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Ingo Steinwart

28 papers · 2001–2024 · 6 conferences · across top CS/AI conferences

Achievements

πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (23) 🌈 Renaissance Researcher (5) 🌍 Conference Polyglot (6) 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (15) 🐝 Cross-Pollinator (14) 🧭 Keyword Pioneer 🐺 Lone Wolf (3) 🌟 Keyword Trendsetter Combo (7) 🌱 Topic Pioneer πŸ† Keyword Champion (2) πŸ”¬ Deep Specialist (10) πŸ’Ž Century Club (28) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (75) πŸš€ Conference Pioneer πŸ”₯ Unstoppable (10)

Conferences

JMLR (14) NIPS (8) AISTATS (2) COLT (2) ICLR (1) ICML (1)

Top co-authors

Andreas Christmann (5) Don Hush (4) David HolzmΓΌller (4) Clint Scovel (4) Philipp Thomann (3) Mona Meister (2) Ingrid Blaschzyk (2) David Reeb (2) Moritz Haas (1) ChloΓ© Pasin (1)

Research topics

Statistics (1)

Keywords

support vector machine (13) kernel methods (9) learning rate (7) oracle inequality (6) gaussian kernel (3) reproducing kernel hilbert space (3) statistical learning theory (3) density-based clustering (2) optimization algorithm (2) universal kernel (2) epsilon-insensitive loss (2) clustering algorithm (2) cluster tree (2) hinge loss (2) kernel density estimation (2) risk minimization (2) unsupervised learning (2) hierarchical clustering (2) neural tangent kernel (2) quantile regression (2)

Papers

Better by default: Strong pre-tuned MLPs and boosted trees on tabular data NIPS 2024
A Framework and Benchmark for Deep Batch Active Learning for Regression JMLR 2023
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension NIPS 2023
Adaptive Clustering Using Kernel Density Estimators JMLR 2023
Utilizing Expert Features for Contrastive Learning of Time-Series Representations ICML 2022
Improved Classification Rates for Localized SVMs JMLR 2022
Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent JMLR 2022
SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning ICLR 2022
Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms JMLR 2020
Kernel Density Estimation for Dynamical Systems JMLR 2018
Spatial Decompositions for Large Scale SVMs AISTATS 2017
Optimal Learning Rates for Localized SVMs JMLR 2016
Towards an Axiomatic Approach to Hierarchical Clustering of Measures JMLR 2015
Elicitation and Identification of Properties COLT 2014
Consistency and Rates for Clustering with DBSCAN AISTATS 2012
Optimal learning rates for least squares SVMs using Gaussian kernels NIPS 2011
Adaptive Density Level Set Clustering COLT 2011
Training SVMs Without Offset JMLR 2011
Universal Kernels on Non-Standard Input Spaces NIPS 2010
Fast Learning from Non-i.i.d. Observations NIPS 2009
Sparsity of SVMs that use the epsilon-insensitive loss NIPS 2008
How SVMs can estimate quantiles and the median NIPS 2007
An Oracle Inequality for Clipped Regularized Risk Minimizers NIPS 2006
QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines JMLR 2006
A Classification Framework for Anomaly Detection JMLR 2005
On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition JMLR 2004
Sparseness of Support Vector Machines JMLR 2003
On the Influence of the Kernel on the Consistency of Support Vector Machines JMLR 2001