Maytal Saar-Tsechansky — accepted AI-conference papers

2024

SEL-BALD: Deep Bayesian Active Learning with Selective Labels

Ruijiang Gao, Mingzhang Yin, Maytal Saar-Tsechansky

NeurIPS 2024poster

Machine learning systems are widely used in many high-stakes contexts in which experimental designs for assigning treatments are infeasible. When evaluating decisions is costly, such as investigating fraud cases, or evaluating biopsy decisions, a sample-efficient strategy is needed. However, while e…

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2021

Human-AI Collaboration with Bandit Feedback

Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga, Ligong Han, Min Kyung Lee, Matthew Lease

IJCAI 2021poster

Human-machine complementarity is important when neither the algorithm nor the human yield dominant performance across all instances in a given domain. Most research on algorithmic decision-making solely centers on the algorithm's performance, while recent work that explores human-machine collaborati…

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