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Artificial Intelligence (AI) is making a profound impact in almost every domain.
Artificial intelligence
Winston, P. H · 1984
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Discovering implicit integrity constraints in rule bases using metagraphs
Basu, A., and Blanning, R. W · 1995
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Active learning with statistical models
Cohn, D. A., Ghahramani, Z., and Jordan, M. I · 1996
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Imputation of missing data using machine learning techniques
Lakshminarayan, K., Harp, S. A., Goldman, R. P., Samad, T., et al · 1996
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An efficient, cost-driven index selection tool for microsoft sql server
Chaudhuri, S., and Narasayya, V. R · 1997
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Stratal slicing, part ii: Real 3-d seismic data
Zeng, H., Henry, S. C., and Riola, J. P · 1998
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Microsoft terraserver: a spatial data warehouse
Barclay, T., Gray, J., and Slutz, D · 2000
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Db2 advisor: An optimizer smart enough to recommend its own indexes
Valentin, G., Zuliani, M., Zilio, D. C., Lohman, G., and Skelley, A · 2000
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Smote: synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P · 2002
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Benchmark development for the evaluation of visualization for data mining
Grinstein, G. G., Hoffman, P., Pickett, R. M., and Laskowski, S. J · 2002
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Data integration: A theoretical perspective
Lenzerini, M · 2002
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Data quality assessment
Pipino, L. L., Lee, Y. W., and Wang, R. Y · 2002
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Data fusion and data grafting
Saporta, G · 2002
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Finding representative patterns with ordered projections
Riquelme, J. C., Aguilar-Ruiz, J. S., and Toro, M · 2003
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Comparison of instance selection algorithms ii. results and comments
Grochowski, M., and Jankowski, N · 2004
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Democratic co-learning
Zhou, Y., and Goldman, S · 2004
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Conditional functional dependencies for data cleaning
Bohannon, P., Fan, W., Geerts, F., Jia, X., and Kementsietsidis, A · 2006
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Integrating structured biological data by kernel maximum mean discrepancy
Borgwardt, K. M., Gretton, A., Rasch, M. J., Kriegel, H.-P., Schölkopf, B., and Smola, A. J · 2006
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Covariate shift adaptation by importance weighted cross validation
Sugiyama, M., Krauledat, M., and Müller, K.-R · 2007
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Freebase: a collaboratively created graph database for structuring human knowledge
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., and Taylor, J · 2008
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Adasyn: Adaptive synthetic sampling approach for imbalanced learning
He, H., Bai, Y., Garcia, E. A., and Li, S · 2008
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Methodologies for data quality assessment and improvement
Batini, C., Cappiello, C., Francalanci, C., and Maurino, A · 2009
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Vox populi: Collecting high-quality labels from a crowd
Dekel, O., and Shamir, O · 2009
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Tuning database configuration parameters with ituned
Duan, S., Thummala, V., and Babu, S · 2009
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Covariate shift by kernel mean matching
Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., and Schölkopf, B · 2009
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Distant supervision for relation extraction without labeled data
Mintz, M., Bills, S., Snow, R., and Jurafsky, D · 2009
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Principal component analysis
Abdi, H., and Williams, L. J · 2010
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Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., and Darrell, T · 2010
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Data warehousing and analytics infrastructure at facebook
Thusoo, A., Shao, Z., Anthony, S., Borthakur, D., Jain, N., Sen Sarma, J., Murthy, R., and Liu, H · 2010
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Dbridge: A program rewrite tool for set-oriented query execution
Chavan, M., Guravannavar, R., Ramachandra, K., and Sudarshan, S · 2011
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Toward a taxonomy of visuals in science communication
Desnoyers, L · 2011
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Starfish: A self-tuning system for big data analytics
Herodotou, H., Lim, H., Luo, G., Borisov, N., Dong, L., Cetin, F. B., and Babu, S · 2011
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Semi-supervised consensus labeling for crowdsourcing
Tang, W., and Lease, M · 2011
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A survey of crowdsourcing systems
Yuen, M.-C., King, I., and Leung, K.-S · 2011
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Introduction to k nearest neighbour classification and condensed nearest neighbour data reduction
Sutton, O · 2012
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Crowder: crowdsourcing entity resolution
Wang, J., Kraska, T., Franklin, M. J., and Feng, J · 2012
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Hadoop: The definitive guide
White, T · 2012
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Feature selection based on information gain
Azhagusundari, B., Thanamani, A. S., et al · 2013
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Evasion attacks against machine learning at test time
Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., and Roli, F · 2013
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What makes a visualization memorable?
