Fetching the paper…
Reading the bibliography…
We study the problem of learning fair prediction models for unseen test sets distributed differently from the train set.
Improving predictive inference under covariate shift by weighting the log-likelihood function
Hidetoshi Shimodaira. 2000 · 2000
Earlier work this paper cites.
Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care
Institute of Medicine. 2003 · 2003
Earlier work this paper cites.
The elements of statistical learning: data mining, inference and prediction
Trevor Hastie, Robert Tibshirani, Jerome Friedman, and James Franklin. 2005 · 2005
Earlier work this paper cites.
Probabilistic soft interventions in conditional Gaussian networks. In Tenth International Workshop on Artificial Intelligence and Statistics . Society for Artificial Intelligence and Statistics, 214–221
Florian Markowetz, Steffen Grossmann, and Rainer Spang. 2005 · 2005
Earlier work this paper cites.
Discriminative learning for differing training and test distributions. In Proceedings of the 24th international conference on Machine learning . 81–88
Steffen Bickel, Michael Brückner, and Tobias Scheffer. 2007 · 2007
Earlier work this paper cites.
Expressing the Modification of Diet in Renal Disease Study equation for estimating glomerular filtration rate with standardized serum creatinine values
Andrew S Levey, Josef Coresh, Tom Greene, Jane Marsh, Lesley A Stevens, John W Kusek, Frederick Van Lente, and Chronic Kidney Disease Epidemiology Collaboration. 2007 · 2007
Earlier work this paper cites.
Biomarkers of Acute Kidney Injury
Charles L Edelstein. 2008 · 2008
Earlier work this paper cites.
Direct importance estimation with model selection and its application to covariate shift adaptation. In Advances in neural information processing systems . 1433–1440
Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul V Buenau, and Motoaki Kawanabe. 2008 · 2008
Earlier work this paper cites.
Building classifiers with independency constraints. In 2009 IEEE International Conference on Data Mining Workshops . IEEE, 13–18
Toon Calders, Faisal Kamiran, and Mykola Pechenizkiy. 2009 · 2009
Earlier work this paper cites.
Ethical Machine Learning in Health Care
Irene Y. Chen, Emma Pierson, Sherri Rose, Shalmali Joshi, Kadija Ferryman, and Marzyeh Ghassemi. 2021 · 2009
Earlier work this paper cites.
Causality
Judea Pearl. 2009 · 2009
Earlier work this paper cites.
Dataset shift in machine learning
Joaquin Quionero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence. 2009 · 2009
Earlier work this paper cites.
A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010a · 2010
Earlier work this paper cites.
Transportability of causal and statistical relations: A formal approach. In Twenty-Fifth AAAI Conference on Artificial Intelligence
Judea Pearl and Elias Bareinboim. 2011 · 2011
Earlier work this paper cites.
Scikit-learn: Machine Learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011 · 2011
Earlier work this paper cites.
KDIGO clinical practice guidelines for acute kidney injury
Arif Khwaja. 2012 · 2012
Earlier work this paper cites.
Explaining the racial difference in AKI incidence
Morgan E Grams, Kunihiro Matsushita, Yingying Sang, Michelle M Estrella, Meredith C Foster, Adrienne Tin, WH Linda Kao, and Josef Coresh. 2014 · 2014
Earlier work this paper cites.
A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions
Jenna Wiens, John Guttag, and Eric Horvitz. 2014 · 2014
Earlier work this paper cites.
A meta-analysis of the association of estimated GFR, albuminuria, age, race, and sex with acute kidney injury
Morgan E Grams, Yingying Sang, Shoshana H Ballew, Ron T Gansevoort, Heejin Kimm, Csaba P Kovesdy, David Naimark, Cecilia Oien, David H Smith, Josef Coresh, et al · 2015
Earlier work this paper cites.
Is racism a fundamental cause of inequalities in health?
Jo C Phelan and Bruce G Link. 2015 · 2015
Earlier work this paper cites.
Optimal transport for domain adaptation
Nicolas Courty, Rémi Flamary, Devis Tuia, and Alain Rakotomamonjy. 2016 · 2016
Earlier work this paper cites.
