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The field of automated machine learning (AutoML) introduces techniques that automate parts of the development of machine learning (ML) systems, accelerating the process and reducing barriers for novices.
Multi-Objective Automatic Machine Learning with AutoxgboostMC
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Testing heuristics: We have it all wrong
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Efficient Global Optimization of Expensive Black Box Functions
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Learning in the “Real World”
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Does automation bias decision-making?
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The CRISP-DM model: the new blueprint for data mining
Shearer, C. (2000) · 2000
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Random Forests
Breimann, L. (2001) · 2001
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AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., and Smola, A. (2020) · 2003
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Performance assessment of multiobjective optimizers: An analysis and review
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Multi-objective Model Selection for Support Vector Machines
Igel, C. (2005) · 2005
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ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems
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Importance of tuning hyperparameters of machine learning algorithms
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Building Classifiers with Independency Constraints
Calders, T., Kamiran, F., and Pechenizkiy, M. (2009) · 2009
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Particle Swarm Model Selection
Escalante, H., Montes, M., and Sucar, E. (2009) · 2009
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Three naive Bayes approaches for discrimination-free classification
Calders, T., and Verwer, S. (2010) · 2010
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Discrimination Aware Decision Tree Learning
Kamiran, F., Calders, T., and Pechenizkiy, M. (2010) · 2010
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Debiasing classifiers: is reality at variance with expectation?
Agrawal, A., Pfisterer, F., Bischl, B., Chen, J., Sood, S., Shah, S., Buet-Golfouse, F., Mateen, B., and Vollmer, S. (2020) · 2011
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Algorithms for Hyper-Parameter Optimization
Bergstra, J., Bardenet, R., Bengio, Y., and Kégl, B. (2011) · 2011
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Sequential Model-Based Optimization for General Algorithm Configuration
Hutter, F., Hoos, H., and Leyton-Brown, K. (2011) · 2011
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Data preprocessing techniques for classification without discrimination
Kamiran, F., and Calders, T. (2011) · 2011
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Random Search for Hyper-Parameter Optimization
Bergstra, J., and Bengio, Y. (2012) · 2012
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Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. (2012) · 2012
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Practical Bayesian Optimization of Machine Learning Algorithms
Snoek, J., Larochelle, H., and Adams, R. (2012) · 2012
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Machine Learning that Matters
Wagstaff, K. (2012) · 2012
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Identifying Key Algorithm Parameters and Instance Features using Forward Selection
Hutter, F., Hoos, H. H., and Leyton-Brown, K. (2013) · 2013
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Techniques for Discrimination-Free Predictive Models
Kamiran, F., Calders, T., and Pechenizkiy, M. (2013) · 2013
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Almost Optimal Exploration in Multi-Armed Bandits
Karnin, Z., Koren, T., and Somekh, O. (2013) · 2013
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Auto-WEKA: combined selection and Hyperparameter Optimization of classification algorithms
Thornton, C., Hutter, F., Hoos, H., and Leyton-Brown, K. (2013) · 2013
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An Efficient Approach for Assessing Hyperparameter Importance
Hutter, F., Hoos, H., and Leyton-Brown, K. (2014) · 2014
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Certifying and Removing Disparate Impact
Feldman, M., Friedler, S., Moeller, J., Scheidegger, C., and Venkatasubramanian, S. (2015) · 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) · 2015
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Scalable Bayesian Optimization Using Deep Neural Networks
Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., Sundaram, N., Patwary, M., Prabhat, and Adams, R. (2015) · 2015
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Machine Bias.
Angwin, J., Larson, J., Mattu, S., and Kichner, L. (2016) · 2016
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Big data’s disparate impact
Barocas, S., and Selbst, A. (2016) · 2016
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XGBoost: A Scalable Tree Boosting System
Chen, T., and Guestrin, C. (2016) · 2016
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Analysing differences between algorithm configurations through ablation
Fawcett, C., and Hoos, H. (2016) · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N. (2016) · 2016
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A general framework for constrained Bayesian optimization using information-based search
Hernández-Lobato, J., Gelbart, M., Adams, R., Hoffman, M., and Ghahramani, Z. (2016) · 2016
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Multi-objective parameter configuration of machine learning algorithms using model-based optimization
Horn, D., and Bischl, B. (2016) · 2016
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Non-stochastic Best Arm Identification and Hyperparameter Optimization
Jamieson, K., and Talwalkar, A. (2016) · 2016
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To predict and serve?
