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We investigate the problem of designing optimal classifiers in the strategic classification setting, where the classification is part of a game in which players can modify their features to attain a favorable classification outcome (while incurring some cost).
On the value of private information in an auction: ignorance may be bliss
Richard Engelbrecht-Wiggans · 1986
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Stackelberg games for adversarial prediction problems
Michael Brückner and Tobias Scheffer · 2011
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Complexity of mechanism design with signaling costs
Andrew Kephart and Vincent Conitzer · 2015
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Price of transparency in strategic machine learning
Emrah Akyol, Cedric Langbort, and Tamer Basar · 2016
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Strategic classification
Moritz Hardt, Nimrod Megiddo, Christos Papadimitriou, and Mary Wootters · 2016
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The revelation principle for mechanism design with reporting costs
Andrew Kephart and Vincent Conitzer · 2016
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Mechanism design for value maximizers
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Strategyproof linear regression in high dimensions
Yiling Chen, Chara Podimata, Ariel D Procaccia, and Nisarg Shah · 2018
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Strategic classification from revealed preferences
Jinshuo Dong, Aaron Roth, Zachary Schutzman, Bo Waggoner, and Zhiwei Steven Wu · 2018
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Alex Frankel and Navin Kartik · 2019
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The disparate effects of strategic manipulation
Lily Hu, Nicole Immorlica, and Jennifer Wortman Vaughan · 2019
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The model selection curse
Access to population-level signaling as a source of inequality
Nicole Immorlica, Katrina Ligett, and Juba Ziani · 2019
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How do classifiers induce agents to invest effort strategically?
Jon Kleinberg and Manish Raghavan · 2019
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Downstream effects of affirmative action
Sampath Kannan, Aaron Roth, and Juba Ziani · 2019
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The social cost of strategic classification
Smitha Milli, John Miller, Anca D Dragan, and Moritz Hardt · 2019
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Strategic adaptation to classifiers: A causal perspective
John Miller, Smitha Milli, and Moritz Hardt · 2019
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Kfir Eliaz and Ran Spiegler
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