Random Vector

Random vectors are multi-dimensional data points whose values are governed by probability distributions, with research focusing on their application in diverse fields like machine learning and causal inference. Current research explores novel model architectures, such as circular vectors and random feature regularization, to improve efficiency and accuracy in tasks ranging from classification and regression to distributed optimization. These advancements offer significant potential for enhancing the performance of various algorithms and improving the analysis of complex datasets across numerous scientific disciplines.