Zobrazeno 1 - 10
of 194
pro vyhledávání: '"Johansson, P. D."'
Bandit algorithms hold great promise for improving personalized decision-making but are notoriously sample-hungry. In most health applications, it is infeasible to fit a new bandit for each patient, and observable variables are often insufficient to
Externí odkaz:
http://arxiv.org/abs/2407.16239
Autor:
Johansson, Fredrik D.
Evaluating observational estimators of causal effects demands information that is rarely available: unconfounded interventions and outcomes from the population of interest, created either by randomization or adjustment. As a result, it is customary t
Externí odkaz:
http://arxiv.org/abs/2405.16069
Autor:
Wang, Quan, Pan, Mingliang, Kreiss, Lucas, Samaei, Saeed, Carp, Stefan A., Johansson, Johannes D., Zhang, Yuanzhe, Wu, Melissa, Horstmeyer, Roarke, Diop, Mamadou, Li, David Day-Uei
Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However, newcomers might
Externí odkaz:
http://arxiv.org/abs/2406.15420
Learning an ordering of items based on noisy pairwise comparisons is useful when item-specific labels are difficult to assign, for example, when annotators have to make subjective assessments. Algorithms have been proposed for actively sampling compa
Externí odkaz:
http://arxiv.org/abs/2405.03059
Autor:
Stempfle, Lena, Johansson, Fredrik D.
Rule models are often preferred in prediction tasks with tabular inputs as they can be easily interpreted using natural language and provide predictive performance on par with more complex models. However, most rule models' predictions are undefined
Externí odkaz:
http://arxiv.org/abs/2311.14108
We address the problem of identifying the optimal policy with a fixed confidence level in a multi-armed bandit setup, when \emph{the arms are subject to linear constraints}. Unlike the standard best-arm identification problem which is well studied, t
Externí odkaz:
http://arxiv.org/abs/2306.12774
Successful unsupervised domain adaptation is guaranteed only under strong assumptions such as covariate shift and overlap between input domains. The latter is often violated in high-dimensional applications like image classification which, despite th
Externí odkaz:
http://arxiv.org/abs/2303.09350
Understanding generalization is crucial to confidently engineer and deploy machine learning models, especially when deployment implies a shift in the data domain. For such domain adaptation problems, we seek generalization bounds which are tractably
Externí odkaz:
http://arxiv.org/abs/2303.08720
We consider the problem of evaluating the performance of a decision policy using past observational data. The outcome of a policy is measured in terms of a loss (aka. disutility or negative reward) and the main problem is making valid inferences abou
Externí odkaz:
http://arxiv.org/abs/2301.08649
Autor:
Jung, Bastian, Johansson, Fredrik D
In domains where sample sizes are limited, efficient learning algorithms are critical. Learning using privileged information (LuPI) offers increased sample efficiency by allowing prediction models access to auxiliary information at training time whic
Externí odkaz:
http://arxiv.org/abs/2209.07067