Lum, K., and Isaac, W. (2016) · 2016
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Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
Olson, R., Bartley, N., Urbanowicz, R., and Moore, J. (2016) · 2016
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Efficient Parameter Importance Analysis via Ablation with Surrogates
Biedenkapp, A., Lindauer, M., Eggensperger, K., Fawcett, C., Hoos, H., and Hutter, F. (2017) · 2017
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Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
Chouldechova, A. (2017) · 2017
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Algorithmic Decision Making and the Cost of Fairness
Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., and Huq, A. (2017) · 2017
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Google Vizier: A Service for Black-Box Optimization
Golovin, D., Solnik, B., Moitra, S., Kochanski, G., Karro, J., and Sculley, D. (2017) · 2017
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LightGBM: A Highly Efficient Gradient Boosting Decision Tree
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017) · 2017
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Inherent Trade-Offs in the Fair Determination of Risk Scores
Kleinberg, J., Mullainathan, S., and Raghavan, M. (2017) · 2017
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Counterfactual Fairness
Kusner, M., Loftus, J., Russell, C., and Silva, R. (2017) · 2017
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Gender and dialect bias in YouTube’s automatic captions
Tatman, R. (2017) · 2017
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Fairness Constraints: Mechanisms for Fair Classification
Zafar, M., Valera, I., Rogriguez, M., and Gummadi, K. (2017) · 2017
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A reductions approach to fair classification
Agarwal, A., Beygelzimer, A., Dudík, M., Langford, J., and Wallach, H. (2018) · 2018
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CAVE: Configuration Assessment, Visualization and Evaluation
Biedenkapp, A., Marben, J., Lindauer, M., and Hutter, F. (2018) · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J., and Gebru, T. (2018) · 2018
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Emergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Research
Cooper, A., and Abrams, E. (2021) · 2021
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Fits and starts: Enterprise use of AutoML and the role of humans in the loop
Crisan, A., and Fiore-Gartland, B. (2021) · 2021
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Promoting Fairness through Hyperparameter Optimization
Cruz, A., Saleiro, P., Belem, C., Soares, C., and Bizarro, P. (2021) · 2021
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The Benchmark Lottery
Dehghani, M., Tay, Y., Gritsenko, A., Zhao, Z., Houlsby, N., Diaz, F., Metzler, D., and Vinyals, O. (2021) · 2021
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Retiring Adult: New Datasets for Fair Machine Learning
Ding, F., Hardt, M., Miller, J., and Schmidt, L. (2021) · 2021
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Automated Machine Learning–A Brief Review at the End of the Early Years
Escalante, H. (2021) · 2021
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Bayesian Optimization in AlphaGo
Chen, Y., Huang, A., Wang, Z., Antonoglou, I., Schrittwieser, J., Silver, D., and de Freitas, N. (2018) · 2018
Cited alongside, same era.
The measure and mismeasure of fairness: A critical review of fair machine learning
Corbett-Davies, S., and Goel, S. (2018) · 2018
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Amazon scraps secret AI recruiting tool that showed bias against women
Dastin, J. (2018) · 2018
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Empirical Risk Minimization Under Fairness Constraints
Donini, M., Oneto, L., Ben-David, S., Shawe-Taylor, J., and Pontil, M. (2018) · 2018
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Runaway feedback loops in predictive policing
Ensign, D., Friedler, S., Neville, S., Scheidegger, C., and Venkatasubramanian, S. (2018) · 2018
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Analysis of the AutoML Challenge Series 2015-2018
Guyon, I., Sun-Hosoya, L., Boullé, M., Escalante, H., Escalera, S., Liu, Z., Jajetic, D., Ray, B., Saeed, M., Sebag, M., Statnikov, A., Tu, W., and Viegas, E. (2019) · 2018
Cited alongside, same era.
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Datasheets for datasets
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J., Wallach, H., Daumé III, H., and Crawford, K. (2021) · 2021
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On statistical criteria of algorithmic fairness
Hedden, B. (2021) · 2021
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On the Moral Justification of Statistical Parity
Hertweck, C., Heitz, C., and Loi, M. (2021) · 2021
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NAS-HPO-Bench-II: A Benchmark Dataset on Joint Optimization of Convolutional Neural Network Architecture and Training Hyperparameters
Hirose, Y., Yoshinari, N., and Shirakawa, S. (2021) · 2021
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Measurement and fairness
Jacobs, A., and Wallach, H. (2021) · 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) · 2021
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Algorithmic fairness: Choices, assumptions, and definitions
Mitchell, S., Potash, E., Barocas, S., D’Amour, A., and Lum, K. (2021) · 2021
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Explaining Hyperparameter Optimization via Partial Dependence Plots
Moosbauer, J., Herbinger, J., Casalicchio, G., Lindauer, M., and Bischl, B. (2021) · 2021
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Fair Bayesian Optimization
Perrone, V., Donini, M., Zafar, M., Schmucker, R., Kenthapadi, K., and Archambeau, C. (2021) · 2021
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AI and the Everything in the Whole Wide World Benchmark
Raji, I., Denton, E., Bender, E., Hanna, A., and Paullada, A. (2021) · 2021
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Multi-objective Asynchronous Successive Halving
