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Found 7 papers, 5 papers with code
Proceedings of AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges
These applications have implications in areas ranging as widely as energy, agriculture, and finance.
Adversarially Robust Learning for Security-Constrained Optimal Power Flow
In recent years, the ML community has seen surges of interest in both adversarially robust learning and implicit layers, but connections between these two areas have seldom been explored.
DC3: A learning method for optimization with hard constraints
Large optimization problems with hard constraints arise in many settings, yet classical solvers are often prohibitively slow, motivating the use of deep networks as cheap "approximate solvers."
Enforcing robust control guarantees within neural network policies
When designing controllers for safety-critical systems, practitioners often face a challenging tradeoff between robustness and performance.
Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help.
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
We demonstrate that by integrating this solver into end-to-end learning systems, we can learn the logical structure of challenging problems in a minimally supervised fashion.
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Task-based End-to-end Model Learning in Stochastic Optimization
With the increasing popularity of machine learning techniques, it has become common to see prediction algorithms operating within some larger process.
