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pro vyhledávání: '"Cruz, André F."'
Current question-answering benchmarks predominantly focus on accuracy in realizable prediction tasks. Conditioned on a question and answer-key, does the most likely token match the ground truth? Such benchmarks necessarily fail to evaluate LLMs' abil
Externí odkaz:
http://arxiv.org/abs/2407.14614
Autor:
Cruz, André F., Hardt, Moritz
Publikováno v:
ICLR 2024
Seven years ago, researchers proposed a postprocessing method to equalize the error rates of a model across different demographic groups. The work launched hundreds of papers purporting to improve over the postprocessing baseline. We empirically eval
Externí odkaz:
http://arxiv.org/abs/2306.07261
Tabular data is prevalent in many high-stakes domains, such as financial services or public policy. Gradient Boosted Decision Trees (GBDT) are popular in these settings due to their scalability, performance, and low training cost. While fairness in t
Externí odkaz:
http://arxiv.org/abs/2209.07850
Autor:
Pombal, José, Cruz, André F., Bravo, João, Saleiro, Pedro, Figueiredo, Mário A. T., Bizarro, Pedro
In recent years, machine learning algorithms have become ubiquitous in a multitude of high-stakes decision-making applications. The unparalleled ability of machine learning algorithms to learn patterns from data also enables them to incorporate biase
Externí odkaz:
http://arxiv.org/abs/2207.06273
Publikováno v:
2021 IEEE International Conference on Data Mining (ICDM)
Considerable research effort has been guided towards algorithmic fairness but real-world adoption of bias reduction techniques is still scarce. Existing methods are either metric- or model-specific, require access to sensitive attributes at inference
Externí odkaz:
http://arxiv.org/abs/2103.12715
Although recurrent neural networks (RNNs) are state-of-the-art in numerous sequential decision-making tasks, there has been little research on explaining their predictions. In this work, we present TimeSHAP, a model-agnostic recurrent explainer that
Externí odkaz:
http://arxiv.org/abs/2012.00073
Considerable research effort has been guided towards algorithmic fairness but there is still no major breakthrough. In practice, an exhaustive search over all possible techniques and hyperparameters is needed to find optimal fairness-accuracy trade-o
Externí odkaz:
http://arxiv.org/abs/2010.03665
Akademický článek
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Publikováno v:
Phytopathologia Mediterranea, 2016 Aug 01. 55(2), 276-284.
Externí odkaz:
https://www.jstor.org/stable/44809333
Autor:
Cruz, Andre F., Hamel, Chantal, Yang, Chao, Matsubara, Tomoko, Gan, Yantai, Singh, Asheesh K., Kuwada, Kousaku, Ishii, Takaaki
Publikováno v:
In Phytochemistry June 2012 78:72-80