Statistical Learning
Evaluating model performance under worst-case subpopulations Mike Li
A particularly problematic form of distribution shift comes from embedded power structures in data collection.
FasterRisk: Fast and Accurate Interpretable Risk Scores
Recent work used mathematical programming, which is computationally slow.
Distributionally Robust Federated Averaging
In this paper, we study communication efficient distributed algorithms for distribu-tionally robust federated learning via periodic averaging with adaptive sampling.
AT Results and Proofs
The result follows. 1 B Gradient Derivations B.1 Weights gradient First, we compute the gradient with respect to weights vector w R B.2 Location gradients Here we take the gradient with respect to a single pseudopoint u Increasing this number is typically expensive to obtain in practice. Bayesian Logistic Regression experiment presented in Section 4. Posterior approximation metrics, coreset gradients and learning rates Estimation of differential privacy cost at all experiments was based on TensorFlow privacy implementation of moments accountant for the subsampled Gaussian mechanism. Experiments were performed on a CPU cluster node with a 2x Intel Xeon Gold 6142 and 12GB RAM.
Dimensionality Reduction for Wasserstein Barycenter
In order to cope with this "curse of dimensionality," we
