Nic Schraudolphs's Publications by Topic

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• Ising Models • Gradient Descent • Quasi-Newton Methods • Stochastic Meta-Descent • Preconditioning • Kernel Methods • Reinforcement Learning • Unsupervised Learning • Entropy Optimization • Competitive Learning • Evolutionary Algorithms • Bioinformatics • Computer Vision • Other •

Ising Models

N. N. Schraudolph. Polynomial-Time Exact Inference in NP-Hard Binary MRFs via Reweighted Perfect Matching. In 13^th Intl. Conf. Artificial Intelligence and Statistics (AIstats), pp. 717–724, Journal of Machine Learning Research, Chia Laguna, Italy, 2010.
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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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Gradient Descent

N. N. Schraudolph and T. Graepel. Combining Conjugate Direction Methods with Stochastic Approximation of Gradients. In Proc. 9^th 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. 9^th Intl. Conf. Neural Information Processing (ICONIP), pp. 853–856, IEEE, 2002.
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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.
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Quasi-Newton Methods

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. 12^th Intl. Conf. Artificial Intelligence and Statistics (AIstats), pp. 560–566, Journal of Machine Learning Research, Clearwater Beach, Florida, 2009.
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J. Yu, S. Vishwanathan, S. Günter, and N. N. Schraudolph. A Quasi-Newton Approach to Nonsmooth Convex Optimization. In Proc. 25^th 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. 11^th Intl. Conf. Artificial Intelligence and Statistics (AIstats), pp. 436–443, Journal of Machine Learning Research, San Juan, Puerto Rico, 2007.
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Stochastic Meta-Descent

D. Chik, J. Trumpf, and N. N. Schraudolph. Using an Adaptive VAR Model for Motion Prediction in 3D Hand Tracking. In 8^th Intl. Conf. Automatic Face & Gesture Recognition (FG), IEEE, Amsterdam, Netherlands, 2008.
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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 19^th Intl. Conf. Pattern Recognition (ICPR), Tampa, Florida, 2008.
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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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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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D. Chik, J. Trumpf, and N. N. Schraudolph. 3D Hand Tracking in a Stochastic Approximation Setting. In 2^nd Workshop on Human Motion: Understanding, Modeling, Capture and Animation, 11^th IEEE Intl. Conf. Computer Vision (ICCV), pp. 136–151, Springer Verlag, Berlin, Rio de Janeiro, Brazil, 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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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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S. Vishwanathan, N. N. Schraudolph, M. W. Schmidt, and K. Murphy. Accelerated Training of Conditional Random Fields with Stochastic Gradient Methods. In Proc. 23^rd 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.
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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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A. Karatzoglou, S. Vishwanathan, N. N. Schraudolph, and A. J. Smola. Step Size-Adapted Online Support Vector Learning. In Proc. 8^th Intl. Symp. Signal Processing & Applications, IEEE, 2005.
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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.
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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.
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N. N. Schraudolph. Fast Curvature Matrix-Vector Products for Second-Order Gradient Descent. Neural Computation, 14(7):1723–1738, 2002.
Earlier version
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N. N. Schraudolph. Fast Curvature Matrix-Vector Products. In Proc. Intl. Conf. Artificial Neural Networks (ICANN), pp. 19–26, Springer Verlag, Berlin, Vienna, Austria, 2001.
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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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N. N. Schraudolph. Online Learning with Adaptive Local Step Sizes. In Neural Nets---WIRN Vietri-99: Proc. 11^th Italian Workshop on Neural Networks, pp. 151–156, Springer Verlag, Berlin, Vietri sul Mare, Salerno, Italy, 1999.
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N. N. Schraudolph. Local Gain Adaptation in Stochastic Gradient Descent. In Proc. Intl. Conf. Artificial Neural Networks (ICANN), pp. 569–574, IEE, London, Edinburgh, Scotland, 1999.
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N. N. Schraudolph. Online Local Gain Adaptation for Multi-Layer Perceptrons. Technical Report IDSIA-09-98, Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, 1998.
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Preconditioning

N. N. Schraudolph. Accelerated Gradient Descent by Factor-Centering Decomposition. Technical Report IDSIA-33-98, Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, 1998.
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N. N. Schraudolph. Slope Centering: Making Shortcut Weights Effective. In Proc. Intl. Conf. Artificial Neural Networks (ICANN), pp. 523–528, Springer Verlag, Berlin, Skövde, Sweden, 1998.
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N. N. Schraudolph. Centering Neural Network Gradient Factors. In G. B. Orr and K. Müller, editors, Neural Networks: Tricks of the Trade, Lecture Notes in Computer Science, pp. 207–226, Springer Verlag, Berlin, 1998.
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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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Kernel Methods

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.
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Reinforcement Learning

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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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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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.
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Unsupervised Learning

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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Entropy Optimization

N. N. Schraudolph. Gradient-Based Manipulation of Nonparametric Entropy Estimates. IEEE Transactions on Neural Networks, 15(4):828–837, 2004.
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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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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. Introduction to Optimization of Entropy with Neural Networks. Ph.D. Thesis, University of California, San Diego, 1995.
Full Ph.D. thesis
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N. N. Schraudolph. Optimization of Entropy with Neural Networks. Ph.D. Thesis, University of California, San Diego, 1995.
Introduction only Related papers: Chapter 2 Chapter 3 Chapter 3 Chapter 4
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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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Competitive Learning

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 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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Evolutionary Algorithms

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.
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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.
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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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N. N. Schraudolph. Genetic Algorithm Software Survey. Incorporated as part 5 into the comp.ai.genetic FAQ, The Hitch-Hiker's Guide to Evolutionary Computation, 1993.
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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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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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Bioinformatics

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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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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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: 6^th Intl. Workshop (WABI), pp. 1–11, Springer Verlag, Berlin, Zurich, Switzerland, 2006.
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Computer Vision

Other

N. N. Schraudolph. Facial Attraction: Symmetry Considered Harmful. Journal of Machine Learning Gossip, 2:1–2, 2005.
To be taken with a grain of salt.
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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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N. N. Schraudolph. A Generic Dataflow Programming Environment for Neural Networks. Technical Report IDSIA-15-00, Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, 2000.
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N. N. Schraudolph. A Fast, Compact Approximation of the Exponential Function. Neural Computation, 11(4):853–862, 1999.
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N. N. Schraudolph. Combining Confidence-Tagged Expert Opinions by Alternate Maximization of Likelihood. Technical Report IDSIA-25-98, Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, 1998.
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N. N. Schraudolph. BibTeX Bibliography. For Neural Computation, vol. 1--7, 1997.
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N. N. Schraudolph. BibTeX Bibliography. For John Hertz, Anders Krogh, and Richard Palmer, Introduction to the Theory of Neural Computation, Addison-Wesley, 1991.
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