Yuxin Chen

TL;DR: This paper looks at the problem from the perspective of discrete optimization and introduces a novel algorithmic framework for teaching multiple concepts with strong performance guarantees, both generic and interactive, and allows the teacher to adapt the schedule to the underlying forgetting mechanisms of the learner.

TL;DR: It is shown that the encoder-decoder model is able to identify the injected anomalies in a modern AM manufacturing process in an unsupervised fashion and gives hints about the temperature non-uniformity of the testbed during manufacturing, which was not previously known prior to the experiment.

TL;DR: A novel framework which captures the teaching process via preference functions $\Sigma$ and identifies preference functions inducing a novel family of sequential models with teaching complexity linear in the VC dimension of the hypothesis class: this is in contrast to the best known complexity result for the batch models which is quadratic in theVC dimension.

TL;DR: The results on the C-MAPSS dataset demonstrate that OC-SVM can achieve satisfactory accuracy in detecting change point in time series with fewer training data, compared to state-of-the-art deep learning approaches.

TL;DR: In this paper, a heuristic search method was proposed to find a good set of input data and hyperparameters that yield a well-performing model for detecting change points in time series with fewer training data.