Zobrazeno 1 - 10
of 335
pro vyhledávání: '"Candès, Emmanuel J"'
Autor:
Zhang, Yao, Candès, Emmanuel J.
Conformal prediction is a popular technique for constructing prediction intervals with distribution-free coverage guarantees. The coverage is marginal, meaning it only holds on average over the entire population but not necessarily for any specific s
Externí odkaz:
http://arxiv.org/abs/2409.19712
Estimating out-of-sample risk for models trained on large high-dimensional datasets is an expensive but essential part of the machine learning process, enabling practitioners to optimally tune hyperparameters. Cross-validation (CV) serves as the de f
Externí odkaz:
http://arxiv.org/abs/2409.09781
Large language models (LLMs) have shown high agreement with human raters across a variety of tasks, demonstrating potential to ease the challenges of human data collection. In computational social science (CSS), researchers are increasingly leveragin
Externí odkaz:
http://arxiv.org/abs/2408.15204
We develop new conformal inference methods for obtaining validity guarantees on the output of large language models (LLMs). Prior work in conformal language modeling identifies a subset of the text that satisfies a high-probability guarantee of corre
Externí odkaz:
http://arxiv.org/abs/2406.09714
This paper introduces a boosted conformal procedure designed to tailor conformalized prediction intervals toward specific desired properties, such as enhanced conditional coverage or reduced interval length. We employ machine learning techniques, not
Externí odkaz:
http://arxiv.org/abs/2406.07449
Autor:
Zrnic, Tijana, Candès, Emmanuel J.
Inspired by the concept of active learning, we propose active inference$\unicode{x2013}$a methodology for statistical inference with machine-learning-assisted data collection. Assuming a budget on the number of labels that can be collected, the metho
Externí odkaz:
http://arxiv.org/abs/2403.03208
Autor:
Zrnic, Tijana, Candès, Emmanuel J.
While reliable data-driven decision-making hinges on high-quality labeled data, the acquisition of quality labels often involves laborious human annotations or slow and expensive scientific measurements. Machine learning is becoming an appealing alte
Externí odkaz:
http://arxiv.org/abs/2309.16598
We study the problem of uncertainty quantification for time series prediction, with the goal of providing easy-to-use algorithms with formal guarantees. The algorithms we present build upon ideas from conformal prediction and control theory, are able
Externí odkaz:
http://arxiv.org/abs/2307.16895
Autor:
Jin, Ying, Candès, Emmanuel J.
This paper introduces novel weighted conformal p-values and methods for model-free selective inference. The problem is as follows: given test units with covariates $X$ and missing responses $Y$, how do we select units for which the responses $Y$ are
Externí odkaz:
http://arxiv.org/abs/2307.09291
We consider the problem of constructing distribution-free prediction sets with finite-sample conditional guarantees. Prior work has shown that it is impossible to provide exact conditional coverage universally in finite samples. Thus, most popular me
Externí odkaz:
http://arxiv.org/abs/2305.12616