Xu L, Biswas A, Fang F, Tambe M. Ranked Prioritization of Groups in Combinatorial Bandit Allocation, in 31st International Joint Conference on Artificial Intelligence (IJCAI 2022). ; Forthcoming. arXiv
Wang K, Wilder B, Perrault A, Tambe M. Automatically Learning Compact Quality-aware Surrogates for Optimization Problems, in NeurIPS (Spotlight). Vancouver, Canada ; 2020.Abstract
Solving optimization problems with unknown parameters often requires learning a predictive model to predict the values of the unknown parameters and then solvingthe problem using these values. Recent work has shown that including the optimization problem as a layer in the model training pipeline results in predictions of the unobserved parameters that lead to higher decision quality. Unfortunately, this process comes at a large computational cost because the optimization problem must be solved and differentiated through in each training iteration; furthermore, it may also sometimes fail to improve solution quality due to non-smoothness issues that arise when training through a complex optimization layer. To address these shortcomings, we learn a low-dimensional surrogate model of a large optimization problem by representing the feasible space in terms of meta-variables, each of which is a linear combination of the original variables. By training a low-dimensional surrogate model end-to-end, and jointly with the predictive model, we achieve: i) a large reduction in training and inference time; and ii) improved per-formance by focusing attention on the more important variables in the optimization and learning in a smoother space. Empirically, we demonstrate these improvements on a non-convex adversary modeling task, a submodular recommendation task and a convex portfolio optimization task.
Perrault A, Fang F, Sinha A, Tambe M. AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline. AI Magazine. 2020.Abstract
With the maturing of AI and multiagent systems research, we have a tremendous
opportunity to direct these advances towards addressing complex societal problems. In pursuit of this goal of AI for Social Impact, we as AI researchers must go beyond improvements in computational methodology; it is important to step out in the field to demonstrate social impact. To this end, we focus on the problems of public safety and security, wildlife conservation, and public health in low-resource communities, and present research advances in multiagent systems to address one key cross-cutting challenge: how to effectively deploy our limited intervention resources in these problem domains. We present case studies from our deployments around the world as well as lessons learned that we hope are of use to researchers who are interested in AI for Social Impact. In pushing this research agenda, we believe AI can indeed play an important
role in fighting social injustice and improving society.
Bayramli I, Bondi E, Tambe M. In the Shadow of Disaster: Finding Shadows to Improve Damage Detection, in AI for Social Good Workshop. ; 2020.Abstract

Rapid damage assessment after natural disasters is crucial for effective planning of relief efforts. Satellites with Very High Resolution (VHR) sensors can provide a detailed aerial image of the affected area, but current damage detection systems are fully- or semi-manual which can delay the delivery of emergency care. In this paper, we apply recent advancements in segmentation and change detection to detect damage given pre- and post-disaster VHR images of an affected area. Moreover, we demonstrate that segmentation models trained for this task rely on shadows by showing that (i) shadows influence false positive detections by the model, and (ii) removing shadows leads to poorer performance. Through this analysis, we aim to inspire future work to improve damage detection.

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In the Shadow of Disaster: Finding Shadows to Improve Damage Detection