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• Aberdeen, Douglas • Büche, Dirk • Barral, Yves • Belew, Richard K. • Borgwardt, Karsten • Bray, Matthieu • Cannarozzi, Gina • Chen, Jing • Chik, Desmond • Chong, Adrian • Dayan, Peter • Eldracher, Martin • Faty (joint first authors), Mahamadou • Friberg, Markus T. • Günter, Simon • Gers, Felix A. • Giannakopoulos, Xavier • Gonnet, Gaston H. • Gonnet, Pedro • Graepel, Thore • Grefenstette, John J. • Hansen, Nikolaus • Kamenetsky, Dmitry • Karatzoglou, Alexandros • Klapper-Rybicka, Magdalena • Koller-Meier, Esther • Kondor, Risi • Koumoutsakos, Petros • Kriegel, Hans-Peter • Li, Zhidong • Müller, Pascal • Müller, Sybille • McInerney, John • Murphy, Kevin • Rohr, Peter von • Roth, Alexander C. • Schmidhuber, Jürgen • Schmidt, Mark W. • Sejnowski, Terrence J. • Smola, Alex J. • Sunehag, Peter • Trumpf, Jochen • Van Gool, Luc • Viola, Paul A. • Vishwanathan, S. V. N. • Yu, Jin • Yu, Zhenghua • Zhao, Jieyu •

Aberdeen, Douglas

N. N. Schraudolph, J. Yu, and D. Aberdeen. Fast Online Policy Gradient Learning with SMD Gain Vector Adaptation. In Advances in Neural Information Processing Systems (NIPS), pp. 1185–1192, MIT Press, Cambridge, MA, 2006.
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Büche, Dirk

D. Büche, N. N. Schraudolph, and P. Koumoutsakos. Accelerating Evolutionary Algorithms with Gaussian Process Fitness Function Models. IEEE Transactions on Systems, Man, and Cybernetics, C35(2):183–194, 2005.
Earlier version
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D. Büche, N. N. Schraudolph, and P. Koumoutsakos. Accelerating Evolutionary Algorithms Using Fitness Function Models. In Genetic and Evolutionary Computation Conference Workshop Program, pp. 166–169, AAAI, Chicago, 2003.
Latest version
Details     Download: [pdf] [djvu] [ps.gz] 

Barral, Yves

G. Cannarozzi, N. N. Schraudolph, M. Faty (joint first authors), P. v. Rohr, M. T. Friberg, A. C. Roth, G. H. Gonnet, and Y. Barral. A Role for Codon Order in Translation Dynamics. Cell, 141(2):355–367, 16 Apr 2010.
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M. T. Friberg, P. Gonnet, Y. Barral, N. N. Schraudolph, and G. H. Gonnet. Measures of Codon Bias in Yeast, the tRNA Pairing Index and Possible DNA Repair Mechanisms. In Algorithms in Bioinformatics: 6th Intl. Workshop (WABI), pp. 1–11, Springer Verlag, Berlin, Zurich, Switzerland, 2006.
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Belew, Richard K.

R. K. Belew, J. McInerney, and N. N. Schraudolph. Evolving Networks: Using the Genetic Algorithm with Connectionist Learning. In C. G. Langton, C. Taylor, J. D. Farmer, and S. Rasmussen, editors, Artificial Life II, SFI Studies in the Sciences of Complexity: Proceedings, pp. 511–547, Addison-Wesley, Redwood City, CA, 1992.
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N. N. Schraudolph and R. K. Belew. Dynamic Parameter Encoding for Genetic Algorithms. Machine Learning, 9:9–21, 1992.
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Borgwardt, Karsten

S. Vishwanathan, N. N. Schraudolph, R. Kondor, and K. Borgwardt. Graph Kernels. Journal of Machine Learning Research, 11:1201–1242, 2010.
Short version
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K. M. Borgwardt, H. Kriegel, S. Vishwanathan, and N. N. Schraudolph. Graph Kernels for Disease Outcome Prediction from Protein-Protein Interaction Networks. In Proc. Pacific Symposium on Biocomputing (PSB), pp. 4–15, World Scientific, Maui, Hawaii, 2007.
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S. Vishwanathan, K. Borgwardt, and N. N. Schraudolph. Fast Computation of Graph Kernels. In Advances in Neural Information Processing Systems (NIPS), pp. 1449–1456, MIT Press, Cambridge, MA, 2007.
Long version
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Bray, Matthieu

