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Developing machine learning models can be seen as a process similar to the one established for traditional software development.
Nearest neighbor pattern classification
T. Cover and P. Hart · 1967
Earlier work this paper cites.
Bayes error estimation using Parzen and k-NN procedures
K. Fukunaga and D. M. Hummels · 1987
Earlier work this paper cites.
Foundations of databases
Abiteboul, Serge · 1995
Earlier work this paper cites.
Learning in the presence of concept drift and hidden contexts
G. Widmer and M. Kubat · 1996
Earlier work this paper cites.
Data quality in context
D. M. Strong, Y. W. Lee, and R. Y. Wang · 1997
Earlier work this paper cites.
Consistent query answers in inconsistent databases
M. Arenas, L. Bertossi, and J. Chomicki · 1999
Earlier work this paper cites.
Improving predictive inference under covariate shift by weighting the log-likelihood function
H. Shimodaira · 2000
Earlier work this paper cites.
The Elements of Statistical Learning
J. Friedman, T. Hastie, and R. Tibshirani · 2001
Earlier work this paper cites.
Data quality under a computer science perspective
M. Scannapieco and T. Catarci · 2002
Earlier work this paper cites.
The problem of concept drift: definitions and related work
A. Tsymbal · 2004
Earlier work this paper cites.
Continuous integration: improving software quality and reducing risk
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Earlier work this paper cites.
Direct importance estimation with model selection and its application to covariate shift adaptation
M. Sugiyama, S. Nakajima, H. Kashima, P. V. Buenau, and M. Kawanabe · 2008
Earlier work this paper cites.
Methodologies for data quality assessment and improvement
C. Batini, C. Cappiello, C. Francalanci, and A. Maurino · 2009
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Active learning literature survey
B. Settles · 2009
Earlier work this paper cites.
Probabilistic databases
D. Suciu, D. Olteanu, C. Ré, and C. Koch · 2011
Earlier work this paper cites.
Domain adaptation under target and conditional shift
K. Zhang, B. Schölkopf, K. Muandet, and Z. Wang · 2013
Earlier work this paper cites.
Do we need hundreds of classifiers to solve real world classification problems?
M. Fernández-Delgado, E. Cernadas, S. Barro, and D. Amorim · 2014
Earlier work this paper cites.
A survey on concept drift adaptation
J. Gama, I. Žliobaitė, A. Bifet, M. Pechenizkiy, and A. Bouchachia · 2014
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Understanding Machine Learning: From Theory to Algorithms
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DevOps: A software architect’s perspective
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The Ladder: A Reliable Leaderboard for Machine Learning Competitions
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The reusable holdout: Preserving validity in adaptive data analysis
C. Dwork, V. Feldman, M. Hardt, T. Pitassi, O. Reingold, and A. Roth · 2015
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On the uniform convergence of relative frequencies of events to their probabilities
V. N. Vapnik and A. Y. Chervonenkis · 2015
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Generative adversarial networks as a tool to recover structural information from cryo-electron microscopy data
M. Su, H. Zhang, K. Schawinski, C. Zhang, and M. A. Cianfrocco · 2018
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Sensing social media signals for cryptocurrency news
J. Beck, R. Huang, D. Lindner, T. Guo, Z. Ce, D. Helbing, and N. Antulov-Fantulin · 2019
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RadioGAN–Translations between different radio surveys with generative adversarial networks
N. Glaser, O. I. Wong, K. Schawinski, and C. Zhang · 2019
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Data Cleaning
I. F. Ilyas and X. Chu · 2019
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Towards ML Engineering with TensorFlow Extended (TFX)
K. Katsiapis and K. Haas · 2019
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CleanML: A Benchmark for Joint Data Cleaning and Machine Learning [Experiments and Analysis]
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ActiveClean: Interactive Data Cleaning for Statistical Modeling
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Software 2.0
A. Karpathy · 2017
Cited alongside, same era.
Snorkel: Rapid training data creation with weak supervision
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Generative adversarial networks recover features in astrophysical images of galaxies beyond the deconvolution limit
K. Schawinski, C. Zhang, H. Zhang, L. Fowler, and G. K. Santhanam · 2017
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Using transfer learning to detect galaxy mergers
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Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks
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Overton: A Data System for Monitoring and Improving Machine-Learned Products
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Continuous integration of machine learning models with ease.ml/ci: Towards a rigorous yet practical treatment
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A Forward Modeling Approach to AGN Variability–Method Description and Early Applications
L. F. Sartori, B. Trakhtenbrot, K. Schawinski, N. Caplar, E. Treister, and C. Zhang · 2019
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Differential Data Quality Verification on Partitioned Data
S. Schelter, S. Grafberger, P. Schmidt, T. Rukat, M. Kiessling, A. Taptunov, F. Biessmann, and D. Lange · 2019
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Online Active Model Selection for Pre-trained Classifiers
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Building Continuous Integration Services for Machine Learning
B. Karlaš, M. Interlandi, C. Renggli, W. Wu, C. Zhang, D. Mukunthu Iyappan Babu, J. Edwards, C. Lauren, A. Xu, and M. Weimer · 2020
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On automatic feasibility study for machine learning application development with ease.ml/snoopy
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Ease.ml/snoopy in action: Towards automatic feasibility analysis for machine learning application development
C. Renggli, L. Rimanic, L. Kolar, W. Wu, and C. Zhang · 2020
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On Convergence of Nearest Neighbor Classifiers over Feature Transformations
L. Rimanic, C. Renggli, B. Li, and C. Zhang · 2020
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Ease.ML: A Lifecycle Management System for Machine Learning
L. Aguilar Melgar, D. Dao, S. Gan, N. M. Gürel, N. Hollenstein, J. Jiang, B. Karlaš, T. Lemmin, T. Li, Y. Li, et al · 2021
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What Is MLOps?
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Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions
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