Zobrazeno 1 - 10
of 2 488
pro vyhledávání: '"Tambe, P."'
Neural Jump ODEs model the conditional expectation between observations by neural ODEs and jump at arrival of new observations. They have demonstrated effectiveness for fully data-driven online forecasting in settings with irregular and partial obser
Externí odkaz:
http://arxiv.org/abs/2412.03271
While advances in machine learning with satellite imagery (SatML) are facilitating environmental monitoring at a global scale, developing SatML models that are accurate and useful for local regions remains critical to understanding and acting on an e
Externí odkaz:
http://arxiv.org/abs/2411.14354
Autor:
Dasgupta, Arpan, Jain, Gagan, Suggala, Arun, Shanmugam, Karthikeyan, Tambe, Milind, Taneja, Aparna
Mobile health (mHealth) programs face a critical challenge in optimizing the timing of automated health information calls to beneficiaries. This challenge has been formulated as a collaborative multi-armed bandit problem, requiring online learning of
Externí odkaz:
http://arxiv.org/abs/2410.21405
Multiagent systems grapple with partial observability (PO), and the decentralized POMDP (Dec-POMDP) model highlights the fundamental nature of this challenge. Whereas recent approaches to addressing PO have appealed to deep learning models, providing
Externí odkaz:
http://arxiv.org/abs/2410.13953
Autor:
Boehmer, Niclas, Zhao, Yunfan, Xiong, Guojun, Rodriguez-Diaz, Paula, Cibrian, Paola Del Cueto, Ngonzi, Joseph, Boatin, Adeline, Tambe, Milind
Maternal mortality remains a significant global public health challenge. One promising approach to reducing maternal deaths occurring during facility-based childbirth is through early warning systems, which require the consistent monitoring of mother
Externí odkaz:
http://arxiv.org/abs/2410.08377
Autor:
Gordon, Lucia, Behari, Nikhil, Collier, Samuel, Bondi-Kelly, Elizabeth, Killian, Jackson A., Ressijac, Catherine, Boucher, Peter, Davies, Andrew, Tambe, Milind
Publikováno v:
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. AI for Good. Pages 5977-5985. 2023
Much of Earth's charismatic megafauna is endangered by human activities, particularly the rhino, which is at risk of extinction due to the poaching crisis in Africa. Monitoring rhinos' movement is crucial to their protection but has unfortunately pro
Externí odkaz:
http://arxiv.org/abs/2409.18104
Isospin-equilibrating weak processes, called ``Urca" processes, are of fundamental importance in astrophysical environments like (proto-)neutron stars, neutron star mergers, and supernovae. In these environments, matter can reach high temperatures of
Externí odkaz:
http://arxiv.org/abs/2409.09423
Comparing datasets is a fundamental task in machine learning, essential for various learning paradigms; from evaluating train and test datasets for model generalization to using dataset similarity for detecting data drift. While traditional notions o
Externí odkaz:
http://arxiv.org/abs/2409.06997
For public health programs with limited resources, the ability to predict how behaviors change over time and in response to interventions is crucial for deciding when and to whom interventions should be allocated. Using data from a real-world materna
Externí odkaz:
http://arxiv.org/abs/2408.16147
LLMs are increasingly used to design reward functions based on human preferences in Reinforcement Learning (RL). We focus on LLM-designed rewards for Restless Multi-Armed Bandits, a framework for allocating limited resources among agents. In applicat
Externí odkaz:
http://arxiv.org/abs/2408.12112