64 citations · 253 across the 34 of their papers we have counts for
6 papers · 1 filter
Landmark Ordinal Embedding
Nikhil Ghosh, Yuxin Chen, Yisong Yue
In this paper, we aim to learn a low-dimensional Euclidean representation from a set of constraints of the form "item j is closer to item i than item k". Existing approaches for th…
Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models
Farnam Mansouri, Yuxin Chen, Ara Vartanian +2
Algorithmic machine teaching studies the interaction between a teacher and a learner where the teacher selects labeled examples aiming at teaching a target hypothesis. In a quest t…
Augmenting Monte Carlo Dropout Classification Models with Unsupervised Learning Tasks for Detecting and Diagnosing Out-of-Distribution Faults
Baihong Jin, Yingshui Tan, Yuxin Chen +1
The Monte Carlo dropout method has proved to be a scalable and easy-to-use approach for estimating the uncertainty of deep neural network predictions. This approach was recently ap…
An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing
Baihong Jin, Yingshui Tan, Alexander Nettekoven +4
We present a novel unsupervised deep learning approach that utilizes the encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial…
Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design
Kevin K. Yang, Yuxin Chen, Alycia Lee +1
In many high-throughput experimental design settings, such as those common in biochemical engineering, batched queries are more cost effective than one-by-one sequential queries. F…
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection
Baihong Jin, Yuxin Chen, Dan Li +2
It is important to identify the change point of a system's health status, which usually signifies an incipient fault under development. The One-Class Support Vector Machine (OC-SVM…