Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Aoki, Raquel"'
Large Language Models (LLMs) have shown remarkable performance in various natural language tasks, but they often struggle with planning problems that require structured reasoning. To address this limitation, the conversion of planning problems into t
Externí odkaz:
http://arxiv.org/abs/2407.12979
Machine learning (ML) methods have experienced significant growth in the past decade, yet their practical application in high-impact real-world domains has been hindered by their opacity. When ML methods are responsible for making critical decisions,
Externí odkaz:
http://arxiv.org/abs/2405.18563
Autor:
Aoki, Raquel, Ester, Martin
Many methods have been proposed to estimate treatment effects with observational data. Often, the choice of the method considers the application's characteristics, such as type of treatment and outcome, confounding effect, and the complexity of the d
Externí odkaz:
http://arxiv.org/abs/2205.09281
Publikováno v:
Pacific Symposium on Biocomputing, 2023
This work proposes the M3E2, a multi-task learning neural network model to estimate the effect of multiple treatments. In contrast to existing methods, M3E2 can handle multiple treatment effects applied simultaneously to the same unit, continuous and
Externí odkaz:
http://arxiv.org/abs/2112.07574
Publikováno v:
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2022)
Predicting multiple heterogeneous biological and medical targets is a challenge for traditional deep learning models. In contrast to single-task learning, in which a separate model is trained for each target, multi-task learning (MTL) optimizes a sin
Externí odkaz:
http://arxiv.org/abs/2106.10595
Autor:
Aoki, Raquel, Ester, Martin
Publikováno v:
Pacific Symposium on Biocomputing - 2021 World Scientific Publishing Co., Singapore, http://psb.stanford.edu/
Methods for causal inference from observational data are an alternative for scenarios where collecting counterfactual data or realizing a randomized experiment is not possible. Adopting a stacking approach, our proposed method ParKCA combines the res
Externí odkaz:
http://arxiv.org/abs/2003.07952
Publikováno v:
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017
Predicting the outcome of sports events is a hard task. We quantify this difficulty with a coefficient that measures the distance between the observed final results of sports leagues and idealized perfectly balanced competitions in terms of skill. Th
Externí odkaz:
http://arxiv.org/abs/1706.02447
Akademický článek
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Autor:
Aoki, Raquel, Ester, Martin
Publikováno v:
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. 26
Methods for causal inference from observational data are an alternative for scenarios where collecting counterfactual data or realizing a randomized experiment is not possible. Adopting a stacking approach, our proposed method ParKCA combines the res
Publikováno v:
Repositório Institucional do STJ
Superior Tribunal de Justiça (STJ)
instacron:STJ
Superior Tribunal de Justiça (STJ)
instacron:STJ
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Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3056::dc641aea426c333ffb0759a394eab60c
http://bdjur.stj.jus.br/dspace/handle/2011/50563
http://bdjur.stj.jus.br/dspace/handle/2011/50563