied across diverse domains, including time series forecasting, multitask optimization, and multifidelity classification, demonstrating their broad utility wherever data cost asymmetries and cross-task dependencies are present [10-16]. The central motivation for MTGPs is to leverage dependencies among related tasks to enhance predictive quality when high-fidelity information is limited [17]. For example, predicting an airfoil's lift coefficient from limited, expensive high-fidelity computational fluid dynamics (CFD) simulations can benefit from correlating with sufficient low-fidelity simulations [3]. Recent work in joint multi-objective and multifidelity optimization has also utilized MT - GPs to balance exploration and exploitation across tasks, improving predictive performance and decision-making by explicitly modeling relationships among outputs and fidelities [12].