M. Bray, E. Koller-Meier, N. N. Schraudolph, and L. Van Gool. Fast Stochastic Optimization for Articulated Structure Tracking. Image and Vision Computing, 25(3):352–364, 2007.
Earlier version
Details     Download: [pdf] [djvu] [ps.gz] 

M. Bray, E. Koller-Meier, P. Müller, N. N. Schraudolph, and L. Van Gool. Stochastic Optimization for High-Dimensional Tracking in Dense Range Maps. IEE Proceedings Vision, Image & Signal Processing, 152(4):501–512, 2005.
Earlier version
Details     Download: [pdf] [djvu] [ps.gz] 

M. Bray, E. Koller-Meier, N. N. Schraudolph, and L. Van Gool. Stochastic Meta-Descent for Tracking Articulated Structures. In IEEE Workshop on Articulated and Nonrigid Motion, Conference on Computer Vision and Pattern Recognition (CVPR), Washington, D.C., 2004.
Latest version
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M. Bray, E. Koller-Meier, P. Müller, L. Van Gool, and N. N. Schraudolph. 3D Hand Tracking by Rapid Stochastic Gradient Descent Using a Skinning Model. In First European Conference on Visual Media Production (CVMP), pp. 59–68, London, 2004.
Latest version
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Cannarozzi, Gina

G. Cannarozzi, N. N. Schraudolph, M. Faty (joint first authors), P. v. Rohr, M. T. Friberg, A. C. Roth, G. H. Gonnet, and Y. Barral. A Role for Codon Order in Translation Dynamics. Cell, 141(2):355–367, 16 Apr 2010.
Details     Download: (unavailable)

Chen, Jing

Z. Li, J. Chen, and N. N. Schraudolph. An Improved Mean-Shift Tracker with Kernel Prediction and Scale Optimisation Targeting for Low-Frame-Rate Video Tracking. In 19th Intl. Conf. Pattern Recognition (ICPR), Tampa, Florida, 2008.
Details     Download: [pdf] [djvu] [ps.gz] 

Z. Li, J. Chen, A. Chong, Z. Yu, and N. N. Schraudolph. Using Stochastic Gradient-Descent Scheme in Appearance Model Based Face Tracking. In Proc. Intl. Workshop Multimedia Signal Processing (MMSP), IEEE, Cairns, Australia, 2008.
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Chik, Desmond

D. Chik, J. Trumpf, and N. N. Schraudolph. Using an Adaptive VAR Model for Motion Prediction in 3D Hand Tracking. In 8th Intl. Conf. Automatic Face & Gesture Recognition (FG), IEEE, Amsterdam, Netherlands, 2008.
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D. Chik, J. Trumpf, and N. N. Schraudolph. 3D Hand Tracking in a Stochastic Approximation Setting. In 2nd Workshop on Human Motion: Understanding, Modeling, Capture and Animation, 11th IEEE Intl. Conf. Computer Vision (ICCV), pp. 136–151, Springer Verlag, Berlin, Rio de Janeiro, Brazil, 2007.
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Chong, Adrian

Z. Li, J. Chen, A. Chong, Z. Yu, and N. N. Schraudolph. Using Stochastic Gradient-Descent Scheme in Appearance Model Based Face Tracking. In Proc. Intl. Workshop Multimedia Signal Processing (MMSP), IEEE, Cairns, Australia, 2008.
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Dayan, Peter

N. N. Schraudolph, P. Dayan, and T. J. Sejnowski. Learning to Evaluate Go Positions via Temporal Difference Methods. In N. Baba and L. C. Jain, editors, Computational Intelligence in Games, Studies in Fuzziness and Soft Computing, pp. 77–98, Springer Verlag, Berlin, 2001.
Earlier version
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N. N. Schraudolph, P. Dayan, and T. J. Sejnowski. Temporal Difference Learning of Position Evaluation in the Game of Go. In Advances in Neural Information Processing Systems (NIPS), pp. 817–824, Morgan Kaufmann, San Francisco, CA, 1994.
Latest version
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Eldracher, Martin

N. N. Schraudolph, M. Eldracher, and J. Schmidhuber. Processing Images by Semi-Linear Predictability Minimization. Network: Computation in Neural Systems, 10(2):133–169, 1999.
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Faty (joint first authors), Mahamadou

G. Cannarozzi, N. N. Schraudolph, M. Faty (joint first authors), P. v. Rohr, M. T. Friberg, A. C. Roth, G. H. Gonnet, and Y. Barral. A Role for Codon Order in Translation Dynamics. Cell, 141(2):355–367, 16 Apr 2010.
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Friberg, Markus T.