Schmucker, R., Donini, M., Zafar, M., Salinas, D., and Archambeau, C. (2021) · 2021
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A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle
Suresh, H., and Guttag, J. (2021) · 2021
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Why fairness cannot be automated: Bridging the gap between EU non-discrimination law and AI
Wachter, S., Mittelstadt, B., and Russell, C. (2021) · 2021
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FLAML: A Fast and Lightweight AutoML Library
Wang, C., Wu, Q., Weimer, M., and Zhu, E. (2021) · 2021
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Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows
Xin, D., Wu, E., Lee, D., Salehi, N., and Parameswaran, A. (2021) · 2021
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On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning
Zhang, B., Rajan, R., Pineda, L., Lambert, N., Biedenkapp, A., Chua, K., Hutter, F., and Calandra, R. (2021) · 2021
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Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL
Zimmer, L., Lindauer, M., and Hutter, F. (2021) · 2021
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JAHS-Bench-201: A Foundation For Research On Joint Architecture And Hyperparameter Search
Bansal, A., Stoll, D., Janowski, M., Zela, A., and Hutter, F. (2022) · 2022
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Automating Data Science
De Bie, T., De Raedt, L., Hernández-Orallo, J., Hoos, H., Smyth, P., and Williams, C. (2022) · 2022
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Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits
Deng, W., Nagireddy, M., Lee, M., Singh, J., Wu, Z., Holstein, K., and Zhu, H. (2022) · 2022
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Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M., and Hutter, F. (2022) · 2022
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Bayesian Optimization
Garnett, R. (2022) · 2022
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AMLB: an AutoML Benchmark
Gijsbers, P., Bueno, M., Coors, S., LeDell, E., Poirier, S., Thomas, J., Bischl, B., and Vanschoren, J. (2022) · 2022
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Sensible AI: Re-Imagining Interpretability and Explainability Using Sensemaking Theory
Kaur, H., Adar, E., Gilbert, E., and Lampe, C. (2022) · 2022
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Auditing the AI auditors: A framework for evaluating fairness and bias in high stakes AI predictive models
Landers, R., and Behrend, T. (2022) · 2022
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De-Biasing “Bias” Measurement
Lum, K., Zhang, Y., and Bower, A. (2022) · 2022
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Assessing the Fairness of AI Systems: AI Practitioners’ Processes, Challenges, and Needs for Support
Madaio, M., Egede, L., Subramonyam, H., Wortman Vaughan, J., and Wallach, H. (2022) · 2022
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General pitfalls of model-agnostic interpretation methods for machine learning models
Molnar, C., König, G., Herbinger, J., Freiesleben, T., Dandl, S., Scholbeck, C., Casalicchio, G., Grosse-Wentrup, M., and Bischl, B. (2022) · 2022
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A survey on multi-objective hyperparameter optimization algorithms for Machine Learning
Morales-Hernández, A., Nieuwenhuyse, I. V., and Gonzalez, S. (2022) · 2022
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A survey on datasets for fairness-aware machine learning
Quy, T., Roy, A., Iosifidis, V., Zhang, W., and Ntoutsi, E. (2022) · 2022
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The Long Arc of Fairness: Formalisations and Ethical Discourse
Schwöbel, P., and Remmers, P. (2022) · 2022
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Operationalizing Machine Learning: An Interview Study
Shankar, S., Garcia, R., Hellerstein, J., and Parameswaran, A. (2022) · 2022
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The four-fifths rule is not disparate impact: a woeful tale of epistemic trespassing in algorithmic fairness
Watkins, E., McKenna, M., and Chen, J. (2022) · 2022
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Does the End Justify the Means? On the Moral Justification of Fairness-Aware Machine Learning
Weerts, H., Royakkers, L., and Pechenizkiy, M. (2022) · 2022
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Fairness in Machine Learning: A Survey
Caton, S., and Haas, C. (2023) · 2023
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Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition
Dooley, S., Sukthanker, R., Dickerson, J., White, C., Hutter, F., and Goldblum, M. (2023) · 2023
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Mind the Gap: Measuring Generalization Performance Across Multiple Objectives
Feurer, M., Eggensperger, K., Bergman, E., Pfisterer, F., Bischl, B., and Hutter, F. (2023) · 2023
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Multi-Objective Hyperparameter Optimization in Machine Learning – An Overview
Karl, F., Pielok, T., Moosbauer, J., Pfisterer, F., Coors, S., Binder, M., Schneider, L., Thomas, J., Richter, J., Lang, M., Garrido-Merchán, E., Branke, J., and Bischl, B. (2023) · 2023
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Can We Trust Fair-AI?
Ruggieri, S., Alvarez, J., Pugnana, A., State, L., and Turini, F. (2023) · 2023
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Fairlearn: Assessing and Improving Fairness of AI Systems
Weerts, H., Dudík, M., Edgar, R., Jalali, A., Lutz, R., and Madaio, M. (2023) · 2023
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Neural Architecture Search: Insights from 1000 Papers
White, C., Safari, M., Sukthanker, R., Ru, B., Elsken, T., Zela, A., Dey, D., and Hutter, F. (2023) · 2023
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Algorithmic Fairness Datasets: The Story so Far
Fabris, A., Messina, S., Silvello, G., and Susto, G. (2022) · 2074
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