Yuan Shi

IJCAI Conference 2017 Conference Paper

Learning with Previously Unseen Features

• Yuan Shi
• Craig A. Knoblock

We study the problem of improving a machine learning model by identifying and using features that are not in the training set. This is applicable to machine learning systems deployed in an open environment. For example, a prediction model built on a set of sensors may be improved when it has access to new and relevant sensors at test time. To effectively use new features, we propose a novel approach that learns a model over both the original and new features, with the goal of making the joint distribution of features and predicted labels similar to that in the training set. Our approach can naturally leverage labels associated with these new features when they are accessible. We present an efficient optimization algorithm for learning the model parameters and empirically evaluate the approach on several regression and classification tasks. Experimental results show that our approach can achieve on average 11. 2% improvement over baselines.

AAAI Conference 2016 Conference Paper

Metric Learning for Ordinal Data

• Yuan Shi
• Wenzhe Li
• Fei Sha

A large amount of ordinal-valued data exist in many domains, including medical and health science, social science, economics, political science, etc. Unlike image and speech datasets of real-valued data, learning with ordinal variables (i. e. , features) presents unique challenges. In particular, the nominal differences between those feature values, which are just ranks, do not necessarily correspond to the real distances between the corresponding categories. Given their wide existence, it is imperative to develop machine learning algorithms that specifically address the need to model and infer with such data. In this paper, we present a novel metric learning algorithm that takes into consideration the nature of ordinal data. Our approach treats ordinal values as latent variables in intervals. Our algorithm then learns what those intervals are as well as distance metrics to measure distances between latent variables in those intervals. We derive the corresponding optimization algorithm and demonstrate how that can be solved effectively. Experimental results show that the proposed approach significantly improves baselines that do not explicitly model ordinal features.

AAAI Conference 2014 Conference Paper

Sparse Compositional Metric Learning

• Yuan Shi
• Aurélien Bellet
• Fei Sha

We propose a new approach for metric learning by framing it as learning a sparse combination of locally discriminative metrics that are inexpensive to generate from the training data. This flexible framework allows us to naturally derive formulations for global, multi-task and local metric learning. The resulting algorithms have several advantages over existing methods in the literature: a much smaller number of parameters to be estimated and a principled way to generalize learned metrics to new testing data points. To analyze the approach theoretically, we derive a generalization bound that justifies the sparse combination. Empirically, we evaluate our algorithms on several datasets against state-of-theart metric learning methods. The results are consistent with our theoretical findings and demonstrate the superiority of our approach in terms of classification performance and scalability.

ICML Conference 2012 Conference Paper

Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation

• Yuan Shi
• Fei Sha