G. Cannarozzi, N. N. Schraudolph, M. Faty (joint first authors), P. v. Rohr, M. T. Friberg, A. C. Roth, G. H. Gonnet, and Y. Barral. A Role for Codon Order in Translation Dynamics. Cell, 141(2):355–367, 16 Apr 2010.
Details     Download: (unavailable)

M. T. Friberg, P. Gonnet, Y. Barral, N. N. Schraudolph, and G. H. Gonnet. Measures of Codon Bias in Yeast, the tRNA Pairing Index and Possible DNA Repair Mechanisms. In Algorithms in Bioinformatics: 6th Intl. Workshop (WABI), pp. 1–11, Springer Verlag, Berlin, Zurich, Switzerland, 2006.
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Günter, Simon

J. Yu, S. Vishwanathan, S. Günter, and N. N. Schraudolph. A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning. Journal of Machine Learning Research, 11:1145–1200, 2010.
Short version
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J. Yu, S. Vishwanathan, S. Günter, and N. N. Schraudolph. A Quasi-Newton Approach to Nonsmooth Convex Optimization. In Proc. 25th Intl. Conf. Machine Learning (ICML), pp. 1216–1223, Omnipress, Helsinki, Finland, 2008.
Long version
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S. Günter, N. N. Schraudolph, and S. Vishwanathan. Fast Iterative Kernel Principal Component Analysis. Journal of Machine Learning Research, 8:1893–1918, 2007.
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N. N. Schraudolph, S. Günter, and S. Vishwanathan. Fast Iterative Kernel PCA. In Advances in Neural Information Processing Systems (NIPS), pp. 1225–1232, MIT Press, Cambridge, MA, 2007.
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N. N. Schraudolph, J. Yu, and S. Günter. A Stochastic Quasi-Newton Method for Online Convex Optimization. In Proc. 11th Intl. Conf. Artificial Intelligence and Statistics (AIstats), pp. 436–443, Journal of Machine Learning Research, San Juan, Puerto Rico, 2007.
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Gers, Felix A.

F. A. Gers, N. N. Schraudolph, and J. Schmidhuber. Learning Precise Timing with LSTM Recurrent Networks. Journal of Machine Learning Research, 3:115–143, 2002.
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Giannakopoulos, Xavier

N. N. Schraudolph and X. Giannakopoulos. Online Independent Component Analysis With Local Learning Rate Adaptation. In Advances in Neural Information Processing Systems (NIPS), pp. 789–795, The MIT Press, Cambridge, MA, 2000.
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Gonnet, Gaston H.

G. Cannarozzi, N. N. Schraudolph, M. Faty (joint first authors), P. v. Rohr, M. T. Friberg, A. C. Roth, G. H. Gonnet, and Y. Barral. A Role for Codon Order in Translation Dynamics. Cell, 141(2):355–367, 16 Apr 2010.
Details     Download: (unavailable)

M. T. Friberg, P. Gonnet, Y. Barral, N. N. Schraudolph, and G. H. Gonnet. Measures of Codon Bias in Yeast, the tRNA Pairing Index and Possible DNA Repair Mechanisms. In Algorithms in Bioinformatics: 6th Intl. Workshop (WABI), pp. 1–11, Springer Verlag, Berlin, Zurich, Switzerland, 2006.
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Gonnet, Pedro

M. T. Friberg, P. Gonnet, Y. Barral, N. N. Schraudolph, and G. H. Gonnet. Measures of Codon Bias in Yeast, the tRNA Pairing Index and Possible DNA Repair Mechanisms. In Algorithms in Bioinformatics: 6th Intl. Workshop (WABI), pp. 1–11, Springer Verlag, Berlin, Zurich, Switzerland, 2006.
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Graepel, Thore

