Conventional machine learning studies generally assume close-environment scenarios
where important factors of the learning process hold invariant. With the great success of …

Objective. Most current electroencephalography (EEG)-based brain–computer interfaces
(BCIs) are based on machine learning algorithms. There is a large diversity of classifier …

The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …

Distributional shift is one of the major obstacles when transferring machine learning
prediction systems from the lab to the real world. To tackle this problem, we assume that …

Despite their renowned in-domain predictive power, convolutional neural networks are
known to rely more on high-frequency patterns that humans deem superficial than on low …

We present a new domain adaptive self-training pipeline, named ST3D, for unsupervised
domain adaptation on 3D object detection from point clouds. First, we pre-train the 3D …

Abstract Domain adaptation enables the learner to safely generalize into novel
environments by mitigating domain shifts across distributions. Previous works may not …

Intelligent agents should be able to learn useful representations by observing changes in
their environment. We model such observations as pairs of non-iid images sharing at least …

A concise and self-contained introduction to causal inference, increasingly important in data
science and machine learning. The mathematization of causality is a relatively recent …

Recent advances in adaptive object detection have achieved compelling results in virtue of
adversarial feature adaptation to mitigate the distributional shifts along the detection …