Semi-supervised learning with adversarially missing label information

We address the problem of semi-supervised learning in an adversarial setting. Instead of assuming that labels are missing at random, we analyze a less favorable scenario where the label information can be missing partially and arbitrarily,
which is motivated by several practical examples. We present nearly matching
upper and lower generalization bounds for learning in this setting under reasonable assumptions about available label information. Motivated by the analysis, we
formulate a convex optimization problem for parameter estimation, derive an efficient algorithm, and analyze its convergence. We provide experimental results on
several standard data sets showing the robustness of our algorithm to the pattern
of missing label information, outperforming several strong baselines.

• Machine intelligence