activity
20162022
most citedRethinking Explainability as a Dialogue: A Practitioner's Perspective

28 citations · 72 across the 11 of their papers we have counts for

collaborators

22 papers

cs.LG2022

Learning Representation for Bayesian Optimization with Collision-free Regularization

Fengxue Zhang, Brian Nord, Yuxin Chen

Bayesian optimization has been challenged by datasets with large-scale, high-dimensional, and non-stationary characteristics, which are common in real-world scenarios. Recent works…

cs.LG202228 cited

Rethinking Explainability as a Dialogue: A Practitioner's Perspective

Himabindu Lakkaraju, Dylan Slack, Yuxin Chen +2

As practitioners increasingly deploy machine learning models in critical domains such as health care, finance, and policy, it becomes vital to ensure that domain experts function e…

cs.LG20216 cited

Understanding the Effect of Bias in Deep Anomaly Detection

Ziyu Ye, Yuxin Chen, Haitao Zheng

Anomaly detection presents a unique challenge in machine learning, due to the scarcity of labeled anomaly data. Recent work attempts to mitigate such problems by augmenting trainin…

cs.AI2021

Towards an Interpretable Data-driven Trigger System for High-throughput Physics Facilities

Chinmaya Mahesh, Kristin Dona, David W. Miller +1

Data-intensive science is increasingly reliant on real-time processing capabilities and machine learning workflows, in order to filter and analyze the extreme volumes of data being…

cs.LG2020

Preference-Based Batch and Sequential Teaching

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…

cs.LG2020

The Teaching Dimension of Kernel Perceptron

Akash Kumar, Hanqi Zhang, Adish Singla +1

Algorithmic machine teaching has been studied under the linear setting where exact teaching is possible. However, little is known for teaching nonlinear learners. Here, we establis…