Samantha Kleinberg

• PhD (2010) New York University (Computer Science)

Abstract

1. M, M.; Marsh, J. K.; Kleinberg, S. N. (2019). The Role of Causal Information and Perceived Knowledge in Decision-Making. Cognitive Science Society Annual Meeting.

Book

1. Kleinberg, S. N.; Kleinberg, S. N. (2019). Time and Causality across the Sciences. Cambridge University Press.

Conference Proceeding

1. Kleinberg, S. N.; Alay, E.; Marsh, J. K. (2022). Absence Makes the Trust in Causal Models Grow Stronger. Proceedings of the 44th Annual Meeting of the Cognitive Science Society (CogSci).
2. Marsh, J. K.; Coachys, C.; Kleinberg, S. N. (2022). The Compelling Complexity of Conspiracy Theories. Proceedings of the 44th Annual Meeting of the Cognitive Science Society (CogSci).
3. Lu, C.; Reddy, C. K.; Chakraborty, P.; Kleinberg, S. N.; Ning, Y. N. (2021). Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare. IJCAI.
4. Mirtchouk, M.; Srikishan, B.; Kleinberg, S. N. (2021). Hierarchical Information Criterion for Variable Abstraction. Machine Learning for Healthcare.
5. Mirtchouk, M.; Kleinberg, S. N. (2021). Detecting Granular Eating Behaviors From Body-worn Audio and Motion Sensors. 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (pp. 1--4).
6. Kleinberg, S. N.; Marsh, J. K. (2021). It’s Complicated: Improving Decisions on Causally Complex Topics. CogSci.
7. Hameed, H.; Kleinberg, S. N. (2020). Comparing Machine Learning Techniques for Blood Glucose Forecasting Using Free-living and Patient Generated Data. Machine Learning for Healthcare.
8. Hameed, H.; Kleinberg, S. N. (2020). Investigating potentials and pitfalls of knowledge distillation across datasets for blood glucose forecasting. Proceedings of the 5th Annual Workshop on Knowledge Discovery in Healthcare Data.
9. Kleinberg, S. N.; Marsh, J. K. (2020). Tell me something I don't know: How perceived knowledge influences the use of information during decision making. Proceedings of the 42nd Annual Meeting of the Cognitive Science Society (CogSci).
10. Zheng, M.; Kleinberg, S. N. (2019). Using Domain Knowledge to Overcome Latent Variables in Causal Inference from Time Series. Machine Learning for Healthcare.
11. Yavuz, T. T.; Claassen, J.; Kleinberg, S. N. (2019). Lagged Correlations among Physiological Variables as Indicators of Consciousness in Stroke Patients. AMIA Annual Symposium Proceedings. Washington D.C..
12. Mirtchouk, M.; McGuire, D. L.; Deierlein, A. L.; Kleinberg, S. N. (2019). Automated Estimation of Food Type from Body-worn Audio and Motion Sensors in Free-Living Environments. Machine Learning for Healthcare.

Journal Article

1. Zhang, P.; Fonnesbeck, C.; Schmidt, D.; White, J.; Kleinberg, S. N.; Mulvaney, S. (2022). Understanding Barriers to Self-Management Using Machine Learning and Momentary Assessment in Youth with Diabetes: An Observational Study. JMIR mHealth and uHealth (3 ed., vol. 10).
2. Zheng, M.; Marsh, J. K.; Nickerson, J. N.; Kleinberg, S. N. (2020). How causal information affects decisions. Cognitive Research: Principles and Implications (1 ed., vol. 5).
3. Zheng, M.; Marsh, J. K.; Nickerson, J. N.; Kleinberg, S. N. (2020). How causal information affects decisions.. Cognitive research: principles and implications (1 ed., vol. 5, pp. 6).
4. Zheng, M.; Ni, B.; Kleinberg, S. N. (2019). Automated Meal Detection from CGM Data Through Simulation and Explanation. JAMIA (12 ed., vol. 26, pp. 1592--1599).
5. Zheng, M.; Claassen, J.; Kleinberg, S. N. (2018). Automated Identification of Causal Moderators in Time-Series Data.. Proceedings of machine learning research (vol. 92, pp. 4-22).