Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Feldman, Shai"'
Autor:
Feldman, Shai, Romano, Yaniv
We develop a method to generate prediction sets with a guaranteed coverage rate that is robust to corruptions in the training data, such as missing or noisy variables. Our approach builds on conformal prediction, a powerful framework to construct pre
Externí odkaz:
http://arxiv.org/abs/2406.05405
Autor:
Einbinder, Bat-Sheva, Feldman, Shai, Bates, Stephen, Angelopoulos, Anastasios N., Gendler, Asaf, Romano, Yaniv
We study the robustness of conformal prediction, a powerful tool for uncertainty quantification, to label noise. Our analysis tackles both regression and classification problems, characterizing when and how it is possible to construct uncertainty set
Externí odkaz:
http://arxiv.org/abs/2209.14295
To provide rigorous uncertainty quantification for online learning models, we develop a framework for constructing uncertainty sets that provably control risk -- such as coverage of confidence intervals, false negative rate, or F1 score -- in the onl
Externí odkaz:
http://arxiv.org/abs/2205.09095
Graph isomorphism testing is usually approached via the comparison of graph invariants. Two popular alternatives that offer a good trade-off between expressive power and computational efficiency are combinatorial (i.e., obtained via the Weisfeiler-Le
Externí odkaz:
http://arxiv.org/abs/2201.13410
We develop a method to generate predictive regions that cover a multivariate response variable with a user-specified probability. Our work is composed of two components. First, we use a deep generative model to learn a representation of the response
Externí odkaz:
http://arxiv.org/abs/2110.00816
We develop a method to generate prediction intervals that have a user-specified coverage level across all regions of feature-space, a property called conditional coverage. A typical approach to this task is to estimate the conditional quantiles with
Externí odkaz:
http://arxiv.org/abs/2106.00394
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