Borkin, M. A., Vo, A. A., Bylinskii, Z., Isola, P., Sunkavalli, S., Oliva, A., and Pfister, H · 2013
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Discovering denial constraints
Chu, X., Ilyas, I. F., and Papotti, P · 2013
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Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
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Quantitative program slicing: Separating statements by relevance
Santelices, R., Zhang, Y., Jiang, S., Cai, H., and Zhang, Y.-j · 2013
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Data curation at scale: the data tamer system
Stonebraker, M., Bruckner, D., Ilyas, I. F., Beskales, G., Cherniack, M., Zdonik, S. B., Pagan, A., and Xu, S · 2013
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Linear discriminant analysis
Xanthopoulos, P., Pardalos, P. M., Trafalis, T. B., Xanthopoulos, P., Pardalos, P. M., and Trafalis, T. B · 2013
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Data normalization and standardization: a technical report
Ali, P. J. M., Faraj, R. H., Koya, E., Ali, P. J. M., and Faraj, R. H · 2014
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On the benefits and drawbacks of radial diagrams
Burch, M., and Weiskopf, D · 2014
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A survey of clustering algorithms for big data: Taxonomy and empirical analysis
Fahad, A., Alshatri, N., Tari, Z., Alamri, A., Khalil, I., Zomaya, A. Y., Foufou, S., and Bouras, A · 2014
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Estimating the number and sizes of fuzzy-duplicate clusters
Heise, A., Kasneci, G., and Naumann, F · 2014
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Tpc-di: the first industry benchmark for data integration
Poess, M., Rabl, T., Jacobsen, H.-A., and Caufield, B · 2014
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Overview of amazon web services
Varia, J., Mathew, S., et al · 2014
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Datahub: Collaborative data science & dataset version management at scale
Bhardwaj, A., Bhattacherjee, S., Chavan, A., Deshpande, A., Elmore, A. J., Madden, S., and Parameswaran, A. G · 2015
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Efficient and robust automated machine learning
Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., and Hutter, F · 2015
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Rodi: A benchmark for automatic mapping generation in relational-to-ontology data integration
Pinkel, C., Binnig, C., Jiménez-Ruiz, E., May, W., Ritze, D., Skjæveland, M. G., Solimando, A., and Kharlamov, E · 2015
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Using random undersampling to alleviate class imbalance on tweet sentiment data
Prusa, J., Khoshgoftaar, T. M., Dittman, D. J., and Napolitano, A · 2015
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Embedded unsupervised feature selection
Wang, S., Tang, J., and Liu, H · 2015
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Voyager: Exploratory analysis via faceted browsing of visualization recommendations
Wongsuphasawat, K., Moritz, D., Anand, A., Mackinlay, J., Howe, B., and Heer, J · 2015
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Feature selection and analysis on correlated gas sensor data with recursive feature elimination
Yan, K., and Zhang, D · 2015
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Character-level convolutional networks for text classification
Zhang, X., Zhao, J., and LeCun, Y · 2015
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Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., and Fidler, S · 2015
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Benchmarking data curation systems
Arocena, P. C., Glavic, B., Mecca, G., Miller, R. J., Papotti, P., and Santoro, D · 2016
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Learning to rewrite queries
He, Y., Tang, J., Ouyang, H., Kang, C., Yin, D., and Chang, Y · 2016
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To join or not to join? thinking twice about joins before feature selection
Kumar, A., Naughton, J., Patel, J. M., and Zhu, X · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.-M., Fawzi, A., and Frossard, P · 2016
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Data programming: Creating large training sets, quickly
Ratner, A. J., De Sa, C. M., Wu, S., Selsam, D., and Ré, C · 2016
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Apache Spark: A unified engine for big data processing
Zaharia, M., Xin, R. S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M. J., Ghodsi, A., Gonzalez, J., Shenker, S., and Stoica, I · 2016
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Missing data imputation: focusing on single imputation
Zhang, Z · 2016
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Tfx: A tensorflow-based production-scale machine learning platform
Baylor, D., Breck, E., Cheng, H.-T., Fiedel, N., Foo, C. Y., Haque, Z., Haykal, S., Ispir, M., Jain, V., Koc, L., et al · 2017
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Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Chen, P.-Y., Zhang, H., Sharma, Y., Yi, J., and Hsieh, C.-J · 2017
