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2020 – today
- 2022
[j16]Tobias Glasmachers, Oswin Krause:
Convergence Analysis of the Hessian Estimation Evolution Strategy. Evol. Comput. 30(1): 27-50 (2022)- [j15]Hlynur Davíð Hlynsson, Merlin Schüler, Robin Schiewer, Tobias Glasmachers, Laurenz Wiskott:
Latent Representation Prediction Networks. Int. J. Pattern Recognit. Artif. Intell. 36(1): 2251002:1-2251002:30 (2022) - [j14]Youhei Akimoto
, Anne Auger, Tobias Glasmachers, Daiki Morinaga:
Global Linear Convergence of Evolution Strategies on More than Smooth Strongly Convex Functions. SIAM J. Optim. 32(2): 1402-1429 (2022) - [c43]Giuseppe Cuccu, Luca Rolshoven, Fabien Vorpe, Philippe Cudré-Mauroux, Tobias Glasmachers:
DiBB: distributing black-box optimization. GECCO 2022: 341-349 - [c42]Tobias Glasmachers:
The (1+1)-ES Reliably Overcomes Saddle Points. PPSN (2) 2022: 309-319 - [i32]Marie Dominique Schmidt, Tobias Glasmachers, Ioannis Iossifidis:
From Motion to Muscle. CoRR abs/2201.11501 (2022) - [i31]Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes:
ConTraNet: A single end-to-end hybrid network for EEG-based and EMG-based human machine interfaces. CoRR abs/2206.10677 (2022) - [i30]Tobias Glasmachers:
Recipe for Fast Large-scale SVM Training: Polishing, Parallelism, and more RAM! CoRR abs/2207.01016 (2022) - 2021
- [c41]Nils Müller, Tobias Glasmachers:
Non-local optimization: imposing structure on optimization problems by relaxation. FOGA 2021: 6:1-6:10 - [i29]Tobias Glasmachers:
The (1+1)-ES Reliably Overcomes Saddle Points. CoRR abs/2112.00888 (2021) - [i28]Stephan Johann Lehmler, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis:
Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification. CoRR abs/2112.15019 (2021) - 2020
- [j13]Tobias Glasmachers:
Global Convergence of the (1 + 1) Evolution Strategy to a Critical Point. Evol. Comput. 28(1): 27-53 (2020) - [c40]Declan Oller, Tobias Glasmachers, Giuseppe Cuccu:
Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing. AAMAS 2020: 975-982 - [c39]Mohamed Nafzi, Michael Brauckmann, Tobias Glasmachers:
Methods of the Vehicle Re-identification. IntelliSys (1) 2020: 516-527 - [c38]Tobias Glasmachers, Oswin Krause:
The Hessian Estimation Evolution Strategy. PPSN (1) 2020: 597-609 - [c37]Mohamed Nafzi, Michael Brauckmann, Tobias Glasmachers:
Data Augmentation and Clustering for Vehicle Make/Model Classification. SAI (1) 2020: 334-346 - [i27]Tobias Glasmachers, Oswin Krause:
The Hessian Estimation Evolution Strategy. CoRR abs/2003.13256 (2020) - [i26]Declan Oller, Tobias Glasmachers, Giuseppe Cuccu:
Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing. CoRR abs/2004.07707 (2020) - [i25]Tobias Glasmachers, Oswin Krause:
Convergence Analysis of the Hessian Estimation Evolution Strategy. CoRR abs/2009.02732 (2020) - [i24]Mohamed Nafzi, Michael Brauckmann, Tobias Glasmachers:
Data Augmentation and Clustering for Vehicle Make/Model Classification. CoRR abs/2009.06679 (2020) - [i23]Mohamed Nafzi, Michael Brauckmann, Tobias Glasmachers:
Methods of the Vehicle Re-identification. CoRR abs/2009.06687 (2020) - [i22]Youhei Akimoto, Anne Auger, Tobias Glasmachers, Daiki Morinaga:
Global Linear Convergence of Evolution Strategies on More Than Smooth Strongly Convex Functions. CoRR abs/2009.08647 (2020) - [i21]Hlynur Davíð Hlynsson, Merlin Schüler, Robin Schiewer, Tobias Glasmachers, Laurenz Wiskott:
