| Reliable decision support using counterfactual models P Schulam, S Saria arXiv preprint arXiv:1703.10651, 2017 | 109 | 2017 |
| 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 | 102 | 2015 |
| A framework for individualizing predictions of disease trajectories by exploiting multi-resolution structure P Schulam, S Saria arXiv preprint arXiv:1601.04674, 2016 | 93 | 2016 |
| 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 | 67 | 2012 |
| 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 | 50 | 2019 |
| Event-based video retrieval using audio Q Jin, P Schulam, S Rawat, S Burger, D Ding, F Metze Thirteenth Annual Conference of the International Speech Communication …, 2012 | 50 | 2012 |
| Opportunities in machine learning for healthcare M Ghassemi, T Naumann, P Schulam, AL Beam, R Ranganath arXiv preprint arXiv:1806.00388, 2018 | 49 | 2018 |
| 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 | 35 | 2019 |
| Can you trust this prediction? Auditing pointwise reliability after learning P Schulam, S Saria The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 34 | 2019 |
| 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 (3), 797-816, 2013 | 30 | 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 | 28 | 2013 |
| 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 | 26 | 2020 |
| Noisemes: Manual annotation of environmental noise in audio streams S Burger, Q Jin, PF Schulam, F Metze Carnegie Mellon University, 2012 | 24 | 2012 |
| Integrative analysis using coupled latent variable models for individualizing prognoses P Schulam, S Saria The Journal of Machine Learning Research 17 (1), 8244-8278, 2016 | 23 | 2016 |
| Disease trajectory maps P Schulam, R Arora arXiv preprint arXiv:1606.09184, 2016 | 21 | 2016 |
| 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 | 15 | 2020 |
| Informedia e-lamp@ trecvid 2012: multimedia event detection and recounting (med and mer) SI Yu, Z Xu, D Ding, W Sze, F Vicente, Z Lan, Y Cai, S Rawat, PF Schulam, ... Carnegie Mellon University, 2012 | 14 | 2012 |
| Automatically Determining the Semantic Gradation of German Adjectives. PF Schulam, C Fellbaum KONVENS, 163-167, 2010 | 13 | 2010 |
| What-if reasoning with counterfactual Gaussian processes P Schulam, S Saria arXiv preprint arXiv:1703.10651, 2017 | 12 | 2017 |
| 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 | 7 | 2019 |
Suchi SariaJohn C. Malone Associate Professor of Computer Science, Statistics, Health Policy, Johns HopkinsBestätigte E-Mail-Adresse bei cs.jhu.edu
