Publications [#375310] of Vincent Conitzer

Conference articles PUBLISHED

1. Zhang, H; Cheng, Y; Conitzer, V, When samples are strategically selected, 36th International Conference on Machine Learning, ICML 2019, vol. 2019-June (January, 2019), pp. 12733-12743, ISBN 9781510886988
   (last updated on 2024/07/31)

   Abstract:
   In standard classification problems, the assumption is that the entity making the decision (the principal) has access to all the samples. However, in many contexts, she either docs not have direct access to the samples, or can inspect only a limited set of samples and does not know which are the most relevant ones. In such cases, she must rely on another party (the agent) to either provide the samples or point out the most relevant ones. If the agent has a different objective, then the principal cannot trust the submitted samples to be representative. She must set a policy for how she makes decisions, keeping in mind the agent's incentives. In this paper, we introduce a theoretical framework for this problem and provide key structural and computational results.