N. N. Schraudolph and T. Graepel. Combining Conjugate Direction Methods with Stochastic Approximation of Gradients. In Proc. 9th Intl. Workshop Artificial Intelligence and Statistics (AIstats), pp. 7–13, Society for Artificial Intelligence and Statistics, Key West, Florida, 2003.
Earlier version     Related paper
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T. Graepel and N. N. Schraudolph. Stable Adaptive Momentum for Rapid Online Learning in Nonlinear Systems. In Proc. Intl. Conf. Artificial Neural Networks (ICANN), pp. 450–455, Springer Verlag, Berlin, Madrid, Spain, 2002.
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N. N. Schraudolph and T. Graepel. Towards Stochastic Conjugate Gradient Methods. In Proc. 9th Intl. Conf. Neural Information Processing (ICONIP), pp. 853–856, IEEE, 2002.
Related paper
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N. N. Schraudolph and T. Graepel. Conjugate Directions for Stochastic Gradient Descent. In Proc. Intl. Conf. Artificial Neural Networks (ICANN), pp. 1351–1356, Springer Verlag, Berlin, Madrid, Spain, 2002.
Latest version     Related paper
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Grefenstette, John J.

N. N. Schraudolph and J. J. Grefenstette. A User's Guide to GAucsd 1.4. Technical Report CS92-249, University of California, San Diego, 1992.
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Hansen, Nikolaus

S. Müller, N. N. Schraudolph, P. Koumoutsakos, and N. Hansen. Step Size Adaptation in Evolution Strategies---Two Approaches. In Genetic and Evolutionary Computation Conference Workshop Program, pp. 161–164, AAAI, New York, 2002.
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Kamenetsky, Dmitry

N. N. Schraudolph and D. Kamenetsky. Efficient Exact Inference in Planar Ising Models. In Advances in Neural Information Processing Systems (NIPS), pp. 1417–1424, Curran Associates, Inc., 2009.
Long version
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N. N. Schraudolph and D. Kamenetsky. Efficient Exact Inference in Planar Ising Models. Technical Report 0810.4401, arXiv, 2008.
Short version
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Karatzoglou, Alexandros

A. Karatzoglou, S. Vishwanathan, N. N. Schraudolph, and A. J. Smola. Step Size-Adapted Online Support Vector Learning. In Proc. 8th Intl. Symp. Signal Processing & Applications, IEEE, 2005.
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Klapper-Rybicka, Magdalena

M. Klapper-Rybicka, N. N. Schraudolph, and J. Schmidhuber. Unsupervised Learning in Recurrent Neural Networks. In Proc. Intl. Conf. Artificial Neural Networks (ICANN), pp. 674–681, Springer Verlag, Berlin, Vienna, Austria, 2001.
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Koller-Meier, Esther

M. Bray, E. Koller-Meier, N. N. Schraudolph, and L. Van Gool. Fast Stochastic Optimization for Articulated Structure Tracking. Image and Vision Computing, 25(3):352–364, 2007.
Earlier version
Details     Download: [pdf] [djvu] [ps.gz] 

M. Bray, E. Koller-Meier, P. Müller, N. N. Schraudolph, and L. Van Gool. Stochastic Optimization for High-Dimensional Tracking in Dense Range Maps. IEE Proceedings Vision, Image & Signal Processing, 152(4):501–512, 2005.
Earlier version
Details     Download: [pdf] [djvu] [ps.gz] 

M. Bray, E. Koller-Meier, N. N. Schraudolph, and L. Van Gool. Stochastic Meta-Descent for Tracking Articulated Structures. In IEEE Workshop on Articulated and Nonrigid Motion, Conference on Computer Vision and Pattern Recognition (CVPR), Washington, D.C., 2004.
Latest version
Details     Download: [pdf] [djvu] [ps.gz] 

M. Bray, E. Koller-Meier, P. Müller, L. Van Gool, and N. N. Schraudolph. 3D Hand Tracking by Rapid Stochastic Gradient Descent Using a Skinning Model. In First European Conference on Visual Media Production (CVMP), pp. 59–68, London, 2004.
Latest version
Details     Download: [pdf] [djvu] [ps.gz] 

Kondor, Risi

S. Vishwanathan, N. N. Schraudolph, R. Kondor, and K. Borgwardt. Graph Kernels. Journal of Machine Learning Research, 11:1201–1242, 2010.
Short version
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Koumoutsakos, Petros

D. Büche, N. N. Schraudolph, and P. Koumoutsakos. Accelerating Evolutionary Algorithms with Gaussian Process Fitness Function Models. IEEE Transactions on Systems, Man, and Cybernetics, C35(2):183–194, 2005.
Earlier version
Details     Download: [pdf] [djvu] [ps.gz] 

