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Machine learning from explanations (MLX) is an approach to learning that useshuman-provided explanations of relevant or irrelevant features for each inputto ensure that model predictions are right for the right reasons. Existing MLXapproaches rely on local model interpretation methods and require strong modelsmoothing to align model and human explanations, leading to sub-optimalperformance. We recast MLX as a robustness problem, where human explanationsspecify a lower dimensional manifold from which perturbations can be drawn, andshow both theoretically and empirically how this approach alleviates the needfor strong model smoothing. We consider various approaches to achievingrobustness, leading to improved performance over prior MLX methods. Finally, weshow how to combine robustness with an earlier MLX method, yieldingstate-of-the-art results on both synthetic and real-world benchmarks.

Use Perturbations when Learning from Explanations