no code implementations • 3 Mar 2022 • Chun-Hao Chang, Jinsung Yoon, Sercan Arik, Madeleine Udell, Tomas Pfister
In addition, the proposed framework, DIAD, can incorporate a small amount of labeled data to further boost anomaly detection performances in semi-supervised settings.
no code implementations • 28 Oct 2021 • Chun-Hao Chang, George Alexandru Adam, Rich Caruana, Anna Goldenberg
Although reinforcement learning (RL) has tremendous success in many fields, applying RL to real-world settings such as healthcare is challenging when the reward is hard to specify and no exploration is allowed.
2 code implementations • ICLR 2022 • Chun-Hao Chang, Rich Caruana, Anna Goldenberg
Deployment of machine learning models in real high-risk settings (e. g. healthcare) often depends not only on the model's accuracy but also on its fairness, robustness, and interpretability.
1 code implementation • CVPR 2021 • Chun-Hao Chang, George Alexandru Adam, Anna Goldenberg
Despite the success of machine learning applications in science, industry, and society in general, many approaches are known to be non-robust, often relying on spurious correlations to make predictions.
Ranked #1 on Out-of-Distribution Generalization on UrbanCars
2 code implementations • 11 Jun 2020 • Chun-Hao Chang, Sarah Tan, Ben Lengerich, Anna Goldenberg, Rich Caruana
Generalized additive models (GAMs) have become a leading modelclass for interpretable machine learning.
1 code implementation • 12 Nov 2019 • Benjamin Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana
Models which estimate main effects of individual variables alongside interaction effects have an identifiability challenge: effects can be freely moved between main effects and interaction effects without changing the model prediction.
1 code implementation • 24 Jan 2019 • Chun-Hao Chang, Mingjie Mai, Anna Goldenberg
We answer this question by deep reinforcement learning (RL) that jointly minimizes the measurement cost and maximizes predictive gain, by scheduling strategically-timed measurements.
no code implementations • 1 Dec 2018 • Chun-Hao Chang, Mingjie Mai, Anna Goldenberg
We address the scheduling problem using deep reinforcement learning (RL) to achieve high predictive gain and low measurement cost, by scheduling fewer, but strategically timed tests.
1 code implementation • ICLR 2019 • Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud
We can rephrase this question to ask: which parts of the image, if they were not seen by the classifier, would most change its decision?
1 code implementation • 22 Dec 2017 • Chun-Hao Chang, Ladislav Rampasek, Anna Goldenberg
Deep neural networks (DNNs) achieve state-of-the-art results in a variety of domains.