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2020 – today
- 2023
[c11]Weston Feely, Prabhakar Gupta, Manas Ranjan Mohanty, Timothy Chon, Tuhin Kundu, Vijit Singh, Sandeep Atluri, Tanya Roosta, Viviane Ghaderi, Peter Schulam:
QCon at SemEval-2023 Task 10: Data Augmentation and Model Ensembling for Detection of Online Sexism. SemEval@ACL 2023: 1260-1270- [c10]Peter Schulam
, Ion Muslea:
Improving the Exploration/Exploitation Trade-Off in Web Content Discovery. WWW (Companion Volume) 2023: 1183-1189
2010 – 2019
- 2019
- [c9]Peter Schulam, Suchi Saria:
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning. AISTATS 2019: 1022-1031 - [c8]Adarsh Subbaswamy, Peter Schulam, Suchi Saria:
Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport. AISTATS 2019: 3118-3127 - [c7]Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski:
Active Learning for Decision-Making from Imbalanced Observational Data. ICML 2019: 6046-6055 - [i8]Peter Schulam, Suchi Saria:
Auditing Pointwise Reliability Subsequent to Training. CoRR abs/1901.00403 (2019) - [i7]Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski:
Active Learning for Decision-Making from Imbalanced Observational Data. CoRR abs/1904.05268 (2019) - 2018
- [i6]Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L. Beam, Rajesh Ranganath:
Opportunities in Machine Learning for Healthcare. CoRR abs/1806.00388 (2018) - [i5]Peter Schulam, Suchi Saria:
Discretizing Logged Interaction Data Biases Learning for Decision-Making. CoRR abs/1810.03025 (2018) - [i4]Natalia Antropova, Andrew L. Beam, Brett K. Beaulieu-Jones, Irene Chen, Corey Chivers, Adrian V. Dalca, Samuel G. Finlayson, Madalina Fiterau, Jason Alan Fries, Marzyeh Ghassemi, Mike Hughes, Bruno Jedynak, Jasvinder S. Kandola, Matthew B. A. McDermott, Tristan Naumann, Peter Schulam, Farah Shamout, Alexandre Yahi:
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018. CoRR abs/1811.07216 (2018) - [i3]Adarsh Subbaswamy, Peter Schulam, Suchi Saria:
Learning Predictive Models That Transport. CoRR abs/1812.04597 (2018) - 2017
- [c6]Peter Schulam, Suchi Saria:
Reliable Decision Support using Counterfactual Models. NIPS 2017: 1696-1706 - [i2]Peter Schulam, Suchi Saria:
What-If Reasoning with Counterfactual Gaussian Processes. CoRR abs/1703.10651 (2017) - 2016
- [j1]Peter Schulam, Suchi Saria:
Integrative Analysis using Coupled Latent Variable Models for Individualizing Prognoses. J. Mach. Learn. Res. 17: 234:1-234:35 (2016) - [c5]Peter Schulam, Raman Arora:
Disease Trajectory Maps. NIPS 2016: 4709-4717 - [i1]Peter Schulam, Raman Arora:
Disease Trajectory Maps. CoRR abs/1606.09184 (2016) - 2015
- [c4]Peter Schulam, Fredrick Wigley, Suchi Saria:
Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data: Applications to Phenotyping and Endotype Discovery. AAAI 2015: 2956-2964 - [c3]Peter Schulam, Colin Ligon, Robert Wise, Laura K. Hummers, Fredrick Wigley, Suchi Saria:
A Probabilistic Graphical Model for Individualizing Prognosis in Chronic, Complex Diseases. AMIA 2015 - [c2]Peter Schulam, Suchi Saria:
A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure. NIPS 2015: 748-756 - 2013
- [c1]Peter Schulam, Roni Rosenfeld, Premkumar T. Devanbu:
Building Statistical Language Models of code. DAPSE@ICSE 2013: 1-3
Coauthor Index
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