Machine Learning

2021

  • F. Croce, M. Andriushchenko, V. Sehwag, N. Flammarion, M. Chiang, P. Mittal, M. Hein (2021). RobustBench: a standardized adversarial robustness benchmark. ICLR Workshop on Security and Safety of Machine Learning Systems PDF (Best Paper Honorable Mention Prize)
  • F. Croce, M. Hein (2021). Mind the box: l_1-APGD for sparse adversarial attacks on image classifiers. ICLR Workshop on Security and Safety of Machine Learning Systems PDF
  • A. Kristiadi, M. Hein, P. Hennig (2021). An Infinite-Feature Extension for Bayesian ReLU nets that fixes their asymptotic overconfidence. ICLR Workshop on Robust and Reliable Machine Learning in the Real World.
  • D. Stutz, N. Chandramoorthy, M. Hein, B. Schiele (2021). Bit Error Robustness for Energy-Efficient DNN Accelerators. MLSys (also accepted at ICLR Workshop on Robust and Reliable Machine Learning in the Real World)  PDF
  • M. Augustin, M. Hein (2021): Out-distribution aware Self-training in an Open World Setting.  available on arxiv PDF

2020

  • J. Bitterwolf, A. Meinke, M. Hein (2020): Certifiably Adversarially Robust Detection of Out-of-Distribution Data. NeurIPS 2020 PDF, presented also at ICML 2020 Workshop on Uncertainty and Robustness
  • D. Stutz, N. Chandramoorthy, M. Hein, B. Schiele (2020). Bit Error Robustness for Energy-Efficient DNN Accelerators. preprint PDF
  • F. Croce, M. Andriushchenko, N. D. Singh, N. Flammarion, M. Hein: Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks. preprint PDF, to be presented at ECCV 2020 Workshop Adversarial Robustness in the Real World
  • M. Augustin, A. Meinke, M. Hein (2020): Adversarial Robustness on In- and Out-Distribution Improves Explainability.  ECCV 2020 PDF
  • M. Andriushchenko, F. Croce, N Flammarion, M. Hein (2020): Square Attack: a query-efficient black-box adversarial attack via random search.  ECCV 2020 PDF   Code
  • F. Croce, M. Hein (2020): Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. ICML 2020 PDF    Benchmark and Code  (also spotlight at the ICML 2020 Workshop on Uncertainty and Robustness)
  • A. Kristiadi, M. Hein, P. Hennig (2020): Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. ICML 2020 PDF
  • D. Stutz, M. Hein, B. Schiele (2020):  Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks. ICML 2020  PDF    Code (also oral at the ICML Workshop on Uncertainty and Robustness)
  • F. Croce, M. Hein (2020): Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack. ICML 2020 PDF Code
  • A. Gautier, M. Hein, F. Tudisco (2020): Computing the norm of nonnegative matrices and the log-Sobolev constant of Markov chains. preprint PDF
  • A. Meinke, M. Hein (2020): Towards neural networks that provably know when they don't know. ICLR 2020  PDF    Code
  • F. Croce, M. Hein (2020): Provable robustness against all adversarial l_p-perturbations for p>=1.  ICLR 2020 PDF   Code

