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Found 6 papers, 3 papers with code
Distributionally Robust Optimization and Generalization in Kernel Methods
We show that MMD DRO is roughly equivalent to regularization by the Hilbert norm and, as a byproduct, reveal deep connections to classic results in statistical learning.
Escaping Saddle Points with Adaptive Gradient Methods
Adaptive methods such as Adam and RMSProp are widely used in deep learning but are not well understood.
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
Leveraging new data sources is a key step in accelerating the pace of materials design and discovery.
Distributionally Robust Submodular Maximization
We also show compelling empirical evidence that DRO improves generalization to the unknown stochastic submodular function.
Parallel Streaming Wasserstein Barycenters
Our method is even robust to nonstationary input distributions and produces a barycenter estimate that tracks the input measures over time.
Robust Budget Allocation via Continuous Submodular Functions
The optimal allocation of resources for maximizing influence, spread of information or coverage, has gained attention in the past years, in particular in machine learning and data mining.
