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Machine learning (ML) prediction APIs are increasingly widely used.
Remark on stably updating mean and standard deviation of data
Ira W. Cotton · 1975
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Improving predictive inference under covariate shift by weighting the log-likelihood function
Hidetoshi Shimodaira · 2000
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Adjusting the outputs of a classifier to new a priori probabilities: A simple procedure
Marco Saerens, Patrice Latinne, and Christine Decaestecker · 2002
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Measure Theory and Probability Theory
K.B. Athreya and S.N. Lahiri · 2006
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Optimized stratified sampling for approximate query processing
Surajit Chaudhuri, Gautam Das, and Vivek R. Narasayya · 2007
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Direct importance estimation with model selection and its application to covariate shift adaptation
Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul von Bünau, and Motoaki Kawanabe · 2007
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Covariate Shift by Kernel Mean Matching
Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence · 2009
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Finite time analysis of stratified sampling for monte carlo
Alexandra Carpentier and Rémi Munos · 2011
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
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Adaptive strategy for stratified monte carlo sampling
Alexandra Carpentier, Rémi Munos, and András Antos · 2015
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Challenges in representation learning: A report on three machine learning contests
Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron C. Courville, Mehdi Mirza, Benjamin Hamner, William Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Tudor Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Zhang Chuang, and Yoshua Bengio · 2015
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Learning social relation traits from face images
Zhanpeng Zhang, Ping Luo, Chen Change Loy, and Xiaoou Tang · 2015
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Multi-armed bandit for stratified sampling: Application to numerical integration
Florian Leprêtre, Fabien Teytaud, and Julien Dehos · 2017
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Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild
Shan Li, Weihong Deng, and JunPing Du · 2017
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Interpreting and explaining deep neural networks for classification of audio signals
Sören Becker, Marcel Ackermann, Sebastian Lapuschkin, Klaus-Robert Müller, and Wojciech Samek · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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Detecting and correcting for label shift with black box predictors
Zachary C. Lipton, Yu-Xiang Wang, and Alexander J. Smola · 2018
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Speech commands: A dataset for limited-vocabulary speech recognition
Pete Warden · 2018
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Regularized learning for domain adaptation under label shifts
Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, and Animashree Anandkumar · 2019
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Beyond accuracy: Behavioral testing of NLP models with checklist
Marco Túlio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh · 2020
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From imagenet to image classification: Contextualizing progress on benchmarks
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Andrew Ilyas, and Aleksander Madry · 2020
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Masked face recognition dataset and application
Zhongyuan Wang, Guangcheng Wang, Baojin Huang, Zhangyang Xiong, Qi Hong, Hao Wu, Peng Yi, Kui Jiang, Nanxi Wang, Yingjiao Pei, Heling Chen, Yu Miao, Zhibing Huang, and Jinbi Liang · 2020
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https://aws.amazon.com/comprehend
Amazon Comprehend API · 2021
Closest in time.
https://ai.baidu.com/
Baidu API · 2021
Closest in time.
https://www.faceplusplus.com/emotion-recognition/
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Learning a unified classifier incrementally via rebalancing
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin · 2019
Cited alongside, same era.
Speech model pre-training for end-to-end spoken language understanding
Loren Lugosch, Mirco Ravanelli, Patrick Ignoto, Vikrant Singh Tomar, and Yoshua Bengio · 2019
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The impact of class imbalance in classification performance metrics based on the binary confusion matrix
Amalia Luque, Alejandro Carrasco, Alejandro Martín, and Ana de las Heras · 2019
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Affectnet: A database for facial expression, valence, and arousal computing in the wild
Ali Mollahosseini, Behzad Hasani, and Mohammad H. Mahoor · 2019
Cited alongside, same era.
Frugalml: How to use ML prediction apis more accurately and cheaply
Lingjiao Chen, Matei Zaharia, and James Y. Zou · 2020
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Model assertions for monitoring and improving ML models
Daniel Kang, Deepti Raghavan, Peter Bailis, and Matei Zaharia · 2020
Cited alongside, same era.
WILDS: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang · 2020
Cited alongside, same era.
Face++ Emotion API · 2021
Closest in time.
https://cloud.google.com/natural-language
Google NLP API · 2021
Closest in time.
https://cloud.google.com/speech-to-text
Google Speech API · 2021
Closest in time.
https://cloud.google.com/vision
Google Vision API · 2021
Closest in time.
https://cloud.ibm.com/apidocs/speech-to-text
IBM Speech API · 2021
Closest in time.
https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision
Microsoft computer vision API · 2021
Closest in time.
https://azure.microsoft.com/en-us/services/cognitive-services/speech-to-text
Microsoft speech API · 2021
Closest in time.
Active learning under label shift
Eric Zhao, Anqi Liu, Animashree Anandkumar, and Yisong Yue · 2021
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