Despite the mounting anticipation for the quantum revolution, the success of quantum machine learning (QML) in the noisy intermediate-scale quantum (NISQ) era hinges on a largely unexplored factor: the generali
Despite the mounting anticipation for the quantum revolution, the success of quantum machine learning (QML) in the noisy intermediate-scale quantum (NISQ)
for some Hermitian observableOxi,yiloss. As is common in classical learning theory, the prediction error bounds will depend on the largest (absolute) value that the loss function can attain. In our case, we therefore assumeCloss:=supx,y||Ox,yloss||<∞, i.e., the spectral norm can ...
In machine learning, the model is not as complicated as possible. Goodgeneralization abilitymeans that the model not only performs well on the training data set, but also can make good prediction on new data.Regularizationimposes a penalty on model’s complexity or smoothness, allowing for good ...
Moreover, we present the generalization bounds based on the Rademacher complexity. Finally, we analyze the asymptotic convergence and the rate of convergence of the learning process for representative domain adaptation. We discuss the factors that affect the asymptotic behavior of the learning process ...
(9), and that asymptotic sample bounds, Eq. (21), also hold across correlated cases. Finally, this illustrates how square enumeration allows correlated data to be ‘put into play’ for contemporary predictive solutions. While the concepts above can give researchers larger control over the ...
We provide upper bounds on the regret of both algorithms and show that the bounds are (worst-case) optimal. As a consequence of our development, we show that our private versions of AdaGrad outperform adaptive…Read more Discover opportunities in Machine Learning. Our research in machine learning...
(MLPNN) are local learning machines for solving problems andtreat unseen samples near the training samples to be more important.In this paper, we propose a localized generalization errormodel which bounds from above the generalization error within aneighborhood of the training samples using stochastic...
3. As for the research method, the author employs the method combining concreteness and generalization, history and logic, by breaking the bounds between traditional modern and contemporary subjects. 在研究方法上,文章打破传统的近代、现代和当代的学科畛域,运用具体与宏观、历史的与逻辑相结合的方法。
Estimating individual treatment effect: generalization bounds and algorithms ICML 2017 · Uri Shalit, Fredrik D. Johansson, David Sontag · Edit social preview There is intense interest in applying machine learning to problems of causal inference in fields such as healthcare, economics and education....