127 citations · 353 across the 13 of their papers we have counts for
17 papers
Approximate Inference via Weighted Rademacher Complexity
Jonathan Kuck, Ashish Sabharwal, Stefano Ermon
Rademacher complexity is often used to characterize the learnability of a hypothesis class and is known to be related to the class size. We leverage this observation and introduce…
Shape optimization in laminar flow with a label-guided variational autoencoder
Stephan Eismann, Stefan Bartzsch, Stefano Ermon
Computational design optimization in fluid dynamics usually requires to solve non-linear partial differential equations numerically. In this work, we explore a Bayesian optimizatio…
Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces
Daniel Levy, Stefano Ermon
Policy optimization methods have shown great promise in solving complex reinforcement and imitation learning tasks. While model-free methods are broadly applicable, they often requ…
Neural Variational Inference and Learning in Undirected Graphical Models
Volodymyr Kuleshov, Stefano Ermon
Many problems in machine learning are naturally expressed in the language of undirected graphical models. Here, we propose black-box learning and inference algorithms for undirecte…
Hierarchical Modeling of Seed Variety Yields and Decision Making for Future Planting Plans
Huaiyang Zhong, Xiaocheng Li, David Lobell +2
Eradicating hunger and malnutrition is a key development goal of the 21st century. We address the problem of optimally identifying seed varieties to reliably increase crop yield wi…
Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning
Anthony Perez, Christopher Yeh, George Azzari +3
Obtaining detailed and reliable data about local economic livelihoods in developing countries is expensive, and data are consequently scarce. Previous work has shown that it is pos…