Automatic Speech Recognition (ASR) has historically been a driving force behind many
machine learning (ML) techniques, including the ubiquitously used hidden Markov model …

Modeling plays an important role in the additive manufacturing (AM) process and quality
control. In practice, however, only limited data are available for each product due to the …

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

Methods of transfer learning try to combine knowledge from several related tasks (or
domains) to improve performance on a test task. Inspired by causal methodology, we relax …

A major assumption in many machine learning and data mining algorithms is that the
training and future data must be in the same feature space and have the same distribution …

Most contemporary multi-task learning methods assume linear models. This setting is
considered shallow in the era of deep learning. In this paper, we present a new deep multi …

This paper deals with the trace regression model where n entries or linear combinations of
entries of an unknown m 1× m 2 matrix A 0 corrupted by noise are observed. We propose a …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

Analysis of Multi-stage Convex Relaxation for Sparse Regularization Page 1 Journal of
Machine Learning Research 11 (2010) 1081-1107 Submitted 5/09; Revised 1/10; …

The problem of minimizing the rank of a matrix subject to affine constraints has applications
in several areas including machine learning, and is known to be NP-hard. A tractable …