Strategic classification. In Proceedings of the 2016 ACM conference on innovations in theoretical computer science . 111–122
Moritz Hardt, Nimrod Megiddo, Christos Papadimitriou, and Mary Wootters. 2016a · 2016
Earlier work this paper cites.
MIMIC-III, a freely accessible critical care database
Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-wei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016 · 2016
Earlier work this paper cites.
To predict and serve?
Kristian Lum and William Isaac. 2016 · 2016
Earlier work this paper cites.
Causal inference by using invariant prediction: identification and confidence intervals
Jonas Peters, Peter Bühlmann, and Nicolai Meinshausen. 2016 · 2016
Earlier work this paper cites.
External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges
Richard D Riley, Joie Ensor, Kym IE Snell, Thomas PA Debray, Doug G Altman, Karel GM Moons, and Gary S Collins. 2016 · 2016
Cited alongside, same era.
Acute kidney disease and renal recovery: consensus report of the Acute Disease Quality Initiative (ADQI) 16 Workgroup
Lakhmir S Chawla, Rinaldo Bellomo, Azra Bihorac, Stuart L Goldstein, Edward D Siew, Sean M Bagshaw, David Bittleman, Dinna Cruz, Zoltan Endre, Robert L Fitzgerald, et al · 2017
Cited alongside, same era.
Algorithmic decision making and the cost of fairness. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 797–806
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq. 2017 · 2017
Cited alongside, same era.
Avoiding discrimination through causal reasoning. In Advances in Neural Information Processing Systems . 656–666
Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf. 2017 · 2017
Cited alongside, same era.
A comparative study of fairness-enhancing interventions in machine learning. In Proceedings of the Conference on Fairness, Accountability, and Transparency . 329–338
Sorelle A Friedler, Carlos Scheidegger, Suresh Venkatasubramanian, Sonam Choudhary, Evan P Hamilton, and Derek Roth. 2019 · 2019
Closest in time.
The role of risk prediction models in prevention and management of AKI. In Seminars in nephrology , Vol. 39. Elsevier, 421–430
Luke E Hodgson, Nicholas Selby, Tao-Min Huang, and Lui G Forni. 2019 · 2019
Closest in time.
Stable and Fair Classification. In International Conference on Machine Learning . 2879–2890
Lingxiao Huang and Nisheeth Vishnoi. 2019 · 2019
Closest in time.
Delayed impact of fair machine learning. In Proceedings of the 28th International Joint Conference on Artificial Intelligence . AAAI Press, 6196–6200
Lydia T Liu, Sarah Dean, Esther Rolf, Max Simchowitz, and Moritz Hardt. 2019 · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Counterfactual fairness. In Advances in Neural Information Processing Systems . 4066–4076
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. 2017 · 2017
Cited alongside, same era.
A Reductions Approach to Fair Classification. In International Conference on Machine Learning . 60–69
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, and Hanna Wallach. 2018 · 2018
Cited alongside, same era.
Empirical risk minimization under fairness constraints. In Advances in Neural Information Processing Systems . 2791–2801
Michele Donini, Luca Oneto, Shai Ben-David, John S Shawe-Taylor, and Massimiliano Pontil. 2018 · 2018
Cited alongside, same era.
Multi-perspective predictive modeling for acute kidney injury in general hospital populations using electronic medical records
Jianqin He, Yong Hu, Xiangzhou Zhang, Lijuan Wu, Lemuel R Waitman, and Mei Liu. 2018 · 2018
Cited alongside, same era.
Residual Unfairness in Fair Machine Learning from Prejudiced Data. In Proceedings of the 35th International Conference on Machine Learning
Nathan Kallus and Angela Zhou. 2018 · 2018
Cited alongside, same era.
Does mitigating ML’s impact disparity require treatment disparity?. In Advances in Neural Information Processing Systems . 8125–8135
Zachary Lipton, Julian McAuley, and Alexandra Chouldechova. 2018 · 2018
Cited alongside, same era.
Learning Adversarially Fair and Transferable Representations. In International Conference on Machine Learning . 3381–3390
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel. 2018 · 2018
Cited alongside, same era.