Latent Representation Prediction Networks. CoRR abs/2009.09439 (2020) - [i20]Nils Müller, Tobias Glasmachers:
Non-local Optimization: Imposing Structure on Optimization Problems by Relaxation. CoRR abs/2011.06064 (2020) - [i19]Omair Ali, Muhammad Saif-ur-Rehman, Susanne Dyck, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes:
Improving the performance of EEG decoding using anchored-STFT in conjunction with gradient norm adversarial augmentation. CoRR abs/2011.14694 (2020)
2010 – 2019
- 2019
- [j12]Ilya Loshchilov, Tobias Glasmachers, Hans-Georg Beyer:
Large Scale Black-Box Optimization by Limited-Memory Matrix Adaptation. IEEE Trans. Evol. Comput. 23(2): 353-358 (2019) - [c36]Tobias Glasmachers:
Challenges of convex quadratic bi-objective benchmark problems. GECCO 2019: 559-567 - [c35]Simon Hakenes, Tobias Glasmachers:
Boosting Reinforcement Learning with Unsupervised Feature Extraction. ICANN (1) 2019: 555-566 - [c34]Sahar Qaadan, Abhijeet Pendyala, Merlin Schüler, Tobias Glasmachers:
Online Budgeted Stochastic Coordinate Ascent for Large-Scale Kernelized Dual Support Vector Machine Training. ICPRAM (Revised Selected Papers) 2019: 23-47 - [c33]Sahar Qaadan, Merlin Schüler, Tobias Glasmachers:
Dual SVM Training on a Budget. ICPRAM 2019: 94-106 - [c32]Haneen Altartouri, Tobias Glasmachers:
Moment Vector Encoding of Protein Sequences for Supervised Classification. PACBB 2019: 25-35 - [i18]Mohamed Nafzi, Michael Brauckmann, Tobias Glasmachers:
Vehicle Shape and Color Classification Using Convolutional Neural Network. CoRR abs/1905.08612 (2019) - 2018
- [j11]Daniel Horn, Aydin Demircioglu, Bernd Bischl, Tobias Glasmachers, Claus Weihs:
A comparative study on large scale kernelized support vector machines. Adv. Data Anal. Classif. 12(4): 867-883 (2018) - [c31]Youhei Akimoto, Anne Auger, Tobias Glasmachers:
Drift theory in continuous search spaces: expected hitting time of the (1 + 1)-ES with 1/5 success rule. GECCO 2018: 801-808 - [c30]Hasham Shahid Qureshi, Tobias Glasmachers, Rebecca Wiczorek:
User-Centered Development of a Pedestrian Assistance System Using End-to-End Learning. ICMLA 2018: 808-813 - [c29]Tobias Glasmachers, Sahar Qaadan:
Speeding Up Budgeted Stochastic Gradient Descent SVM Training with Precomputed Golden Section Search. LOD 2018: 329-340 - [c28]Nils Müller, Tobias Glasmachers:
Challenges in High-Dimensional Reinforcement Learning with Evolution Strategies. PPSN (2) 2018: 411-423 - [i17]Youhei Akimoto, Anne Auger, Tobias Glasmachers:
Drift Theory in Continuous Search Spaces: Expected Hitting Time of the (1+1)-ES with 1/5 Success Rule. CoRR abs/1802.03209 (2018) - [i16]Nils Müller, Tobias Glasmachers:
Challenges in High-dimensional Reinforcement Learning with Evolution Strategies. CoRR abs/1806.01224 (2018) - [i15]Sahar Qaadan, Tobias Glasmachers:
Multi-Merge Budget Maintenance for Stochastic Gradient Descent SVM Training. CoRR abs/1806.10179 (2018) - [i14]Tobias Glasmachers, Sahar Qaadan:
Speeding Up Budgeted Stochastic Gradient Descent SVM Training with Precomputed Golden Section Search. CoRR abs/1806.10180 (2018) - [i13]Sahar Qaadan, Merlin Schüler, Tobias Glasmachers:
Dual SVM Training on a Budget. CoRR abs/1806.10182 (2018) - [i12]Tobias Glasmachers:
Challenges of Convex Quadratic Bi-objective Benchmark Problems. CoRR abs/1810.09690 (2018) - 2017
- [c27]Tobias Glasmachers:
Limits of End-to-End Learning. ACML 2017: 17-32 - [c26]Tobias Glasmachers:
