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ICLR 2024(2024) (to appear) Preview Preview abstract Predictive uncertainty-a design's self recognition relating to its accuracy on an enter-is vital for both equally building robust styles by using coaching interventions and for examination-time apps for instance report the articles by google selective classification. We suggest a novel instance-conditioned reweighting method that captures predictive uncertainty employing an auxiliary network and unifies these practice- and take a look at-time applications. The auxiliary community is trained using a meta-aim in a bilevel optimization framework. A key contribution of our proposal could be the meta-goal of minimizing the dropout variance, an approximation of Bayesian Predictive uncertainty.
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The Twelfth Worldwide Meeting on Finding out Representations(2024) Preview Preview summary A notable obstacle of offline reinforcement Understanding (RL) is The problem of hidden confounding. There, unobserved variables may well influence equally the steps taken via the agent along with the results noticed in the data. Concealed confounding can compromise the validity of any causal conclusion drawn from the information and offers a major obstacle to helpful offline RL. Within this paper, we tackle the condition of hidden confounding while in the nonidentifiable location. We suggest a definition of uncertainty because of confounding bias, termed delphic uncertainty, which takes advantage of variation above appropriate globe types, and differentiate it from the well-known epistemic and aleatoric uncertainties.