Domain adaptation by using causal inference to predict invariant conditional distributions. In Advances in Neural Information Processing Systems . 10846–10856
Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, and Joris M Mooij. 2018 · 2018
Cited alongside, same era.
Bret Nestor, Matthew McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C Hughes, Anna Goldenberg, and Marzyeh Ghassemi. 2019 · 2019
Closest in time.
Dissecting racial bias in an algorithm used to manage the health of populations
Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. 2019 · 2019
Closest in time.
Learning fair and transferable representations
Luca Oneto, Michele Donini, Andreas Maurer, and Massimiliano Pontil. 2019 · 2019
Closest in time.
Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (Honolulu, HI, USA) (AIES ’19) . Association for Computing Machinery, New York, NY, USA, 271–278
Stephen Pfohl, Ben Marafino, Adrien Coulet, Fatima Rodriguez, Latha Palaniappan, and Nigam H. Shah. 2019b · 2019
Closest in time.
Eduardo HP Pooch, Pedro L Ballester, and Rodrigo C Barros. 2019 · 2019
Closest in time.
Transfer of Machine Learning Fairness across Domains
Candice Schumann, Xuezhi Wang, Alex Beutel, Jilin Chen, Hai Qian, and Ed H Chi. 2019 · 2019
Closest in time.
Preventing failures due to dataset shift: Learning predictive models that transport. In The 22nd International Conference on Artificial Intelligence and Statistics . 3118–3127
Adarsh Subbaswamy, Peter Schulam, and Suchi Saria. 2019 · 2019
Closest in time.
A clinically applicable approach to continuous prediction of future acute kidney injury
Nenad Tomašev, Xavier Glorot, Jack W Rae, Michal Zielinski, Harry Askham, Andre Saraiva, Anne Mottram, Clemens Meyer, Suman Ravuri, Ivan Protsyuk, et al · 2019
Closest in time.
Fairness without Harm: Decoupled Classifiers with Preference Guarantees. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97) , Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, Long Beach, California, USA, 6373–6382
Berk Ustun, Yang Liu, and David Parkes. 2019 · 2019
Closest in time.
Fairness Constraints: A Flexible Approach for Fair Classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna P Gummadi. 2019 · 2019
Closest in time.
Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements
Lindsay P Zimmerman, Paul A Reyfman, Angela DR Smith, Zexian Zeng, Abel Kho, L Nelson Sanchez-Pinto, and Yuan Luo. 2019 · 2019
Closest in time.
Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?. In 1st Symposium on Foundations of Responsible Computing (FORC 2020) . Schloss Dagstuhl-Leibniz-Zentrum für Informatik
Avrim Blum and Kevin Stangl. 2020 · 2020
Closest in time.
Ensuring fairness beyond the training data
Debmalya Mandal, Samuel Deng, Suman Jana, and Daniel Hsu. 2020 · 2020
Closest in time.
Population-aware hierarchical bayesian domain adaptation via multi-component invariant learning. In Proceedings of the ACM Conference on Health, Inference, and Learning . 182–192
Vishwali Mhasawade, Nabeel Abdur Rehman, and Rumi Chunara. 2020 · 2020
Closest in time.
Joint Causal Inference from Multiple Contexts
Joris M. Mooij, Sara Magliacane, and Tom Claassen. 2020 · 2020
Closest in time.
Robust Fairness under Covariate Shift
Ashkan Rezaei, Anqi Liu, Omid Memarrast, and Brian Ziebart. 2020 · 2020
Closest in time.
Fairness warnings and fair-MAML: learning fairly with minimal data. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency . 200–209
Dylan Slack, Sorelle A Friedler, and Emile Givental. 2020 · 2020
Closest in time.
I-SPEC: An End-to-End Framework for Learning Transportable, Shift-Stable Models
Adarsh Subbaswamy and Suchi Saria. 2020 · 2020
Closest in time.
Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms
Darshali A. Vyas, Leo G. Eisenstein, and David S. Jones. 2020 · 2020
Closest in time.
Fair regression for health care spending
Anna Zink and Sherri Rose. 2020 · 2020
Closest in time.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. 2016 · 2030
Closest in time.