D. Büche, N. N. Schraudolph, and P. Koumoutsakos. Accelerating Evolutionary Algorithms Using Fitness Function Models. In Genetic and Evolutionary Computation Conference Workshop Program, pp. 166–169, AAAI, Chicago, 2003.
Latest version
Details     Download: [pdf] [djvu] [ps.gz] 

S. Müller, N. N. Schraudolph, and P. Koumoutsakos. Evolutionary and Gradient-Based Algorithms for Lennard-Jones Cluster Optimization. In Genetic and Evolutionary Computation Conference Workshop Program, pp. 160–165, AAAI, Chicago, 2003.
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S. Müller, N. N. Schraudolph, P. Koumoutsakos, and N. Hansen. Step Size Adaptation in Evolution Strategies---Two Approaches. In Genetic and Evolutionary Computation Conference Workshop Program, pp. 161–164, AAAI, New York, 2002.
Details     Download: [pdf] [djvu] [ps.gz] 

S. Müller, N. N. Schraudolph, and P. D. Koumoutsakos. Step Size Adaptation in Evolution Strategies using Reinforcement Learning. In Proc. Congress on Evolutionary Computation, pp. 151–156, IEEE, 2002.
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Kriegel, Hans-Peter

K. M. Borgwardt, H. Kriegel, S. Vishwanathan, and N. N. Schraudolph. Graph Kernels for Disease Outcome Prediction from Protein-Protein Interaction Networks. In Proc. Pacific Symposium on Biocomputing (PSB), pp. 4–15, World Scientific, Maui, Hawaii, 2007.
Details     Download: [pdf] [djvu] [ps.gz] 

Li, Zhidong

Z. Li, J. Chen, and N. N. Schraudolph. An Improved Mean-Shift Tracker with Kernel Prediction and Scale Optimisation Targeting for Low-Frame-Rate Video Tracking. In 19th Intl. Conf. Pattern Recognition (ICPR), Tampa, Florida, 2008.
Details     Download: [pdf] [djvu] [ps.gz] 

Z. Li, J. Chen, A. Chong, Z. Yu, and N. N. Schraudolph. Using Stochastic Gradient-Descent Scheme in Appearance Model Based Face Tracking. In Proc. Intl. Workshop Multimedia Signal Processing (MMSP), IEEE, Cairns, Australia, 2008.
Details     Download: [pdf] [djvu] [ps.gz] 

Müller, Pascal

M. Bray, E. Koller-Meier, P. Müller, N. N. Schraudolph, and L. Van Gool. Stochastic Optimization for High-Dimensional Tracking in Dense Range Maps. IEE Proceedings Vision, Image & Signal Processing, 152(4):501–512, 2005.
Earlier version
Details     Download: [pdf] [djvu] [ps.gz] 

M. Bray, E. Koller-Meier, P. Müller, L. Van Gool, and N. N. Schraudolph. 3D Hand Tracking by Rapid Stochastic Gradient Descent Using a Skinning Model. In First European Conference on Visual Media Production (CVMP), pp. 59–68, London, 2004.
Latest version
Details     Download: [pdf] [djvu] [ps.gz] 

Müller, Sybille

S. Müller, N. N. Schraudolph, and P. Koumoutsakos. Evolutionary and Gradient-Based Algorithms for Lennard-Jones Cluster Optimization. In Genetic and Evolutionary Computation Conference Workshop Program, pp. 160–165, AAAI, Chicago, 2003.
Details     Download: [pdf] [djvu] [ps.gz] 

S. Müller, N. N. Schraudolph, P. Koumoutsakos, and N. Hansen. Step Size Adaptation in Evolution Strategies---Two Approaches. In Genetic and Evolutionary Computation Conference Workshop Program, pp. 161–164, AAAI, New York, 2002.
Details     Download: [pdf] [djvu] [ps.gz] 

S. Müller, N. N. Schraudolph, and P. D. Koumoutsakos. Step Size Adaptation in Evolution Strategies using Reinforcement Learning. In Proc. Congress on Evolutionary Computation, pp. 151–156, IEEE, 2002.
Details     Download: [pdf] [djvu] [ps.gz] 

