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pro vyhledávání: '"Alur, Rohan"'
We introduce a novel framework for human-AI collaboration in prediction and decision tasks. Our approach leverages human judgment to distinguish inputs which are algorithmically indistinguishable, or "look the same" to any feasible predictive algorit
Externí odkaz:
http://arxiv.org/abs/2410.08783
Text-to-image diffusion models rely on massive, web-scale datasets. Training them from scratch is computationally expensive, and as a result, developers often prefer to make incremental updates to existing models. These updates often compose fine-tun
Externí odkaz:
http://arxiv.org/abs/2410.08074
Autor:
Cen, Sarah H., Alur, Rohan
Artificial intelligence (AI) is increasingly intervening in our lives, raising widespread concern about its unintended and undeclared side effects. These developments have brought attention to the problem of AI auditing: the systematic evaluation and
Externí odkaz:
http://arxiv.org/abs/2410.04772
We introduce a novel framework for incorporating human expertise into algorithmic predictions. Our approach focuses on the use of human judgment to distinguish inputs which `look the same' to any feasible predictive algorithm. We argue that this fram
Externí odkaz:
http://arxiv.org/abs/2402.00793
High-stakes prediction tasks (e.g., patient diagnosis) are often handled by trained human experts. A common source of concern about automation in these settings is that experts may exercise intuition that is difficult to model and/or have access to i
Externí odkaz:
http://arxiv.org/abs/2306.01646
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