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Deep reinforcement learning from human preferences
Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D · 2017
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A publicly available benchmark for biomedical dataset retrieval: the reference standard for the 2016 biocaddie dataset retrieval challenge
Cohen, T., Roberts, K., Gururaj, A. E., Chen, X., Pournejati, S., Alter, G., Hersh, W. R., Demner-Fushman, D., Ohno-Machado, L., and Xu, H · 2017
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Unsupervised domain adaptation for robust speech recognition via variational autoencoder-based data augmentation
Hsu, W.-N., Zhang, Y., and Glass, J · 2017
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2017
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Feature selection: A data perspective
Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., and Liu, H · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
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Uniclust databases of clustered and deeply annotated protein sequences and alignments
Mirdita, M., Von Den Driesch, L., Galiez, C., Martin, M. J., Söding, J., and Steinegger, M · 2017
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Practical black-box attacks against machine learning
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., and Swami, A · 2017
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Snorkel: Rapid training data creation with weak supervision
Ratner, A., Bach, S. H., Ehrenberg, H., Fries, J., Wu, S., and Ré, C · 2017
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Scribbler: Controlling deep image synthesis with sketch and color
Sangkloy, P., Lu, J., Fang, C., Yu, F., and Hays, J · 2017
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Intrusion detection model using fusion of chi-square feature selection and multi class svm
Thaseen, I. S., and Kumar, C. A · 2017
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Automatic database management system tuning through large-scale machine learning
Van Aken, D., Pavlo, A., Gordon, G. J., and Zhang, B · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Wachter, S., Mittelstadt, B., and Russell, C · 2017
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Time series classification from scratch with deep neural networks: A strong baseline
Wang, Z., Yan, W., and Oates, T · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J., and Gebru, T · 2018
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Robust physical-world attacks on deep learning visual classification
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., and Song, D · 2018
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Aurum: A data discovery system
Fernandez, R. C., Abedjan, Z., Koko, F., Yuan, G., Madden, S., and Stonebraker, M · 2018
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Synthetic data augmentation using gan for improved liver lesion classification
Frid-Adar, M., Klang, E., Amitai, M., Goldberger, J., and Greenspan, H · 2018
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Feature engineering for predictive modeling using reinforcement learning
Khurana, U., Samulowitz, H., and Turaga, D · 2018
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Adversarial examples in the physical world
Kurakin, A., Goodfellow, I. J., and Bengio, S · 2018
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Comparison-based inverse classification for interpretability in machine learning
Laugel, T., Lesot, M.-J., Marsala, C., Renard, X., and Detyniecki, M · 2018
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Detecting and correcting for label shift with black box predictors
Lipton, Z., Wang, Y.-X., and Smola, A · 2018
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Deepeye: Towards automatic data visualization
Luo, Y., Qin, X., Tang, N., and Li, G · 2018
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Deep learning for healthcare: review, opportunities and challenges
Miotto, R., Wang, F., Wang, S., Jiang, X., and Dudley, J. T · 2018
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Table union search on open data
Nargesian, F., Zhu, E., Pu, K. Q., and Miller, R. J · 2018
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An adaptive approach for index tuning with learning classifier systems on hybrid storage environments
Pedrozo, W. G., Nievola, J. C., and Ribeiro, D. C · 2018
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al · 2018
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Data quality: The role of empiricism
Sadiq, S., Dasu, T., Dong, X. L., Freire, J., Ilyas, I. F., Link, S., Miller, M. J., Naumann, F., Zhou, X., and Srivastava, D · 2018
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Automating large-scale data quality verification
Schelter, S., Lange, D., Schmidt, P., Celikel, M., Biessmann, F., and Grafberger, A · 2018
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Poison frogs! targeted clean-label poisoning attacks on neural networks