McInerney, John

R. K. Belew, J. McInerney, and N. N. Schraudolph. Evolving Networks: Using the Genetic Algorithm with Connectionist Learning. In C. G. Langton, C. Taylor, J. D. Farmer, and S. Rasmussen, editors, Artificial Life II, SFI Studies in the Sciences of Complexity: Proceedings, pp. 511–547, Addison-Wesley, Redwood City, CA, 1992.
Details     Download: [pdf] [djvu] [ps.gz] 

Murphy, Kevin

S. Vishwanathan, N. N. Schraudolph, M. W. Schmidt, and K. Murphy. Accelerated Training of Conditional Random Fields with Stochastic Gradient Methods. In Proc. 23rd Intl. Conf. Machine Learning (ICML), pp. 969–976, ACM Press, 2006.
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Rohr, Peter von

G. Cannarozzi, N. N. Schraudolph, M. Faty (joint first authors), P. v. Rohr, M. T. Friberg, A. C. Roth, G. H. Gonnet, and Y. Barral. A Role for Codon Order in Translation Dynamics. Cell, 141(2):355–367, 16 Apr 2010.
Details     Download: (unavailable)

Roth, Alexander C.

G. Cannarozzi, N. N. Schraudolph, M. Faty (joint first authors), P. v. Rohr, M. T. Friberg, A. C. Roth, G. H. Gonnet, and Y. Barral. A Role for Codon Order in Translation Dynamics. Cell, 141(2):355–367, 16 Apr 2010.
Details     Download: (unavailable)

Schmidhuber, Jürgen

F. A. Gers, N. N. Schraudolph, and J. Schmidhuber. Learning Precise Timing with LSTM Recurrent Networks. Journal of Machine Learning Research, 3:115–143, 2002.
Details     Download: [pdf] [djvu] [ps.gz] 

M. Klapper-Rybicka, N. N. Schraudolph, and J. Schmidhuber. Unsupervised Learning in Recurrent Neural Networks. In Proc. Intl. Conf. Artificial Neural Networks (ICANN), pp. 674–681, Springer Verlag, Berlin, Vienna, Austria, 2001.
Details     Download: [pdf] [djvu] [ps.gz] 

N. N. Schraudolph, M. Eldracher, and J. Schmidhuber. Processing Images by Semi-Linear Predictability Minimization. Network: Computation in Neural Systems, 10(2):133–169, 1999.
Details     Download: [djvu] [ps.gz] 

J. Schmidhuber, J. Zhao, and N. N. Schraudolph. Reinforcement Learning with Self-Modifying Policies. In S. Thrun and L. Pratt, editors, Learning to Learn, pp. 293–309, Kluwer Academic Publishers, Norwell, MA, 1998.
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Schmidt, Mark W.

S. Vishwanathan, N. N. Schraudolph, M. W. Schmidt, and K. Murphy. Accelerated Training of Conditional Random Fields with Stochastic Gradient Methods. In Proc. 23rd Intl. Conf. Machine Learning (ICML), pp. 969–976, ACM Press, 2006.
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Sejnowski, Terrence J.

N. N. Schraudolph, P. Dayan, and T. J. Sejnowski. Learning to Evaluate Go Positions via Temporal Difference Methods. In N. Baba and L. C. Jain, editors, Computational Intelligence in Games, Studies in Fuzziness and Soft Computing, pp. 77–98, Springer Verlag, Berlin, 2001.
Earlier version
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N. N. Schraudolph and T. J. Sejnowski. Tempering Backpropagation Networks: Not All Weights Are Created Equal. In Advances in Neural Information Processing Systems (NIPS), pp. 563–569, The MIT Press, Cambridge, MA, 1996.
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P. A. Viola, N. N. Schraudolph, and T. J. Sejnowski. Empirical Entropy Manipulation for Real-World Problems. In Advances in Neural Information Processing Systems (NIPS), pp. 851–857, The MIT Press, Cambridge, MA, 1996.
In Ph.D. thesis     Latest version
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N. N. Schraudolph and T. J. Sejnowski. Plasticity-Mediated Competitive Learning. In Advances in Neural Information Processing Systems (NIPS), pp. 475–480, The MIT Press, Cambridge, MA, 1995.
In Ph.D. thesis
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N. N. Schraudolph, P. Dayan, and T. J. Sejnowski. Temporal Difference Learning of Position Evaluation in the Game of Go. In Advances in Neural Information Processing Systems (NIPS), pp. 817–824, Morgan Kaufmann, San Francisco, CA, 1994.
Latest version
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N. N. Schraudolph and T. J. Sejnowski. Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain. In Advances in Neural Information Processing Systems (NIPS), pp. 499–506, Morgan Kaufmann, San Mateo, CA, 1993.
In Ph.D. thesis
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N. N. Schraudolph and T. J. Sejnowski. Competitive Anti-Hebbian Learning of Invariants. In Advances in Neural Information Processing Systems (NIPS), pp. 1017–1024, Morgan Kaufmann, San Mateo, CA, 1992.
In Ph.D. thesis
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Smola, Alex J.

