Gyemin Lee

Studies on esports league demand via new media platforms are limited yet. This paper is the first to identify determinants of esports highlight viewership. Using set-level highlight view count from YouTube, we analyze various determinants to explain view counts. As a result, we found that the number of kills, playoff games, age of video clip, 2nd round games, and 3rd set is positively correlated to view counts. Outcome uncertainty and upset results do not affect view counts. We interpret the results that as highlight clips are released after the game is finished, viewers can know the results when making a decision. Or, relatively short highlight videos reduce opportunity costs for fans and fans do not care about game outcomes much.

In the problem of domain generalization (DG), there are labeled training data sets from several related prediction problems, and the goal is to make accurate predictions on future unlabeled data sets that are not known to the learner. This problem arises in several applications where data distributions fluctuate because of environmental, technical, or other sources of variation. We introduce a formal framework for DG, and argue that it can be viewed as a kind of supervised learning problem by augmenting the original feature space with the marginal distribution of feature vectors. While our framework has several connections to conventional analysis of supervised learning algorithms, several unique aspects of DG require new methods of analysis. This work lays the learning theoretic foundations of domain generalization, building on our earlier conference paper where the problem of DG was introduced (Blanchard et al., 2011). We present two formal models of data generation, corresponding notions of risk, and distribution-free generalization error analysis. By focusing our attention on kernel methods, we also provide more quantitative results and a universally consistent algorithm. An efficient implementation is provided for this algorithm, which is experimentally compared to a pooling strategy on one synthetic and three real-world data sets.

This paper presents a novel hierarchical clustering method using support vector machines. A common approach for hierarchical clustering is to use distance for the task. However, different choices for computing inter-cluster distances often lead to fairly distinct clustering outcomes, causing interpretation difficulties in practice. In this paper, we propose to use a one-class support vector machine (OC-SVM) to directly find high-density regions of data. Our algorithm generates nested set estimates using the OC-SVM and exploits the hierarchical structure of the estimated sets. We demonstrate the proposed algorithm on synthetic datasets. The cluster hierarchy is visualized with dendrograms and spanning trees.

Objective To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percu