2019

  • M. Andriushchenko, F. Croce, N Flammarion, M. Hein (2019): Square Attack: a query-efficient black-box adversarial attack via random search. preprint PDF   Code
  • F. Croce, M. Hein (2019): Provable robustness against all adversarial l_p-perturbations for p>=1. NeurIPS 2019 Workshop "Machine Learning with Guarantees" PDF   Code
  • A. Meinke, M. Hein (2019): Towards neural networks that provably know when they don't know. NeurIPS 2019 Workshop "Machine Learning with Guarantees"  PDF   Code
  • M. Andriushchenko, M. Hein (2019): Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks. NeurIPS 2019 PDF (oral presentation in NeurIPS 2019 Workshop "Machine Learning with Guarantees")    Code
  • P. Mercado, F. Tudisco, M. Hein (2019): Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs. NeurIPS 2019. PDF (long version)  Code
  • F. Croce, M. Hein (2019): Sparse and imperceivable adversarial attacks. ICCV 2019. PDF  Code
  • N. Garcia Trillos, M. Gerlach, M. Hein, D. Slepcev (2019): Error estimates for spectral convergence of the graph Laplacian on random geometric graphs towards the Laplace--Beltrami operator. accepted at Foundations of Computational Mathematics (FOCM). PDF  (Link to journal)
  • F. Croce, M. Hein (2019): Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack. preprint PDF Code
  • A. Gautier, F. Tudisco, M. Hein (2019): A unifying Perron-Frobenius theorem for nonnegative tensors via multi-homogenous maps. SIAM J. Matrix Analysis (SIMAX), Vol. 40, No. 3, pp. 1206–1231. PDF
  • A. Gautier, F. Tudisco, M. Hein (2019): The Perron-Frobenius theorem for multi-homogeneous mappings. SIAM J. Matrix Analysis (SIMAX), Vol. 40, No. 3, pp. 1179--1205. PDF
  • F. Croce, J. Rauber, M. Hein (2019): Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks. Accepted at International Journal of Computer Vision (IJCV). PDF (Link to journal) Code
  • P. Mercado, F. Tudisco, M. Hein (2019): Spectral Clustering of Signed Graphs via Matrix Power Means. ICML 2019. PDF  Code
  • M. Hein, M. Andriushchenko, J. Bitterwolf (2019): Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem. CVPR 2019 (oral presentation) - (also oral at ICML 2019 Workshop on  Uncertainty & Robustness in Deep Learning). PDF   Code
  • D. Stutz, M. Hein, B. Schiele (2019): Disentangling Adversarial Robustness and Generalization. CVPR 2019. PDF   Code
  • F. Croce, M. Andriushchenko, M. Hein (2019): Provable Robustness of ReLU networks via Maximization of Linear Regions. AISTATS 2019. PDF     Code
  • Q. Nguyen, M. Mukkamala, M. Hein (2019): On the loss landscape of a class of deep neural networks with no bad local valleys. ICLR 2019. PDF

2018

  • ​​​​​​M. Mosbach, M. Andriushchenko, T. Trost, M. Hein, D. Klakow (2018): Logit Pairing Methods Can Fool Gradient-Based Attacks. NeurIPS 2018 Workshop on Security in Machine Learning. PDF  Code
  • F. Croce, M. Hein (2018): A randomized gradient-free attack on ReLU networks. GCPR 2018.  PDF  Code
  • F. Tudisco, P. Mercado, M. Hein (2018): Community Detection in Networks via Nonlinear Modularity Eigenvectors. SIAM Journal of Applied Mathematics, 78(5): 2393--2419. PDF
  • M. Lapin, M. Hein, B. Schiele (2018): Analysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 40(7):1533-1554. PDF
  • F. Tudisco, M. Hein (2018): A nodal domain theorem and a higher-order Cheeger inequality for the graph p-Laplacian. Journal of Spectral Theory, 8(3): 883-908. PDF
  • Q. Nguyen, M. Hein (2018): Optimization Landscape and Expressivity of Deep CNNs. ICML 2018. PDF (long version including proofs)
  • Q. Nguyen, M. Mukkamala, M. Hein (2018): Neural networks should be wide enough to learn disconnected decision regions. ICML 2018. PDF (long version including proofs)
  • N. Garcia Trillos, M. Gerlach, M. Hein, D. Slepcev (2018): Error estimates for spectral convergence of the graph Laplacian on random geometric graphs towards the Laplace--Beltrami operator. PDF
  • A. Gautier, F. Tudisco, M. Hein (2018): A unifying Perron-Frobenius theorem for nonnegative tensors via multi-homogeneous maps. PDF
  • A. Gautier, F. Tudisco, M. Hein (2018): The Perron-Frobenius theorem for multi-homogeneous mappings. PDF
  • P. Mercado, A. Gautier, F. Tudisco, M. Hein (2018): The Power Mean Laplacian for Multilayer Graph Clustering. AISTATS 2018. PDF (Appendix containing proofs) Code