A Fast Incremental BSP Tree Archive for Non-dominated Points. EMO 2017: 252-266 - [c25]Oswin Krause, Tobias Glasmachers, Christian Igel:
Qualitative and Quantitative Assessment of Step Size Adaptation Rules. FOGA 2017: 139-148 - [c24]Thomas Irmer, Tobias Glasmachers, Subhransu Maji:
Texture Attribute Synthesis and Transfer Using Feed-Forward CNNs. WACV 2017: 852-861 - [i11]Tobias Glasmachers:
Limits of End-to-End Learning. CoRR abs/1704.08305 (2017) - [i10]Ilya Loshchilov, Tobias Glasmachers, Hans-Georg Beyer:
Limited-Memory Matrix Adaptation for Large Scale Black-box Optimization. CoRR abs/1705.06693 (2017) - [i9]Tobias Glasmachers:
Global Convergence of the (1+1) Evolution Strategy. CoRR abs/1706.02887 (2017) - 2016
- [j10]Ürün Dogan, Tobias Glasmachers, Christian Igel:
A Unified View on Multi-class Support Vector Classification. J. Mach. Learn. Res. 17: 45:1-45:32 (2016) - [c23]Ilya Loshchilov, Tobias Glasmachers:
Anytime Bi-Objective Optimization with a Hybrid Multi-Objective CMA-ES (HMO-CMA-ES). GECCO (Companion) 2016: 1169-1176 - [c22]Oswin Krause, Tobias Glasmachers, Nikolaus Hansen, Christian Igel:
Unbounded Population MO-CMA-ES for the Bi-Objective BBOB Test Suite. GECCO (Companion) 2016: 1177-1184 - [i8]Aydin Demircioglu, Daniel Horn, Tobias Glasmachers, Bernd Bischl, Claus Weihs:
Fast model selection by limiting SVM training times. CoRR abs/1602.03368 (2016) - [i7]Tobias Glasmachers:
A Fast Incremental Archive for Multi-objective Optimization. CoRR abs/1604.01169 (2016) - [i6]Ilya Loshchilov, Tobias Glasmachers:
Anytime Bi-Objective Optimization with a Hybrid Multi-Objective CMA-ES (HMO-CMA-ES). CoRR abs/1605.02720 (2016) - 2015
- [c21]Oswin Krause, Tobias Glasmachers:
A CMA-ES with Multiplicative Covariance Matrix Updates. GECCO 2015: 281-288 - 2014
- [j9]Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, Jürgen Schmidhuber:
Natural evolution strategies. J. Mach. Learn. Res. 15(1): 949-980 (2014) - [c20]Tobias Glasmachers:
Handling sharp ridges with local supremum transformations. GECCO 2014: 389-396 - [c19]Luigi Malagò, Tobias Glasmachers:
Information geometry in evolutionary computation. GECCO (Companion) 2014: 709-726 - [c18]Tobias Glasmachers:
Optimized Approximation Sets for Low-Dimensional Benchmark Pareto Fronts. PPSN 2014: 569-578 - [c17]Tobias Glasmachers, Boris Naujoks, Günter Rudolph:
Start Small, Grow Big? Saving Multi-objective Function Evaluations. PPSN 2014: 579-588 - [i5]Tobias Glasmachers, Ürün Dogan:
Coordinate Descent with Online Adaptation of Coordinate Frequencies. CoRR abs/1401.3737 (2014) - 2013
- [c16]Tobias Glasmachers, Ürün Dogan:
Accelerated Coordinate Descent with Adaptive Coordinate Frequencies. ACML 2013: 72-86 - [c15]Tobias Glasmachers:
A natural evolution strategy with asynchronous strategy updates. GECCO 2013: 431-438 - [c14]Oswin Krause, Asja Fischer, Tobias Glasmachers, Christian Igel:
Approximation properties of DBNs with binary hidden units and real-valued visible units. ICML (1) 2013: 419-426 - [i4]Tobias Glasmachers, Ürün Dogan:
Accelerated Linear SVM Training with Adaptive Variable Selection Frequencies. CoRR abs/1302.5608 (2013) - [i3]Somayeh Danafar, Paola M. V. Rancoita, Tobias Glasmachers, Kevin Whittingstall, Jürgen Schmidhuber:
Testing Hypotheses by Regularized Maximum Mean Discrepancy. CoRR abs/1305.0423 (2013) - [i2]Tobias Glasmachers:
The Planning-ahead SMO Algorithm. CoRR abs/1307.8305 (2013) - 2012
- [j8]Tobias Glasmachers, Jan Koutník, Jürgen Schmidhuber:
