Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Tec, Mauricio"'
Autor:
Battiloro, Claudio, Karaismailoğlu, Ege, Tec, Mauricio, Dasoulas, George, Audirac, Michelle, Dominici, Francesca
Graph neural networks excel at modeling pairwise interactions, but they cannot flexibly accommodate higher-order interactions and features. Topological deep learning (TDL) has emerged recently as a promising tool for addressing this issue. TDL enable
Externí odkaz:
http://arxiv.org/abs/2405.15429
Autor:
Considine, Ellen M., Nethery, Rachel C., Wellenius, Gregory A., Dominici, Francesca, Tec, Mauricio
A key strategy in societal adaptation to climate change is the use of alert systems to reduce the adverse health impacts of extreme heat events by prompting preventative action. In this work, we investigate reinforcement learning (RL) as a tool to op
Externí odkaz:
http://arxiv.org/abs/2312.14196
Autor:
Tec, Mauricio, Trisovic, Ana, Audirac, Michelle, Woodward, Sophie, Hu, Jie Kate, Khoshnevis, Naeem, Dominici, Francesca
Spatial confounding poses a significant challenge in scientific studies involving spatial data, where unobserved spatial variables can influence both treatment and outcome, possibly leading to spurious associations. To address this problem, we introd
Externí odkaz:
http://arxiv.org/abs/2312.00710
In policy research, one of the most critical analytic tasks is to estimate the causal effect of a policy-relevant shift to the distribution of a continuous exposure/treatment on an outcome of interest. We call this problem shift-response function (SR
Externí odkaz:
http://arxiv.org/abs/2302.02560
A fundamental task in science is to design experiments that yield valuable insights about the system under study. Mathematically, these insights can be represented as a utility or risk function that shapes the value of conducting each experiment. We
Externí odkaz:
http://arxiv.org/abs/2210.12122
Publikováno v:
AAAI 2023
Estimating the causal effects of a spatially-varying intervention on a spatially-varying outcome may be subject to non-local confounding (NLC), a phenomenon that can bias estimates when the treatments and outcomes of a given unit are dictated in part
Externí odkaz:
http://arxiv.org/abs/2209.12316
Reinforcement Learning (RL) is a computational approach to reward-driven learning in sequential decision problems. It implements the discovery of optimal actions by learning from an agent interacting with an environment rather than from supervised da
Externí odkaz:
http://arxiv.org/abs/2205.04023
Analysis of observational studies increasingly confronts the challenge of determining which of a possibly high-dimensional set of available covariates are required to satisfy the assumption of ignorable treatment assignment for estimation of causal e
Externí odkaz:
http://arxiv.org/abs/2203.11798
Publikováno v:
iGISc 2021: Advances in Geospatial Data Science pp 163-175
In August 2020, as Texas was coming down from a large summer COVID-19 surge, forecasts suggested that Hurricane Laura was tracking towards 6M residents along the East Texas coastline, threatening to spread COVID-19 across the state and cause pandemic
Externí odkaz:
http://arxiv.org/abs/2203.00136
Learning with an objective to minimize the mismatch with a reference distribution has been shown to be useful for generative modeling and imitation learning. In this paper, we investigate whether one such objective, the Wasserstein-1 distance between
Externí odkaz:
http://arxiv.org/abs/2105.13345