2017

  • Q. Nguyen, M. Hein (2017): The loss surface and expressivity of deep convolutional neural networks. PDF
  • M. Hein, M. Andriushchenko (2017): Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation. NeurIPS 2017. PDF (long version including proofs) Code
  • M. C. Mukkamala, M. Hein (2017): Variants of RMSProp and Adagrad with Logarithmic Regret Bounds. ICML 2017. PDF (long version including proofs) Code
  • Q. Nguyen, M. Hein (2017): The loss surface of deep and wide neural networks. ICML 2017. PDF (long version including proofs)
  • A. Khoreva, R. Beneson, J. Hosang, M. Hein and B. Schiele (2017): Simple does it: Weakly Supervised Instance and Semantic Segmentation. CVPR 2017. PDF
  • A. Gautier, F. Tudisco, M. Hein (2017): The Perron-Frobenius Theorem for Multi-homogeneous Maps. PDF
  • P. Lutsik, M. Slawski, G. Gasparoni, N. Vedeneev, M. Hein, J. Walter (2017): MeDeCom: discovery and quantification of latent components of heterogeneous methylomes. Genome Biology, 18:55. PDF Code

2016

  • M. Lapin, M. Hein, B. Schiele (2016): Analysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel Classification. PDF  Code
  • A. Gautier, Q. Nguyen Ngoc, M. Hein (2016): Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods. NeurIPS 2016. PDF (long version) Matlab Code
  • P. Mercado, F. Tudisco, M. Hein (2016): Clustering Signed Networks with the Geometric Mean of Laplacians. NeurIPS 2016. PDF (long version) Code
  • A. Gautier, M. Hein (2016): Tensor norm and maximal singular vectors of non-negative tensors - a Perron-Frobenius theorem, a Collatz-Wielandt characterization and a generalized power method. Linear Algebra and its Applications, 505:313–343. Article Preprint 
  • F. Tudisco, M. Hein (2016): A nodal domain theorem and a higher-order Cheeger inequality for the graph p-Laplacian. PDF
  • Q. Nguyen, F. Tudisco, A. Gautier, M. Hein (2016): An Efficient Multilinear Optimization Framework for Hypergraph Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 39(6):1054-1075. PDF Code available on request
  • M. Lapin, M. Hein and B. Schiele (2016): Loss Functions for Top-k Error: Analysis and Insights. CVPR 2016. PDF (long version) Code
  • Y. Xian, Z. Akata, G. Sharma, Q. Ngoc, M. Hein, B. Schiele (2016): Latent Embeddings for Zero-shot Classification. CVPR 2016 (spotlight). PDF (long version) 
  • A. Khoreva, R. Beneson, M. Omran, M. Hein,  B. Schiele (2016): Weakly Supervised Object Boundaries. CVPR 2016 (spotlight). PDF (long version)

2015

  • M. Lapin, M. Hein, B. Schiele (2015): Top-k Multiclass SVM. Spotlight at NeurIPS 2015. PDF (long version) Code
  • P. Jawanpuria, M. Lapin, M. Hein, B. Schiele (2015): Efficient Output Kernel Learning for Multiple Tasks. NeurIPS 2015. PDF (long version) Code
  • M. Slawski, P. Li, M. Hein (2015): Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices. NeurIPS 2015. PDF (long version)
  • A. Gautier, M. Hein (2015): Tensor norm and maximal singular vectors of non-negative tensors - a Perron-Frobenius theorem, a Collatz-Wielandt characterization and a generalized power method. PDF 
  • Q. Ngoc, A. Gautier, M. Hein (2015): A Flexible Tensor Block Coordinate Ascent Scheme for Hypergraph Matching. CVPR 2015 (oral presentation). PDF (Supplementary material) 
  • A. Khoreva, F. Galasso, M. Hein, B. Schiele (2015): Classifier Based Graph Construction for Video Segmentation. CVPR 2015. PDF (Supplementary material) 
  • S. Bhadra, M. Hein (2015): Correction of Noisy Labels via Mutual Consistency Check. Neurocomputing, 160: 34-52. PDF LND CVScript 
  • M. Slawski, M. Hein (2015): Estimation of positive definite M-matrices and structure learning for attractive Gaussian Markov Random fields. Linear Algebra and its Applications, 473: 145-179 (Special Issue on Statistics). PDF