Shafahi, A., Huang, W. R., Najibi, M., Suciu, O., Studer, C., Dumitras, T., and Goldstein, T · 2018
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Data integration: The current status and the way forward
Stonebraker, M., Ilyas, I. F., et al · 2018
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Deep learning for computer vision: A brief review
Voulodimos, A., Doulamis, N., Doulamis, A., Protopapadakis, E., et al · 2018
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Deep learning for biology
Webb, S., et al · 2018
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A benchmark and comparison of active learning for logistic regression
Yang, Y., and Loog, M · 2018
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mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2018
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Algorithmic recourse: from counterfactual explanations to interventions
Karimi, A.-H., Schölkopf, B., and Valera, I · 2021
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Variational diffusion models
Kingma, D., Salimans, T., Poole, B., and Ho, J · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al · 2021
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Tods: An automated time series outlier detection system
Lai, K.-H., Zha, D., Wang, G., Xu, J., Zhao, Y., Kumar, D., Chen, Y., Zumkhawaka, P., Wan, M., Martinez, D., et al · 2021
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Revisiting time series outlier detection: Definitions and benchmarks
Lai, K.-H., Zha, D., Xu, J., Zhao, Y., Wang, G., and Hu, X · 2021
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Automated anomaly detection via curiosity-guided search and self-imitation learning
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A marketplace for data: An algorithmic solution
Agarwal, A., Dahleh, M., and Sarkar, T · 2019
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Regularized learning for domain adaptation under label shifts
Azizzadenesheli, K., Liu, A., Yang, F., and Anandkumar, A · 2019
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Bridging the semantic gap with sql query logs in natural language interfaces to databases
Baik, C., Jagadish, H. V., and Li, Y · 2019
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Propublica’s compas data revisited
Barenstein, M · 2019
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Introduction to scikit-learn
Bisong, E., and Bisong, E · 2019
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Slice finder: Automated data slicing for model validation
Chung, Y., Kraska, T., Polyzotis, N., Tae, K. H., and Whang, S. E · 2019
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Li, Y., Chen, Z., Zha, D., Zhou, K., Jin, H., Chen, H., and Hu, X · 2021
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Autood: Neural architecture search for outlier detection
Li, Y., Chen, Z., Zha, D., Zhou, K., Jin, H., Chen, H., and Hu, X · 2021
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A survey on bias and fairness in machine learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A · 2021
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Towards data-centric machine learning: a short review
Miranda, L. J · 2021
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Comparison of different image data augmentation approaches
Nanni, L., Paci, M., Brahnam, S., and Lumini, A · 2021
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Data-centric ai resource hub
Ng, A · 2021
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Data-centric ai competition
Ng, A., Laird, D., and He, L · 2021
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Mind the performance gap: examining dataset shift during prospective validation
Otles, E., Oh, J., Li, B., Bochinski, M., Joo, H., Ortwine, J., Shenoy, E., Washer, L., Young, V. B., Rao, K., et al · 2021
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Deep learning for anomaly detection: A review
Pang, G., Shen, C., Cao, L., and Hengel, A. V. D · 2021
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Carla: a python library to benchmark algorithmic recourse and counterfactual explanation algorithms
Pawelczyk, M., Bielawski, S., Heuvel, J. v. d., Richter, T., and Kasneci, G · 2021
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What can data-centric ai learn from data and ml engineering?
Polyzotis, N., and Zaharia, M · 2021
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A survey of deep active learning
Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Gupta, B. B., Chen, X., and Wang, X · 2021
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Sliceline: Fast, linear-algebra-based slice finding for ml model debugging
Sagadeeva, S., and Boehm, M · 2021
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“everyone wants to do the model work, not the data work”: Data cascades in high-stakes ai
Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P., and Aroyo, L. M · 2021
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Active feature selection for the mutual information criterion
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Do image classifiers generalize across time?
