Q Zheng, A Zhang, A Grover - international conference on …, 2022 - proceedings.mlr.press
Recent work has shown that offline reinforcement learning (RL) can be formulated as a sequence modeling problem (Chen et al., 2021; Janner et al., 2021) and solved via …
In performative prediction, predictions guide decision-making and hence can influence the distribution of future data. To date, work on performative prediction has focused on finding …
M Hardt, M Jagadeesan… - Advances in Neural …, 2022 - proceedings.neurips.cc
We introduce the notion of performative power, which measures the ability of a firm operating an algorithmic system, such as a digital content recommendation platform, to …
Predictions about people, such as their expected educational achievement or their credit risk, can be performative and shape the outcome that they are designed to predict …
Z Izzo, L Ying, J Zou - International Conference on Machine …, 2021 - proceedings.mlr.press
Performative distribution shift captures the setting where the choice of which ML model is deployed changes the data distribution. For example, a bank which uses the number of …
D Drusvyatskiy, L Xiao - Mathematics of Operations …, 2023 - pubsonline.informs.org
Stochastic optimization problems often involve data distributions that change in reaction to the decision variables. This is the case, for example, when members of the population …
G Brown, S Hod, I Kalemaj - International conference on …, 2022 - proceedings.mlr.press
Deployed supervised machine learning models make predictions that interact with and influence the world. This phenomenon is called performative prediction by Perdomo et …
Learning problems commonly exhibit an interesting feedback mechanism wherein the population data reacts to competing decision makers' actions. This paper formulates a new …
When reasoning about strategic behavior in a machine learning context it is tempting to combine standard microfoundations of rational agents with the statistical decision theory …