Resheff, Y. S., Harel, R., Zlotnick, O. B., & Rotics, S. (2026). pyecoacc: A python package for supervised learning of behavioural modes from accelerometer data. Methods in Ecology and Evolution, 00, 1–8. (PDF, git)
Resheff, Y. S., Bensch, H. M., Zöttl, M., Harel, R., Matsumoto-Oda, A., Crofoot, M. C., ... & Rotics, S. (2024). How to treat mixed behavior segments in supervised machine learning of behavioural modes from inertial measurement data. Movement Ecology, 12(1), 44.
Resheff, Y. S., Bensch, H. M., Zöttl, M., & Rotics, S. (2022). Correcting a bias in the computation of behavioural time budgets that are based on supervised learning. Methods in Ecology and Evolution, 13(7), 1488-1496.
Resheff, Y. S., Rotics, S., Harel, R., Spiegel, O., & Nathan, R. (2014). AcceleRater: a web application for supervised learning of behavioral modes from acceleration measurements. Movement ecology, 2(1), 1-7.
Bloy, L., Resheff, Y., Kluger, A., & Malovicki-Yaffe, N. (2025). Identifying careless survey respondents through machine learning using responses to a gibberish scale. Advances in Methods and Practices in Psychological Science, 8(4), 25152459251378420. Best paper award @ ESRA 25'
Akavia, A., Leibovich, M., Resheff, Y. S., Ron, R., Shahar, M., & Vald, M. (2022). Privacy-preserving decision trees training and prediction. ACM Transactions on Privacy and Security, 25(3), 1-30. [ECML conference version here]
Ben David, D., Resheff, Y. S., & Tron, T. (2021, July). Explainable AI and adoption of financial algorithmic advisors: an experimental study. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (pp. 390-400).
Resheff, Y. S., Horesh, Y., & Shahar, M. (2023). Proving unfairness of decision making systems without model access. Expert Systems with Applications, 213, 118987.
Sherzer, E., Resheff, Y., & Telek, M. (2025). An Unconstrained Optimization Approach to Moment Fitting with Phase Type Distributions. arXiv preprint arXiv:2505.20379.
Sherzer, E., Baron, O., Krass, D., & Resheff, Y. (2024). Approximating G (t)/GI/1 queues with deep learning. European Journal of Operational Research. (download) Best paper award @ ORSIS 25' (Rotblum prize)
Resheff, Y. S., Merran, E., Sher, M., & Adler, N. (2024). Preemptive Strategic Road Safety Policy: The Case of Minor Accidents. Available at SSRN 4994481. (link)
Resheff, Y. S., Sher, M., & Adler, N. (2024, September). The Temporal Dynamics of Road Traffic Crash Hotspots. In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC) (pp. 1956-1961). IEEE. (link)
Gelashvili, A., Resheff, Y. S., & Blumrosen, G. (2024). Construction of a Social-Media Based Clinical Database–Roadmap, Challenges, and Feasibility for ADHD Recognition. IEEE Access.
Klug, M., Barash, Y., Bechler, S., Resheff, Y. S., Tron, T., Ironi, A., ... & Klang, E. (2020). A gradient boosting machine learning model for predicting early mortality in the emergency department triage: devising a nine-point triage score. Journal of general internal medicine, 35, 220-227.
Wagner, A., Fixler, N., & Resheff, Y. S. (2017, March). A wavelet-based approach to monitoring Parkinson's disease symptoms. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5980-5984). IEEE.