2014

  • S. Rangapuram, P. K. Mudrakarta, M. Hein (2014): Tight continuous relaxation of the balanced k-cut problem. NeurIPS 2014. PDF (long version) Code
  • L. Jost, S. Setzer, M. Hein (2014): Nonlinear Eigenproblems in Data Analysis - Balanced Graph Cuts and the RatioDCA-Prox. In: S. Dahlke, W. Dahmen, M. Griebel, W. Hackbusch, K. Ritter, R. Schneider, C. Schwab, H. Yserentant (Eds.): Extraction of Quantifiable Information from Complex Systems. Springer. PDF
  • A. Podosinnikova, S. Setzer, M. Hein (2014): Robust PCA: Optimization of the Robust Reconstruction Error on the Stiefel Manifold. GCPR 2014. PDF (Supplementary material) Code 
  • A. Khoreva, F. Galasso, M. Hein, B. Schiele (2014): Learning Must-Link Constraints for Video Segmentation based on Spectral Clustering. GCPR 2014. PDF 
  • M. Slawski, M. Hein (2014): Sparse Recovery for Protein Mass Spectrometry Data. In: I. Rish, G. Cecchi, A. Lozano, A. Niculescu-Mizil (Eds.): Practical Applications of Sparse Modeling. MIT press. PDF
  • U. von Luxburg, A. Radl, M. Hein (2014): Hitting and Commute Times in Large Random Neighborhood Graphs. Journal of Machine Learning Research, 15: 1751-1798. PDF
  • M. Lapin, M. Hein, B. Schiele (2014): Scalable Multitask Representation Learning for Scene Classification. CVPR 2014. PDF  Code
  • M. Lapin, M. Hein, B. Schiele (2014): Learning Using Privileged Information: SVM+ and Weighted SVM. Neural Networks, 53: 95-108. PDF

2013

  • M. Slawski, M. Hein (2013): Non-negative least squares for high-dimensional linear models: consistency and sparse recovery without regularization. Electronic Journal of Statistics, 7(0):3004-3056. PDF
  • M. Hein, S. Setzer, L. Jost, S. Rangapuram (2013): The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited. Spotlight at NeurIPS 2013 (acceptance rate < 5%). PDF (long version)
  • M. Slawski, M. Hein, P. Lutsik (2013): Matrix Factorization with Binary Components. Spotlight at NeurIPS 2013 (acceptance rate < 5%). PDF (Supplementary material)
  • S. Rangapuram, T. Buehler, M. Hein (2013): Towards Realistic Team Formation in Social Networks based on Densest Subgraphs. WWW 2013, 1077-1088. PDF
  • T. Buehler, S. Rangapuram, S. Setzer, M. Hein (2013): Constrained fractional set programs and their application in local clustering and community detection. ICML 2013, 624-632. PDF (long version) Code
  • M. Maier, U. von Luxburg, M. Hein (2013): How the result of graph clustering methods depends on the construction of the graph. ESAIM: Probability and Statistics, 17: 370-418. PDF

2012

  • M. Slawski, R. Hussong, A. Tholey, T. Jakoby, B. Gregorius, A. Hildebrandt, M. Hein (2012): Isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching. BMC Bioinformatics 2012, 13:291 (8 November 2012). PDF
  • C. Backes, A. Rurainski, G.W. Klau, O. Müller, D. Stöckel, A. Gerasch, J. Küntzer, D. Maisel, N. Ludwig, M. Hein, A. Keller, H. Burtscher, M. Kaufmann, E. Meese, H.-P. Lenhof (2012): An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research, 40(6):e43. PDF
  • S. Rangapuram, M. Hein (2012): Constrained 1-Spectral Clustering. AISTATS 2012, JMLR W&CP 22: 1143-1151. PDF (Supplementary material)

2011

  • M. Slawski,M. Hein (2011): Sparse recovery by thresholded non-negative least squares. In: Advances in Neural Information Processing Systems 24 (NeurIPS 2011). 1926--1934. PDF (Supplementary material)
  • M. Hein, S. Setzer (2011): Beyond Spectral Clustering - Tight Relaxations of Balanced Graph Cuts. In: Advances in Neural Information Processing Systems 24 (NeurIPS 2011). 2366--2374. PDF (long version)
  • M. Slawski, M. Hein (2011): Robust sparse recovery with non-negativity constraints. In: Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS). PDF

