2 citations · 7 across the 6 of their papers we have counts for
6 papers
Wasserstein Distributional Learning
Chengliang Tang, Nathan Lenssen, Ying Wei +1
Learning conditional densities and identifying factors that influence the entire distribution are vital tasks in data-driven applications. Conventional approaches work mostly with…
Toward a Taxonomy of Trust for Probabilistic Machine Learning
Tamara Broderick, Andrew Gelman, Rachael Meager +2
Probabilistic machine learning increasingly informs critical decisions in medicine, economics, politics, and beyond. We need evidence to support that the resulting decisions are we…
Artificial Perceptual Learning: Image Categorization with Weak Supervision
Chengliang Tang, María Uriarte, Helen Jin +2
Machine learning has achieved much success on supervised learning tasks with large sets of well-annotated training samples. However, in many practical situations, such strong and h…
Weakly Supervised Learning Creates a Fusion of Modeling Cultures
Chengliang Tang, Gan Yuan, Tian Zheng
The past two decades have witnessed the great success of the algorithmic modeling framework advocated by Breiman et al. (2001). Nevertheless, the excellent prediction performance o…
Stabilized Sparse Online Learning for Sparse Data
Yuting Ma, Tian Zheng
Stochastic gradient descent (SGD) is commonly used for optimization in large-scale machine learning problems. Langford et al. (2009) introduce a sparse online learning method to in…
Boosted Sparse Non-linear Distance Metric Learning
Yuting Ma, Tian Zheng
This paper proposes a boosting-based solution addressing metric learning problems for high-dimensional data. Distance measures have been used as natural measures of (dis)similarity…