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pro vyhledávání: '"Loog, Marco"'
Conformal prediction, which makes no distributional assumptions about the data, has emerged as a powerful and reliable approach to uncertainty quantification in practical applications. The nonconformity measure used in conformal prediction quantifies
Externí odkaz:
http://arxiv.org/abs/2410.09894
Autor:
Loog, Marco, Viering, Tom
Plotting a learner's generalization performance against the training set size results in a so-called learning curve. This tool, providing insight in the behavior of the learner, is also practically valuable for model selection, predicting the effect
Externí odkaz:
http://arxiv.org/abs/2211.14061
Estimating uncertainty of machine learning models is essential to assess the quality of the predictions that these models provide. However, there are several factors that influence the quality of uncertainty estimates, one of which is the amount of m
Externí odkaz:
http://arxiv.org/abs/2210.16938
Many methods for Model-based Reinforcement learning (MBRL) in Markov decision processes (MDPs) provide guarantees for both the accuracy of the model they can deliver and the learning efficiency. At the same time, state abstraction techniques allow fo
Externí odkaz:
http://arxiv.org/abs/2208.14407
Autor:
van Tulder, Gijs, Loog, Marco
An interesting but not extensively studied question in active learning is that of sample reusability: to what extent can samples selected for one learner be reused by another? This paper explains why sample reusability is of practical interest, why r
Externí odkaz:
http://arxiv.org/abs/2206.06276
Forecasting tasks surrounding the dynamics of low-level human behavior are of significance to multiple research domains. In such settings, methods for explaining specific forecasts can enable domain experts to gain insights into the predictive relati
Externí odkaz:
http://arxiv.org/abs/2206.00679
Autor:
Wang, Ziqi, Loog, Marco
We illustrate the detrimental effect, such as overconfident decisions, that exponential behavior can have in methods like classical LDA and logistic regression. We then show how polynomiality can remedy the situation. This, among others, leads purpos
Externí odkaz:
http://arxiv.org/abs/2203.12693
Free-standing social conversations constitute a yet underexplored setting for human behavior forecasting. While the task of predicting pedestrian trajectories has received much recent attention, an intrinsic difference between these settings is how g
Externí odkaz:
http://arxiv.org/abs/2107.13576
Publikováno v:
IEEE Transactions on Image Processing, vol. 30, pp. 8342-8353, 2021
Resolution in deep convolutional neural networks (CNNs) is typically bounded by the receptive field size through filter sizes, and subsampling layers or strided convolutions on feature maps. The optimal resolution may vary significantly depending on
Externí odkaz:
http://arxiv.org/abs/2106.03412
Autor:
Viering, Tom, Loog, Marco
Learning curves provide insight into the dependence of a learner's generalization performance on the training set size. This important tool can be used for model selection, to predict the effect of more training data, and to reduce the computational
Externí odkaz:
http://arxiv.org/abs/2103.10948