S. Vishwanathan, N. N. Schraudolph, and A. J. Smola. Step Size Adaptation in Reproducing Kernel Hilbert Space. Journal of Machine Learning Research, 7:1107–1133, 2006.
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A. Karatzoglou, S. Vishwanathan, N. N. Schraudolph, and A. J. Smola. Step Size-Adapted Online Support Vector Learning. In Proc. 8th Intl. Symp. Signal Processing & Applications, IEEE, 2005.
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Sunehag, Peter

P. Sunehag, J. Trumpf, S. Vishwanathan, and N. N. Schraudolph. Variable Metric Stochastic Approximation Theory. In Proc. 12th Intl. Conf. Artificial Intelligence and Statistics (AIstats), pp. 560–566, Journal of Machine Learning Research, Clearwater Beach, Florida, 2009.
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Trumpf, Jochen

P. Sunehag, J. Trumpf, S. Vishwanathan, and N. N. Schraudolph. Variable Metric Stochastic Approximation Theory. In Proc. 12th Intl. Conf. Artificial Intelligence and Statistics (AIstats), pp. 560–566, Journal of Machine Learning Research, Clearwater Beach, Florida, 2009.
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D. Chik, J. Trumpf, and N. N. Schraudolph. Using an Adaptive VAR Model for Motion Prediction in 3D Hand Tracking. In 8th Intl. Conf. Automatic Face & Gesture Recognition (FG), IEEE, Amsterdam, Netherlands, 2008.
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D. Chik, J. Trumpf, and N. N. Schraudolph. 3D Hand Tracking in a Stochastic Approximation Setting. In 2nd Workshop on Human Motion: Understanding, Modeling, Capture and Animation, 11th IEEE Intl. Conf. Computer Vision (ICCV), pp. 136–151, Springer Verlag, Berlin, Rio de Janeiro, Brazil, 2007.
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Van Gool, Luc

M. Bray, E. Koller-Meier, N. N. Schraudolph, and L. Van Gool. Fast Stochastic Optimization for Articulated Structure Tracking. Image and Vision Computing, 25(3):352–364, 2007.
Earlier version
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M. Bray, E. Koller-Meier, P. Müller, N. N. Schraudolph, and L. Van Gool. Stochastic Optimization for High-Dimensional Tracking in Dense Range Maps. IEE Proceedings Vision, Image & Signal Processing, 152(4):501–512, 2005.
Earlier version
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M. Bray, E. Koller-Meier, N. N. Schraudolph, and L. Van Gool. Stochastic Meta-Descent for Tracking Articulated Structures. In IEEE Workshop on Articulated and Nonrigid Motion, Conference on Computer Vision and Pattern Recognition (CVPR), Washington, D.C., 2004.
Latest version
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M. Bray, E. Koller-Meier, P. Müller, L. Van Gool, and N. N. Schraudolph. 3D Hand Tracking by Rapid Stochastic Gradient Descent Using a Skinning Model. In First European Conference on Visual Media Production (CVMP), pp. 59–68, London, 2004.
Latest version
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Viola, Paul A.

P. A. Viola, N. N. Schraudolph, and T. J. Sejnowski. Empirical Entropy Manipulation for Real-World Problems. In Advances in Neural Information Processing Systems (NIPS), pp. 851–857, The MIT Press, Cambridge, MA, 1996.
In Ph.D. thesis     Latest version
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Vishwanathan, S. V. N.

