Ghandeharioun, A., Eoff, B., Jou, B., & Picard, R. W. (2019). Characterizing Sources of Uncertainty to Proxy Calibration and Disambiguate Annotator and Data Bias. arXiv preprint arXiv:1909.09285.
Nosakhare, E., Picard, R. W. Probabilistic Latent Variable Modeling for Assessing Behavioral Influences on Well-Being," ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’19), August 4–8, 2019, Anchorage, AK, USA. ACM, New York, NY, USA. https://doi.org/10.1145/3292500.3330738
Nosakhare, E., and Picard. R. 2019. Toward Assessing and Recommending Combinations of Behaviors for Improving Health and Well-Being. ACM Trans. Comput. Healthcare 1, 1, Article 4 (December 2019), 29 pages.
Rudovic, O., Zhang, M, Schuller, B., Picard, R. "Multi-modal Active Learning From Human Data: A Deep Reinforcement Learning Approach", In 2019 International Conference on Multimodal Interaction (ICMI ’19), October 14–18, 2019, Suzhou, China. ACM, New York, NY, USA.
Doorley, Ronan & Noyman, Ariel & Sakai, Yasushi & Larson, Kent. (2019). What's your MoCho? Real-time Mode Choice Prediction Using Discrete Choice Models and a HCI Platform. UrbComp SIGKDD 2019
Umematsu, T., Sano, A., Taylor, S., and Picard, R. "Improving Students' Daily Life Stress Forecasting using LSTM Neural Networks." IEEE International Conference on Biomedical and Health Informatics (BHI), Chicago, Illinois, May 2019. (BEST PAPER AWARD - 1st Prize)
Rudovic, O., Lee, J., Dai, M., Schuller, B. , Picard, R. W., " Personalized machine learning for robot perception of affect and engagement in autism therapy," Science Robotics, June 2018.