Zobrazeno 1 - 10
of 349
pro vyhledávání: '"Candes, Emmanuel"'
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
Pretraining on large-scale, unstructured internet text enables language models to acquire a significant amount of world knowledge. However, this knowledge acquisition is data-inefficient--to learn a given fact, models must be trained on hundreds to t
Externí odkaz:
http://arxiv.org/abs/2409.07431
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
Financial firms often rely on fundamental factor models to explain correlations among asset returns and manage risk. Yet after major events, e.g., COVID-19, analysts may reassess whether existing risk models continue to fit well: specifically, after
Externí odkaz:
http://arxiv.org/abs/2404.15017
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
We prove that training neural networks on 1-D data is equivalent to solving convex Lasso problems with discrete, explicitly defined dictionary matrices. We consider neural networks with piecewise linear activations and depths ranging from 2 to an arb
Externí odkaz:
http://arxiv.org/abs/2403.01046
Autor:
Chen, Zhaomeng, He, Zihuai, Chu, Benjamin B., Gu, Jiaqi, Morrison, Tim, Sabatti, Chiara, Candès, Emmanuel
Identifying which variables do influence a response while controlling false positives pervades statistics and data science. In this paper, we consider a scenario in which we only have access to summary statistics, such as the values of marginal empir
Externí odkaz:
http://arxiv.org/abs/2402.12724