S. Vishwanathan, N. N. Schraudolph, R. Kondor, and K. Borgwardt. Graph Kernels. Journal of Machine Learning Research, 11:1201–1242, 2010.
Short version
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J. Yu, S. Vishwanathan, S. Günter, and N. N. Schraudolph. A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning. Journal of Machine Learning Research, 11:1145–1200, 2010.
Short version
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P. Sunehag, J. Trumpf, S. Vishwanathan, and N. N. Schraudolph. Variable Metric Stochastic Approximation Theory. In Proc. 12th Intl. Conf. Artificial Intelligence and Statistics (AIstats), pp. 560–566, Journal of Machine Learning Research, Clearwater Beach, Florida, 2009.
Details     Download: [pdf] [djvu] [ps.gz] 

J. Yu, S. Vishwanathan, S. Günter, and N. N. Schraudolph. A Quasi-Newton Approach to Nonsmooth Convex Optimization. In Proc. 25th Intl. Conf. Machine Learning (ICML), pp. 1216–1223, Omnipress, Helsinki, Finland, 2008.
Long version
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K. M. Borgwardt, H. Kriegel, S. Vishwanathan, and N. N. Schraudolph. Graph Kernels for Disease Outcome Prediction from Protein-Protein Interaction Networks. In Proc. Pacific Symposium on Biocomputing (PSB), pp. 4–15, World Scientific, Maui, Hawaii, 2007.
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S. Günter, N. N. Schraudolph, and S. Vishwanathan. Fast Iterative Kernel Principal Component Analysis. Journal of Machine Learning Research, 8:1893–1918, 2007.
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N. N. Schraudolph, S. Günter, and S. Vishwanathan. Fast Iterative Kernel PCA. In Advances in Neural Information Processing Systems (NIPS), pp. 1225–1232, MIT Press, Cambridge, MA, 2007.
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S. Vishwanathan, K. Borgwardt, and N. N. Schraudolph. Fast Computation of Graph Kernels. In Advances in Neural Information Processing Systems (NIPS), pp. 1449–1456, MIT Press, Cambridge, MA, 2007.
Long version
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S. Vishwanathan, N. N. Schraudolph, M. W. Schmidt, and K. Murphy. Accelerated Training of Conditional Random Fields with Stochastic Gradient Methods. In Proc. 23rd Intl. Conf. Machine Learning (ICML), pp. 969–976, ACM Press, 2006.
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S. Vishwanathan, N. N. Schraudolph, and A. J. Smola. Step Size Adaptation in Reproducing Kernel Hilbert Space. Journal of Machine Learning Research, 7:1107–1133, 2006.
Details     Download: [pdf] [djvu] [ps.gz] 

A. Karatzoglou, S. Vishwanathan, N. N. Schraudolph, and A. J. Smola. Step Size-Adapted Online Support Vector Learning. In Proc. 8th Intl. Symp. Signal Processing & Applications, IEEE, 2005.
Details     Download: [pdf] [djvu] [ps.gz] 

Yu, Jin

J. Yu, S. Vishwanathan, S. Günter, and N. N. Schraudolph. A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning. Journal of Machine Learning Research, 11:1145–1200, 2010.
Short version
Details     Download: [pdf] [djvu] [ps.gz] 

J. Yu, S. Vishwanathan, S. Günter, and N. N. Schraudolph. A Quasi-Newton Approach to Nonsmooth Convex Optimization. In Proc. 25th Intl. Conf. Machine Learning (ICML), pp. 1216–1223, Omnipress, Helsinki, Finland, 2008.
Long version
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N. N. Schraudolph, J. Yu, and S. Günter. A Stochastic Quasi-Newton Method for Online Convex Optimization. In Proc. 11th Intl. Conf. Artificial Intelligence and Statistics (AIstats), pp. 436–443, Journal of Machine Learning Research, San Juan, Puerto Rico, 2007.
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N. N. Schraudolph, J. Yu, and D. Aberdeen. Fast Online Policy Gradient Learning with SMD Gain Vector Adaptation. In Advances in Neural Information Processing Systems (NIPS), pp. 1185–1192, MIT Press, Cambridge, MA, 2006.
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Yu, Zhenghua

Z. Li, J. Chen, A. Chong, Z. Yu, and N. N. Schraudolph. Using Stochastic Gradient-Descent Scheme in Appearance Model Based Face Tracking. In Proc. Intl. Workshop Multimedia Signal Processing (MMSP), IEEE, Cairns, Australia, 2008.
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Zhao, Jieyu

J. Schmidhuber, J. Zhao, and N. N. Schraudolph. Reinforcement Learning with Self-Modifying Policies. In S. Thrun and L. Pratt, editors, Learning to Learn, pp. 293–309, Kluwer Academic Publishers, Norwell, MA, 1998.
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