2010

  • M. Slawski, M. Hein (2010): Sparse Recovery for Protein Mass Spectrometry Data. In: NeurIPS Workshop: Practical Application of Sparse Modeling: Open Issues and New Directions. PDF
  • M. Hein, T. Buehler (2010): An inverse power method for nonlinear eigenproblems with applications in 1-spectral clustering and sparse PCA. In: Advances in Neural Information Processing Systems 23 (NeurIPS 2010). 847-855. PDF (long version) Code (1-spectral clustering),  Code (sparse PCA)
  • U. von Luxburg, A. Radl, M. Hein (2010): Getting lost in space: Large sample analysis of the commute distance. In: Advances in Neural Information Processing Systems 23 (NeurIPS 2010). 2622-2630. PDF (long version)
  • F. Steinke, M. Hein, B. Schoelkopf )2010): Non-parametric regression between general Riemannian manifolds. SIAM Journal on Imaging Sciences, 3:527-563. PDF
  • U. von Luxburg, A. Radl, M. Hein (2010): Hitting times, commute distances and the spectral gap for large random geometric graphs. arXiv:1003.1266v1. PDF
  • M. Slawski, W. zu Castell, G. Tutz (2010): Feature Selection Guided by Structural Information. Annals of Applied Statistics, 4:1056-1080. PDF

2009

  • M. Hein (2009): Robust Nonparametric Regression with Metric-Space valued Output. In: Y. Bengio and D. Schuurmans, J. Lafferty, C. K. I. Williams, A. Culotta (Eds.) Advances in Neural Information Processing Systems 22 (NeurIPS 2009), 718-726, MIT Press, Cambridge, MA, 2010. PDF (long version)
  • K.I. Kim, F. Steinke, M. Hein (2009): Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction. In: Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams and A. Culotta (Eds.): Advances in Neural Information Processing Systems 22 (NeurIPS 2009), 979-987, MIT Press, Cambridge, MA, 2010. PDF (long version)
  • A. Keller, N. Ludwig, S. Heisel, P. Leidinger, C. Andres, W.-I. Steudel, H. Huwer, B. Burgeth, M. Hein, J. Weickert, E. Meese, H.-P. Lenhof (2009): Large-scale antibody profiling of human blood sera: The future of molecular diagnosis. Informatik-Spektrum, 32:332-338. PDF
  • T. Buehler, M. Hein (2009): Spectral Clustering based on the graph p-Laplacian. In: Leon Bottou, Michael Littman (Eds.): Proceedings of the 26th International Conference on Machine Learning (ICML 2009), 81-88, Omnipress. PDF (Supplementary material)  Errata to Supp. Mat.   Code
  • M. Maier, M. Hein, U. von Luxburg (2009): Optimal construction of k-nearest neighbor graphs for identifying noisy clusters. Theoretical Computer Science, 410:1749-1764. PDF

2008

  • F. Steinke, M. Hein (2008): Non-parametric Regression between Manifolds. In: D. Koller, D. Schuurmans, Y. Bengio, L. Bottou (Eds.):  Advances in Neural Information Processing Systems 21 (NeurIPS 2008), 1561 - 1568, MIT Press, Cambridge, MA, 2009. PDF
  • M. Maier, U. von Luxburg, M. Hein (2008): Influence of Graph Construction on Graph-based Clustering Measures. In: D. Koller, D. Schuurmans, Y. Bengio, L. Bottou (Eds.): Advances in Neural Information Processing Systems 21 (NeurIPS 2008), 1025 - 1032, MIT Press, Cambridge, MA, 2009. PDF
    > Markus Maier obtained for this paper the Outstanding Student Paper Award at NeurIPS 2008 <
  • M. Hein, F. Steinke, B. Schoelkopf (2008): Nonparametric regression between manifolds. Oberwolfach Report 30:34-35.
  • P. Didyk, R. Mantiuk, M. Hein, H. P. Seidel (2008): Enhancement of Bright Video Features for HDR Displays. Computer Graphics Forum, 27:1265-1274. (Proceedings of Eurographics Symposium on Rendering 2008).
  • F. Steinke, M. Hein, J. Peters, B. Schoelkopf (2008): Manifold-valued Thin-Plate Splines with Applications in Computer Graphics. Computer Graphics Forum, 27:437-448. PDF (Proceedings of EUROGRAPHICS 2008).
  • M. Hein (2008): Binary Classification under Sample Selection Bias. In: J. Quinonero Candela, M. Sugiyama, A. Schwaighofer, N. D. Lawrence (Eds.): Dataset Shift in Machine Learning. MIT Press. PDF
  • M. Hein, F. Steinke, B. Schoelkopf (2008): Energy functionals for manifold-valued mappings and their properties. Technical Report 167 (January 2008), Max Planck Institute for Biological Cybernetics. PDF