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Towards natural language interfaces for data visualization: A survey
Shen, L., Shen, E., Luo, Y., Yang, X., Hu, X., Zhang, X., Tai, Z., and Wang, J · 2021
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Towards out-of-distribution generalization: A survey
Shen, Z., Liu, J., He, Y., Zhang, X., Xu, R., Yu, H., and Cui, P · 2021
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Text data augmentation for deep learning
Shorten, C., Khoshgoftaar, T. M., and Furht, B · 2021
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Snowy: Recommending utterances for conversational visual analysis
Srinivasan, A., and Setlur, V · 2021
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Benchmarking differentially private synthetic data generation algorithms
Tao, Y., McKenna, R., Hay, M., Machanavajjhala, A., and Miklau, G · 2021
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A crowdsourcing open platform for literature curation in uniprot
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Time series data augmentation for deep learning: A survey
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Fairness-aware unsupervised feature selection
Xing, X., Liu, H., Chen, C., and Li, J · 2021
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Gpt3mix: Leveraging large-scale language models for text augmentation
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Bartscore: Evaluating generated text as text generation
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An evaluation-focused framework for visualization recommendation algorithms
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Rlcard: a platform for reinforcement learning in card games
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Simplifying deep reinforcement learning via self-supervision
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Rank the episodes: A simple approach for exploration in procedurally-generated environments
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Douzero: Mastering doudizhu with self-play deep reinforcement learning
Zha, D., Xie, J., Ma, W., Zhang, S., Lian, X., Hu, X., and Liu, J · 2021
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Dirichlet energy constrained learning for deep graph neural networks
Zhou, K., Huang, X., Zha, D., Chen, R., Li, L., Choi, S.-H., and Hu, X · 2021
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Multi-channel graph neural networks
Zhou, K., Song, Q., Huang, X., Zha, D., Zou, N., and Hu, X · 2021
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Dbmind: A self-driving platform in opengauss
Zhou, X., Jin, L., Sun, J., Zhao, X., Yu, X., Feng, J., Li, S., Wang, T., Li, K., and Liu, L · 2021
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Benchmark and survey of automated machine learning frameworks
Zöller, M.-A., and Huber, M. F · 2021
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Data excellence for ai: why should you care?
Aroyo, L., Lease, M., Paritosh, P., and Schaekermann, M · 2022
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A study on the evaluation of generative models
Betzalel, E., Penso, C., Navon, A., and Fetaya, E · 2022
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Benchmark of filter methods for feature selection in high-dimensional gene expression survival data
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A feature extraction & selection benchmark for structural health monitoring
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Mitigating relational bias on knowledge graphs
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A human-ml collaboration framework for improving video content reviews
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Data augmentation for deep graph learning: A survey
Ding, K., Xu, Z., Tong, H., and Liu, H · 2022
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Human-ai collaboration for improving the identification of cars for autonomous driving
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G-mixup: Graph data augmentation for graph classification
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Cascaded diffusion models for high fidelity image generation
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Data-centric artificial intelligence
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The principles of data-centric ai (dcai)
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Fmp: Toward fair graph message passing against topology bias
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Generalized demographic parity for group fairness
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An information fusion approach to learning with instance-dependent label noise
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Chart-to-text: A large-scale benchmark for chart summarization
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Tts-gan: A transformer-based time-series generative adversarial network
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Towards learning disentangled representations for time series
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Rsc: Accelerating graph neural networks training via randomized sparse computations
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Focus: Flexible optimizable counterfactual explanations for tree ensembles
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Dataperf: Benchmarks for data-centric ai development
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Interpretability and fairness evaluation of deep learning models on mimic-iv dataset
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Training language models to follow instructions with human feedback
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Cleaning big data: Most time-consuming, least enjoyable data science task, survey says, Oct 2022
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High-resolution image synthesis with latent diffusion models
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Dc-check: A data-centric ai checklist to guide the development of reliable machine learning systems
Seedat, N., Imrie, F., and van der Schaar, M · 2022
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
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Blood-based transcriptomic signature panel identification for cancer diagnosis: benchmarking of feature extraction methods
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In-processing modeling techniques for machine learning fairness: A survey
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Bed: A real-time object detection system for edge devices
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Accelerating shapley explanation via contributive cooperator selection
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Usb: A unified semi-supervised learning benchmark for classification
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Knowledge graph quality management: a comprehensive survey
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Autoshard: Automated embedding table sharding for recommender systems
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Dreamshard: Generalizable embedding table placement for recommender systems
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Towards automated imbalanced learning with deep hierarchical reinforcement learning
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Towards similarity-aware time-series classification
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Autovideo: An automated video action recognition system
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A survey on programmatic weak supervision
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Facilitating database tuning with hyper-parameter optimization: a comprehensive experimental evaluation
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Rein: A comprehensive benchmark framework for data cleaning methods in ml pipelines
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Efficient xai techniques: A taxonomic survey
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Cortx: Contrastive framework for real-time explanation
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Fairly predicting graft failure in liver transplant for organ assigning
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Active ensemble learning for knowledge graph error detection
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Weakly supervised anomaly detection: A survey
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
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Towards personalized preprocessing pipeline search
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Landing ai
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Bring your own view: Graph neural networks for link prediction with personalized subgraph selection
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Scale ai
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Data collection and quality challenges in deep learning: A data-centric ai perspective
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Data-centric ai: Perspectives and challenges
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