Peter Schulam
Peter Schulam
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Title
Cited by
Cited by
Year
Reliable decision support using counterfactual models
P Schulam, S Saria
Advances in Neural Information Processing Systems 30, 1697-1708, 2017
132*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
1052015
A framework for individualizing predictions of disease trajectories by exploiting multi-resolution structure
P Schulam, S Saria
arXiv preprint arXiv:1601.04674, 2016
992016
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
702019
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
682012
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
582020
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
572012
Opportunities in machine learning for healthcare
M Ghassemi, T Naumann, P Schulam, AL Beam, R Ranganath
arXiv preprint arXiv:1806.00388, 2018
542018
Can you trust this prediction? Auditing pointwise reliability after learning
P Schulam, S Saria
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
532019
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
462019
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
342013
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
322013
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
242020
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
242016
Noisemes: Manual annotation of environmental noise in audio streams
S Burger, Q Jin, PF Schulam, F Metze
Carnegie Mellon University, 2012
232012
Disease trajectory maps
P Schulam, R Arora
arXiv preprint arXiv:1606.09184, 2016
222016
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
142012
Automatically Determining the Semantic Gradation of German Adjectives.
PF Schulam, C Fellbaum
KONVENS, 163-167, 2010
142010
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
112019
Learning predictive models that transport
A Subbaswamy, P Schulam, S Saria
arXiv preprint arXiv:1812.04597, 2018
92018
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