Kernel representations for evolving continuous functions. Evol. Intell. 5(3): 171-187 (2012) - [c13]Ürün Dogan, Tobias Glasmachers, Christian Igel:
A Note on Extending Generalization Bounds for Binary Large-Margin Classifiers to Multiple Classes. ECML/PKDD (1) 2012: 122-129 - [c12]Tobias Glasmachers:
Convergence of the IGO-Flow of Isotropic Gaussian Distributions on Convex Quadratic Problems. PPSN (1) 2012: 1-10 - 2011
- [c11]Tom Schaul, Leo Pape, Tobias Glasmachers, Vincent Graziano, Jürgen Schmidhuber:
Coherence Progress: A Measure of Interestingness Based on Fixed Compressors. AGI 2011: 21-30 - [c10]Tobias Glasmachers, Jürgen Schmidhuber:
Optimal Direct Policy Search. AGI 2011: 52-61 - [c9]Giuseppe Cuccu, Faustino J. Gomez, Tobias Glasmachers:
Novelty-based restarts for evolution strategies. IEEE Congress on Evolutionary Computation 2011: 158-163 - [c8]Tom Schaul, Tobias Glasmachers, Jürgen Schmidhuber:
High dimensions and heavy tails for natural evolution strategies. GECCO 2011: 845-852 - [i1]Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jürgen Schmidhuber:
Natural Evolution Strategies. CoRR abs/1106.4487 (2011) - 2010
- [j7]Tobias Glasmachers, Christian Igel:
Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters. IEEE Trans. Pattern Anal. Mach. Intell. 32(8): 1522-1528 (2010) - [c7]Tobias Glasmachers, Tom Schaul, Yi Sun, Daan Wierstra, Jürgen Schmidhuber:
Exponential natural evolution strategies. GECCO 2010: 393-400 - [c6]Tobias Glasmachers:
Universal Consistency of Multi-Class Support Vector Classification. NIPS 2010: 739-747 - [c5]Tobias Glasmachers, Tom Schaul, Jürgen Schmidhuber:
A Natural Evolution Strategy for Multi-objective Optimization. PPSN (1) 2010: 627-636
2000 – 2009
- 2008
- [b1]Tobias Glasmachers:
Gradient based optimization of support vector machines. Ruhr University Bochum, 2008 - [j6]Christian Igel, Verena Heidrich-Meisner, Tobias Glasmachers:
Shark. J. Mach. Learn. Res. 9: 993-996 (2008) - [j5]Tobias Glasmachers, Christian Igel:
Second-Order SMO Improves SVM Online and Active Learning. Neural Comput. 20(2): 374-382 (2008) - [c4]Tobias Glasmachers:
On related violating pairs for working set selection in SMO algorithms. ESANN 2008: 475-480 - [c3]Tobias Glasmachers, Christian Igel:
Uncertainty Handling in Model Selection for Support Vector Machines. PPSN 2008: 185-194 - 2007
- [j4]Britta Mersch, Tobias Glasmachers, Peter Meinicke, Christian Igel:
Evolutionary Optimization of Sequence Kernels for Detection of bacterial gene Starts. Int. J. Neural Syst. 17(5): 369-381 (2007) - [j3]Christian Igel, Tobias Glasmachers, Britta Mersch, Nico Pfeifer, Peter Meinicke:
Gradient-Based Optimization of Kernel-Target Alignment for Sequence Kernels Applied to Bacterial Gene Start Detection. IEEE ACM Trans. Comput. Biol. Bioinform. 4(2): 216-226 (2007) - 2006
- [j2]Tobias Glasmachers, Christian Igel:
Maximum-Gain Working Set Selection for SVMs. J. Mach. Learn. Res. 7: 1437-1466 (2006) - [c2]Tobias Glasmachers:
Degeneracy in model selection for SVMs with radial Gaussian kernel. ESANN 2006: 587-592 - [c1]Britta Mersch, Tobias Glasmachers, Peter Meinicke, Christian Igel:
Evolutionary Optimization of Sequence Kernels for Detection of Bacterial Gene Starts. ICANN (2) 2006: 827-836 - 2005
- [j1]Tobias Glasmachers, Christian Igel:
Gradient-Based Adaptation of General Gaussian Kernels. Neural Comput. 17(10): 2099-2105 (2005)
Coauthor Index
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