Random forests have been one of the successful ensemble algorithms in machine learning,
and the basic idea is to construct a large number of random trees individually and make …

Adaptivity to Noise Parameters in Nonparametric Active Learning Page 1 Proceedings of
Machine Learning Research vol 65:1–34, 2017 Adaptivity to Noise Parameters in …

Learning non-linear systems from noisy, limited, and/or dependent data is an important task
across various scientific fields including statistics, engineering, computer science …

We study fast learning rates when the losses are not necessarily bounded and may have a
distribution with heavy tails. To enable such analyses, we introduce two new conditions:(i) …

Existing generalization bounds for deep neural networks require data to be independent
and identically distributed (iid). This assumption may not hold in real-life applications such …

Multi-classifiers are widely applied in many practical problems. But the features that can
significantly discriminate a certain class from others are often deleted in the feature selection …

Existing generalization bounds for deep neural networks require data to be independent
and identically distributed (iid). This assumption may not hold in real-life applications such …

We consider a problem of multiclass classification, where the training sample S_n={(X i, Y i)}
ni= 1 is generated from the model ℙ (Y= m| X= x)= η m (x), 1≤ m≤ M, and η 1 (x),…, η M (x) …

The intrinsic nature of non-independent and identically distributed datasets on
heterogeneous devices slows down the distributed model training process and reduces the …

Learning nonlinear functions from time-varying measurements is always difficult due to the
high correlation among observations. This task is more challenging when the target function …