Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Zrnic, Tijana"'
Autor:
Zrnic, Tijana, Fithian, William
Across science and policy, decision-makers often need to draw conclusions about the best candidate among competing alternatives. For instance, researchers may seek to infer the effectiveness of the most successful treatment or determine which demogra
Externí odkaz:
http://arxiv.org/abs/2411.18569
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
Autor:
Zrnic, Tijana
We introduce PPBoot: a bootstrap-based method for prediction-powered inference. PPBoot is applicable to arbitrary estimation problems and is very simple to implement, essentially only requiring one application of the bootstrap. Through a series of ex
Externí odkaz:
http://arxiv.org/abs/2405.18379
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 present PPI++: a computationally lightweight methodology for estimation and inference based on a small labeled dataset and a typically much larger dataset of machine-learning predictions. The methods automatically adapt to the quality of available
Externí odkaz:
http://arxiv.org/abs/2311.01453
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
Autor:
Lin, Licong, Zrnic, Tijana
When predictions are performative, the choice of which predictor to deploy influences the distribution of future observations. The overarching goal in learning under performativity is to find a predictor that has low \emph{performative risk}, that is
Externí odkaz:
http://arxiv.org/abs/2305.18728
We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms. We propose a simple theoretical model of a collective interacting with a firm's learning algorithm. The collective pools the
Externí odkaz:
http://arxiv.org/abs/2302.04262
Autor:
Angelopoulos, Anastasios N., Bates, Stephen, Fannjiang, Clara, Jordan, Michael I., Zrnic, Tijana
Prediction-powered inference is a framework for performing valid statistical inference when an experimental dataset is supplemented with predictions from a machine-learning system. The framework yields simple algorithms for computing provably valid c
Externí odkaz:
http://arxiv.org/abs/2301.09633
Autor:
Zrnic, Tijana, Fithian, William
Selective inference is the problem of giving valid answers to statistical questions chosen in a data-driven manner. A standard solution to selective inference is simultaneous inference, which delivers valid answers to the set of all questions that co
Externí odkaz:
http://arxiv.org/abs/2212.09009