Zobrazeno 1 - 10 of 126 pro vyhledávání: '"Duen Horng, Chau"'
1
GAM Coach: Towards Interactive and User-centered Algorithmic Recourse
Autor: Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau
Publikováno v: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems.
Machine learning (ML) recourse techniques are increasingly used in high-stakes domains, providing end users with actions to alter ML predictions, but they assume ML developers understand what input variables can be changed. However, a recourse plan's
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e51b3d621fe861db71ec796f19684b4
https://doi.org/10.1145/3544548.3580816
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2
Lessons from the Development of an Anomaly Detection Interface on the Mars Perseverance Rover using the ISHMAP Framework
Autor: Austin P Wright, Peter Nemere, Adrian Galvin, Duen Horng Chau, Scott Davidoff
Publikováno v: Proceedings of the 28th International Conference on Intelligent User Interfaces.
While anomaly detection stands among the most important and valuable problems across many scientific domains, anomaly detection research often focuses on AI methods that can lack the nuance and interpretability so critical to conducting scientific in
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e69ebc2f63bbd6d5802df46022e140e9
https://doi.org/10.1145/3581641.3584036
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3
A Cluster-then-label Approach for Few-shot Learning with Application to Automatic Image Data Labeling
Autor: Renzhi Wu, Nilaksh Das, Sanya Chaba, Sakshi Gandhi, Duen Horng Chau, Xu Chu
Publikováno v: Journal of Data and Information Quality. 14:1-23
Few-shot learning (FSL) aims at learning to generalize from only a small number of labeled examples for a given target task. Most current state-of-the-art FSL methods typically have two limitations. First, they usually require access to a source data
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b9212a50c5417c9ec9c74c946d2a896f
https://doi.org/10.1145/3491232
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4
WebSHAP: Towards Explaining Any Machine Learning Models Anywhere
Autor: Zijie J. Wang, Duen Horng Chau
As machine learning (ML) is increasingly integrated into our everyday Web experience, there is a call for transparent and explainable web-based ML. However, existing explainability techniques often require dedicated backend servers, which limit their
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b0d1b9948f918c3c03121d88d1760c8
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6
Towards De-Anonymization of Google Play Search Rank Fraud
Autor: Bogdan Carbunar, Duen Horng Chau, Nestor Hernandez, Mizanur Rahman
Publikováno v: IEEE Transactions on Knowledge and Data Engineering. 33:3648-3661
Search rank fraud, the fraudulent promotion of products hosted on peer-review sites, is driven by expert workers recruited online, often from crowdsourcing sites. In this paper we introduce the fraud de-anonymization problem, that goes beyond fraud d
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::47ab060d0721b4561846d9a94a74715c
https://doi.org/10.1109/tkde.2020.2975170
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9
Back-calculation of soil parameters from displacement-controlled cavity expansion under geostatic stress by FEM and machine learning
Autor: Fernando Patino-Ramirez, Zijie Jay Wang, Duen Horng Chau, Chloe Arson
Estimating soil properties from the mechanical reaction to a displacement is a common strategy, used not only in in situ soil characterization (e.g., pressuremeter and dilatometer tests) but also by biological organisms (e.g., roots, earthworms, razo
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::72c9a703e8799f1e481fe51bd30d6adc
http://hdl.handle.net/10044/1/100224
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