Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i.e., generalizing). In this work, we provide a comprehensive study of generalization performance in QML ...
In statistics and machine learning, overfitting occurs when a statistical model describes random error or noise instead of the underlying relationship. “Overfitting” is when a classifier fits the training data too tightly. Such a classifier works well on the training data but not on independent ...
Generalization ability in machine learning refers to the model's capacity to perform well on new, unseen data beyond the training set, especially crucial in dynamic network environments where the model needs to adapt without extensive retraining. ...
*=Equal Contributors This paper was accepted at the workshop "Trustworthy Machine Learning for Healthcare Workshop" at the conference ICLR 2023. When analyzing robustness of predictive models under distribution shift, many works focus on tackling generalization in the presence of spurious correlations....
Active learning differs from “learning from examples” in that the learning algorithm assumes at least some control over what part of the input domain it receives information about. In some situations, active learning is provably more powerful than learning from examples alone, giving better general...
In order to compare learning algorithms, experimental results reported in the machine learning literature often use statistical tests of significance to support the claim that a new learning algorithm generalizes better. Such tests should take into account the variability due to the choice of training ...
Cobbe, Karl, et al. "Quantifying generalization in reinforcement learning."International Conference on Machine Learning. PMLR, 2019. 通常的强化学习环境实际上是在同一个环境上训练和测试,因此无法对泛化相关问题提供研究场景。这里提出 Coinrun 环境,它是逐渐生成的环境(类似超级马里奥);由于有不同的关卡,因此能...
Bengio, Emmanuel, Joelle Pineau, and Doina Precup. "Interference and generalization in temporal difference learning." International Conference on Machine Learning. PMLR, 2020. 特色 本文的一大特色就是 Bengio 大佬写的英语实在看不懂(误)。这篇文章给了很多实验和理论上的线索,来探究强化学习里面的 interfere...
Machine LearningAims and scopeSubmit manuscript David Cohn, Les Atlas& Richard Ladner 18kAccesses 854Citations 7Altmetric Explore all metrics Abstract Active learning differs from “learning from examples” in that the learning algorithm assumes at least some control over what part of the input domain...
The lack of out-of-domain generalization is a critical weakness of deep networks for semantic segmentation. Previous studies relied on the assumption of a static model, i. e., once the training process is complete, model parameters remain fixed at test time. In this work, we challenge this ...