Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Rolf, Esther"'
Stochastic multi-agent multi-armed bandits typically assume that the rewards from each arm follow a fixed distribution, regardless of which agent pulls the arm. However, in many real-world settings, rewards can depend on the sensitivity of each agent
Externí odkaz:
http://arxiv.org/abs/2408.03405
Autor:
Rolnick, David, Aspuru-Guzik, Alan, Beery, Sara, Dilkina, Bistra, Donti, Priya L., Ghassemi, Marzyeh, Kerner, Hannah, Monteleoni, Claire, Rolf, Esther, Tambe, Milind, White, Adam
As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly important. Such work offers the potential for significant impact not merely in domains of application but also
Externí odkaz:
http://arxiv.org/abs/2403.17381
Satellite data has the potential to inspire a seismic shift for machine learning -- one in which we rethink existing practices designed for traditional data modalities. As machine learning for satellite data (SatML) gains traction for its real-world
Externí odkaz:
http://arxiv.org/abs/2402.01444
Geographic information is essential for modeling tasks in fields ranging from ecology to epidemiology. However, extracting relevant location characteristics for a given task can be challenging, often requiring expensive data fusion or distillation fr
Externí odkaz:
http://arxiv.org/abs/2311.17179
Publikováno v:
Published as a conference paper at ICLR 2024
Learning representations of geographical space is vital for any machine learning model that integrates geolocated data, spanning application domains such as remote sensing, ecology, or epidemiology. Recent work embeds coordinates using sine and cosin
Externí odkaz:
http://arxiv.org/abs/2310.06743
Autor:
Xu, Lily, Rolf, Esther, Beery, Sara, Bennett, Joseph R., Berger-Wolf, Tanya, Birch, Tanya, Bondi-Kelly, Elizabeth, Brashares, Justin, Chapman, Melissa, Corso, Anthony, Davies, Andrew, Garg, Nikhil, Gaylard, Angela, Heilmayr, Robert, Kerner, Hannah, Klemmer, Konstantin, Kumar, Vipin, Mackey, Lester, Monteleoni, Claire, Moorcroft, Paul, Palmer, Jonathan, Perrault, Andrew, Thau, David, Tambe, Milind
In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-2
Externí odkaz:
http://arxiv.org/abs/2307.08774
Publikováno v:
IJCAI 2023 - AI for Social Good Track
Poverty maps derived from satellite imagery are increasingly used to inform high-stakes policy decisions, such as the allocation of humanitarian aid and the distribution of government resources. Such poverty maps are typically constructed by training
Externí odkaz:
http://arxiv.org/abs/2305.01783
Autor:
Rolf, Esther
As geospatial machine learning models and maps derived from their predictions are increasingly used for downstream analyses in science and policy, it is imperative to evaluate their accuracy and applicability. Geospatial machine learning has key dist
Externí odkaz:
http://arxiv.org/abs/2303.18087
Machine learning-based estimates of poverty and wealth are increasingly being used to guide the targeting of humanitarian aid and the allocation of social assistance. However, the ground truth labels used to train these models are typically borrowed
Externí odkaz:
http://arxiv.org/abs/2211.08735
Building trustworthy, effective, and responsible machine learning systems hinges on understanding how differences in training data and modeling decisions interact to impact predictive performance. In this work, we seek to better understand how we mig
Externí odkaz:
http://arxiv.org/abs/2211.06348