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of 75
pro vyhledávání: '"Bacallado, Sergio A"'
Autor:
Zhu, Max, Yao, Jian, Mynatt, Marcus, Pugzlys, Hubert, Li, Shuyi, Bacallado, Sergio, Zhao, Qingyuan, Jia, Chunjing
We introduce a Bayesian active learning algorithm that efficiently elucidates phase diagrams. Using a novel acquisition function that assesses both the impact and likelihood of the next observation, the algorithm iteratively determines the most infor
Externí odkaz:
http://arxiv.org/abs/2409.07042
Recent developments in regularized Canonical Correlation Analysis (CCA) promise powerful methods for high-dimensional, multiview data analysis. However, justifying the structural assumptions behind many popular approaches remains a challenge, and fea
Externí odkaz:
http://arxiv.org/abs/2403.02979
The Tanimoto coefficient is commonly used to measure the similarity between molecules represented as discrete fingerprints, either as a distance metric or a positive definite kernel. While many kernel methods can be accelerated using random feature a
Externí odkaz:
http://arxiv.org/abs/2306.14809
Neural processes (NPs) are models for transfer learning with properties reminiscent of Gaussian Processes (GPs). They are adept at modelling data consisting of few observations of many related functions on the same input space and are trained by mini
Externí odkaz:
http://arxiv.org/abs/2210.09211
Autor:
García-Ortegón, Miguel, Simm, Gregor N. C., Tripp, Austin J., Hernández-Lobato, José Miguel, Bender, Andreas, Bacallado, Sergio
The field of machine learning for drug discovery is witnessing an explosion of novel methods. These methods are often benchmarked on simple physicochemical properties such as solubility or general druglikeness, which can be readily computed. However,
Externí odkaz:
http://arxiv.org/abs/2110.15486
We define a generalisation of the Edge-Reinforced Random Walk (ERRW) introduced by Coppersmith and Diaconis in 1986, called *-Edge-Reinforced Random Walk (*-ERRW), which can be seen as an extension of the r-dependent ERRW introduced by Bacallado (201
Externí odkaz:
http://arxiv.org/abs/2102.08984
BETS: The dangers of selection bias in early analyses of the coronavirus disease (COVID-19) pandemic
The coronavirus disease 2019 (COVID-19) has quickly grown from a regional outbreak in Wuhan, China to a global pandemic. Early estimates of the epidemic growth and incubation period of COVID-19 may have been biased due to sample selection. Using deta
Externí odkaz:
http://arxiv.org/abs/2004.07743
Akademický článek
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Most Bayesian response-adaptive designs unbalance randomization rates towards the most promising arms with the goal of increasing the number of positive treatment outcomes during the study, even though the primary aim of the trial is different. We di
Externí odkaz:
http://arxiv.org/abs/1806.11370
Autor:
Ren, Boyu, Bacallado, Sergio, Favaro, Stefano, Vatanen, Tommi, Huttenhower, Curtis, Trippa, Lorenzo
Detecting associations between microbial compositions and sample characteristics is one of the most important tasks in microbiome studies. Most of the existing methods apply univariate models to single microbial species separately, with adjustments f
Externí odkaz:
http://arxiv.org/abs/1711.01241