2007

  • M. Hein, J.-Y. Audibert, U. von Luxburg (2007): Graph Laplacians and their convergence on random neighborhood graphs. Journal of Machine Learning Research, 8:1325-1370. PDF
  • M. Maier, M. Hein, U. von Luxburg (2007): Cluster Identification in neighborhood graphs. In: M. Hutter, R. Servedio, E. Takimoto (Eds.): Proceedings of the 18th International Confererence on Algorithmic Learning Theory (ALT 2007), 196 - 210, Springer, New York. PDF
    > Markus Maier obtained the E. M. Gold Award (best student paper) at ALT 2007 for this paper < 
    • Corresponding technical report: 
      M. Maier, M. Hein, U. von Luxburg (2007): Cluster identification in nearest-neighbor graphs. Technical Report 163 (May 2007), Max Planck Institute for Biological Cybernetics. PDF
  • M. Hein, M. Maier (2007): Manifold Denoising for finding natural representations of data. In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI-07), 1646-1649, AAAI Press. PDF

2006

  • M. Hein, M. Maier (2006): Manifold Denoising. In: B. Schoelkopf, J. Platt,  T. Hofmann (Eds.):  Advances in Neural Information Processing Systems 19 (NeurIPS 2006), 561 - 568, MIT Press, Cambridge, MA, 2007. PDF
  • M. Hein (2006(): Uniform convergence of adaptive graph-based regularization. In: G. Lugosi,H. U. Simon (Eds.): Proceedings of the 19th Annual Conference on Learning Theory (COLT 2006), 50-64, Springer, New York. PDF

2005

  • M. Hein, O. Bousquet, B. Schoelkopf (2005): Maximal margin classification for metric spaces. Journal of Computer and System Sciences, 71:333-359. PDF
  • M. Hein, J.-Y. Audibert (2005): Intrinsic dimensionality estimation of submanifolds in Euclidean space. In: L. de Raedt, S. Wrobel (Eds.): Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), 289 - 296, ACM press. PDF
  • M. Hein, J.-Y. Audibert, U. von Luxburg (2005): From graphs to manifolds - weak and strong pointwise consistency of graph Laplacians. In: R. Meir, P. Auer (Eds.): Proceedings of the 18th Conference on Learning Theory (COLT 2005), 470-485, Springer, New York. PDF
    > This paper won a best student paper award at COLT 2005 <
  • M. Hein, O. Bousquet (2005): Hilbertian metrics and positive definite kernels on probability measures. In: Z. Ghahramani, R. Cowell (Eds.): Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics (AISTATS). Society for Artificial Intelligence and Statistics. PDF

2004

  • M. Hein, T. N. Lal, O. Bousquet (2004): Hilbertian metrics on probability measures and their application in SVMs. In: C. E. Rasmussen, H. H. Buelthoff, M. Giese, B. Schoelkopf (Eds.): Proceedings of the 26th DAGM Symposium. 270-277, Springer, Berlin. PDF
    • Corresponding technical report: 
      M. Hein, O. Bousquet (2004): Hilbertian metrics and positive definite kernels on probability measures. Technical Report 126 (July 2004), Max Planck Institute for Biological Cybernetics. PDF
  • M. Hein, O. Bousquet (2004): Kernels, associated structures and generalizations. Technical Report 127 (July 2004), Max Planck Institute for Biological Cybernetics. PDF

2003

  • O. Bousquet, O. Chapelle, M. Hein (2003): Measure based regularization. In: S. Thrun, L. Saul, B. Schoelkopf (Eds.): Advances in Neural Information Processing Systems 16 (NeurIPS 2003). MIT Press, Cambridge, MA, 2004. PDF
  • M. Hein, O. Bousquet (2003): Maximal margin classification for metric spaces. In: B. Schoelkopf, M. K. Warmuth (Eds.): 16th Annual Conference on Learning Theory (COLT 2003). Berlin, Springer. PDF