A review of challenges and opportunities in machine learning for health M Ghassemi, T Naumann, P Schulam, AL Beam, IY Chen, R Ranganath AMIA Summits on Translational Science Proceedings 2020, 191, 2020 | 375 | 2020 |
Reliable decision support using counterfactual models P Schulam, S Saria Advances in neural information processing systems 30, 2017 | 248 | 2017 |
Preventing failures due to dataset shift: Learning predictive models that transport A Subbaswamy, P Schulam, S Saria The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 168 | 2019 |
Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data: Applications to Phenotyping and Endotype Discovery P Schulam, F Wigley, S Saria The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), 2015 | 127 | 2015 |
Can you trust this prediction? Auditing pointwise reliability after learning P Schulam, S Saria The 22nd international conference on artificial intelligence and statistics …, 2019 | 122 | 2019 |
A framework for individualizing predictions of disease trajectories by exploiting multi-resolution structure P Schulam, S Saria Advances in neural information processing systems 28, 2015 | 113 | 2015 |
Reporting and implementing interventions involving machine learning and artificial intelligence DW Bates, A Auerbach, P Schulam, A Wright, S Saria Annals of internal medicine 172 (11_Supplement), S137-S144, 2020 | 97 | 2020 |
Practical guidance on artificial intelligence for health-care data M Ghassemi, T Naumann, P Schulam, AL Beam, IY Chen, R Ranganath The Lancet Digital Health 1 (4), e157-e159, 2019 | 97 | 2019 |
Opportunities in machine learning for healthcare M Ghassemi, T Naumann, P Schulam, AL Beam, R Ranganath arXiv preprint arXiv:1806.00388, 2018 | 88 | 2018 |
Beyond audio and video retrieval: towards multimedia summarization D Ding, F Metze, S Rawat, PF Schulam, S Burger, E Younessian, L Bao, ... Proceedings of the 2nd ACM International Conference on Multimedia Retrieval, 1-8, 2012 | 78 | 2012 |
Event-based Video Retrieval Using Audio. Q Jin, PF Schulam, S Rawat, S Burger, D Ding, F Metze Interspeech, 2085-2088, 2012 | 72 | 2012 |
Large, huge or gigantic? Identifying and encoding intensity relations among adjectives in WordNet V Sheinman, C Fellbaum, I Julien, P Schulam, T Tokunaga Language resources and evaluation 47, 797-816, 2013 | 38 | 2013 |
Robust audio-codebooks for large-scale event detection in consumer videos. S Rawat, PF Schulam, S Burger, D Ding, Y Wang, F Metze INTERSPEECH, 2929-2933, 2013 | 37 | 2013 |
Active learning for decision-making from imbalanced observational data I Sundin, P Schulam, E Siivola, A Vehtari, S Saria, S Kaski International conference on machine learning, 6046-6055, 2019 | 34 | 2019 |
Integrative analysis using coupled latent variable models for individualizing prognoses P Schulam, S Saria Journal of Machine Learning Research 17 (232), 1-35, 2016 | 29 | 2016 |
Noisemes: Manual annotation of environmental noise in audio streams S Burger, Q Jin, PF Schulam, F Metze Carnegie Mellon University, 2012 | 28 | 2012 |
Disease trajectory maps P Schulam, R Arora Advances in neural information processing systems 29, 2016 | 25 | 2016 |
Factors associated with physicians’ prescriptions for rheumatoid arthritis drugs not filled by patients HJ Kan, K Dyagilev, P Schulam, S Saria, H Kharrazi, D Bodycombe, ... Arthritis research & therapy 20, 1-12, 2018 | 16 | 2018 |
Learning predictive models that transport A Subbaswamy, P Schulam, S Saria arXiv preprint arXiv:1812.04597, 2018 | 14 | 2018 |
Automatically Determining the Semantic Gradation of German Adjectives. PF Schulam, C Fellbaum KONVENS, 163-167, 2010 | 14 | 2010 |