no code implementations • 14 Aug 2023 • Mathias Kraus, Stefan Feuerriegel, Maytal Saar-Tsechansky
In this paper, we develop a data-driven decision model for determining a cost-effective allocation of preventive treatments to patients at risk.
no code implementations • 18 Jul 2023 • Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky
While the presence of labeling bias has been discussed conceptually, there is a lack of methodologies to address this problem.
no code implementations • 6 Feb 2023 • Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga, Ligong Han, Wei Sun, Min Kyung Lee, Matthew Lease
We then extend our approach to leverage opportunities and mitigate risks that arise in important contexts in practice: 1) when a team is composed of multiple humans with differential and potentially complementary abilities, 2) when the observational data includes consistent deterministic actions, and 3) when the covariate distribution of future decisions differ from that in the historical data.
no code implementations • 23 Oct 2022 • Nicholas Wolczynski, Maytal Saar-Tsechansky, Tong Wang
The human's reconciliation costs and imperfect discretion behavior introduce the need to develop AI systems which (1) provide recommendations selectively, (2) leverage the human partner's ADB to maximize the team's decision accuracy while regularizing for reconciliation costs, and (3) are inherently interpretable.
no code implementations • 22 Jul 2022 • Maria De-Arteaga, Stefan Feuerriegel, Maytal Saar-Tsechansky
The extensive adoption of business analytics (BA) has brought financial gains and increased efficiencies.
no code implementations • 15 Jul 2022 • Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky
We then empirically show that, when overlooking label bias, collecting more data can aggravate bias, and imposing fairness constraints that rely on the observed labels in the data collection process may not address the problem.
no code implementations • 21 Oct 2021 • Wanxue Dong, Maytal Saar-Tsechansky, Tomer Geva
We first formulate the problem of estimating experts' decision accuracy in this setting and then develop a machine-learning-based framework to address it.
1 code implementation • 24 May 2021 • Ruijiang Gao, Maytal Saar-Tsechansky
Moreover, a given labeler may exhibit different labeling accuracies for different instances.
no code implementations • 17 Nov 2020 • Tong Wang, Maytal Saar-Tsechansky
We formulate a multi-objective optimization for building a surrogate model, where we simultaneously optimize for both predictive performance and bias.
no code implementations • 9 Jan 2014 • Elad Liebman, Maytal Saar-Tsechansky, Peter Stone
In this work we present DJ-MC, a novel reinforcement-learning framework for music recommendation that does not recommend songs individually but rather song sequences, or playlists, based on a model of preferences for both songs and song transitions.