| 2022 | ICLR | SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning. | Manuel Nonnenmacher, Thomas Pfeil, Ingo Steinwart, David Reeb |
| 2022 | ICML | Utilizing Expert Features for Contrastive Learning of Time-Series Representations. | Manuel T. Nonnenmacher, Lukas Oldenburg, Ingo Steinwart, David Reeb |
| 2017 | AISTATS | Spatial Decompositions for Large Scale SVMs. | Philipp Thomann, Ingrid Blaschzyk, Mona Meister, Ingo Steinwart |
| 2014 | COLT | Elicitation and Identification of Properties. | Ingo Steinwart, Chlo Pasin, Robert C. Williamson, Siyu Zhang |
| 2010 | IGARSS | Using support vector machines for anomalous change detection. | Ingo Steinwart, James Theiler, Daniel Llamocca |
| 2009 | COLT | Optimal Rates for Regularized Least Squares Regression. | Ingo Steinwart, Don R. Hush, Clint Scovel |
| 2007 | COLT | Gaps in Support Vector Optimization. | Nikolas List, Don R. Hush, Clint Scovel, Ingo Steinwart |
| 2006 | COLT | Function Classes That Approximate the Bayes Risk. | Ingo Steinwart, Don R. Hush, Clint Scovel |
| 2005 | COLT | Fast Rates for Support Vector Machines. | Ingo Steinwart, Clint Scovel |