Entity Aware Modelling: A Survey

Personalized prediction of responses for individual entities caused by external drivers is vital across many disciplines. Recent machine learning (ML) advances have led to new state-of-the-art response prediction models. Models built at a population level often lead to sub-optimal performance in many personalized prediction settings due to heterogeneity in data across entities (tasks). In personalized prediction, the goal is to incorporate inherent characteristics of different entities to improve prediction performance. In this survey, we focus on the recent developments in the ML community for such entity-aware modeling approaches. ML algorithms often modulate the network using these entity characteristics when they are readily available. However, these entity characteristics are not readily available in many real-world scenarios, and different ML methods have been proposed to infer these characteristics from the data. In this survey, we have organized the current literature on entity-aware modeling based on the availability of these characteristics as well as the amount of training data. We highlight how recent innovations in other disciplines, such as uncertainty quantification, fairness, and knowledge-guided machine learning, can improve entity-aware modeling.Comment: Submitted to IJCAI, Survey Trac

An intelligent interface for integrating climate, hydrology, agriculture, and socioeconomic models

Understanding the interactions between natural processes and human activities poses major challenges as it requires the integration of models and data across disparate disciplines. It typically takes many months and even years to create valid end-to-end simulations as different models need to be configured in consistent ways and generate data that is usable by other models. MINT is a novel framework for model integration that captures extensive knowledge about models and data and aims to automatically compose them together. MINT guides a user to pose a well-formed modeling question, select and configure appropriate models, find and prepare appropriate datasets, compose data and models into end-to-end workflows, run the simulations, and visualize the results. MINT currently includes hydrology, agriculture, and socioeconomic models.Office of the VP for Researc

Global Lake Monitoring using Group-specific Local Learning

Global lake monitoring is crucial for the effective management of water resources as well as for conducting studies that link the impact of lake dynamics on climate change. Remote sensing datasets offer an opportunity for global lake monitoring by providing discriminatory features that can help distinguish land and water bodies at a global scale and in a timely fashion. A major challenge in global lake monitoring using remote sensing datasets is the presence of a rich variety in the land and water bodies at a global scale, motivating the need for local learning algorithms that can take into account the heterogeneity in the data. We propose a novel group-specific local learning scheme that uses information about the local neighborhood of a group of test instances for estimating the relevant context for classification. By comparing the performance of the proposed scheme with baseline approaches over 180 lakes from diverse regions of the world, we are able to demonstrate that the proposed scheme provides significant improvements in the classification performance