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Found 3 papers, 1 papers with code
Hidden yet quantifiable: A lower bound for confounding strength using randomized trials
Further, we show how our lower bound can correctly identify the absence and presence of unobserved confounding in a real-world setting.
How robust accuracy suffers from certified training with convex relaxations
In this paper, we systematically compare the standard and robust error of these two robust training paradigms across multiple computer vision tasks.
Challenging Common Assumptions in Convex Reinforcement Learning
In particular, we show that erroneously optimizing the infinite trials objective in place of the actual finite trials one, as it is usually done, can lead to a significant approximation error.
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