Statistical Learning
Optimizing Data Collection for Machine Learning
Optimizing Data Collection for Machine Learning
Optimizing Data Collection for Machine Learning
Optimizing Data Collection for Machine Learning
An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers (Appendix)
See table 1 for the results. We next perform regression in the Joint setting (Sec.5.3, main paper) where we fit a regression model across all environments, with 5 features instead of 2 reported in the main We find that it is possible to get an Spearman's We considered a set of 40 metrics overall and report only a small subset of them in the main paper. In table 2 we provide detailed results of all the measures we study. Figure 1 provides details of the canonicalization performed on each of the measures as explained in the main paper. In particular, (Ben-David et al., 2007) prove We also develop measures based on follow-up theoretical work in (Ben-David et al., 2010) on divergence measures using the symmetric difference hypothesis space. Here we summarize a result from (Ben-David et al., 2010), This canonicalization is used to report the results in Sec. 5 H: Z P (Y), we follow the steps in algorithm 1. Algorithm 1 Computing H -divergence measure As explained in the main paper, this divergence measure was proposed in (Ben-David et al., 2010).
An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers
Minimization (ERM) can generalize under distribution shift, outperforming specialized training algorithms for domain generalization. The goal of this paper is to further understand this phenomenon.
Global Convergence of Federated Learning for Mixed Regression
Then each client alternately estimates its cluster labels and refines the model estimates based on FedAvg or FedProx.
Unsupervised Part Discovery from Contrastive Reconstruction
On the other hand, representation learning at part level has received significantly less attention.
A First Approach to Noise-Adaptive Accelerated Second-Order Methods
Over the last few decades, first-order (convex) minimization methods have gained popularity for modern machine learning and optimization problems due to their efficient per-iteration cost and global convergence properties.