Theory of Machine Learning

RUB
INI
Research Groups
Theory of Machine Learning

Creating Autonomous Agents

A long-standing goal of reinforcement learning is to create truly autonomous systems, e.g., in robotics and in virtual environments. We are working towards this vision. To this end we aim to equip deep reinforcement learning systems with additional structure, e.g., for efficient navigation and object-oriented actions.

Machine Learning Applications and Transfer

We have several ongoing projects aiming to transfer machine learning as a technology into different application areas, in academia as well as in industry. In these activities we keep an eye on problems of general interest, like transfer learning, automated machine learning, long-term maintainability and human factors.

Optimization

Optimization is underlying most of machine learning. It is used for training models, but also for tuning hyper-parameters and for automated model selection. Complementing gradient-based methods, we pursue research in the area of evolutionary optimization, where we are interested in algorithm design and provable performance guarantees.

Group Leader

Prof. Dr. Tobias Glasmachers

Members

Haneen Altartouri

Simon Hakenes, M.Sc.

Dr.-Ing. Sahar Qaadan

Daniel Vonk, M.Sc.

2019

Parallel Training of Non-linear Support-Vector-Machines on a Budget

Dual Training of Non-linear Support-Vector-Machines on a Budget

2021

Application of Reinforcement Learning to a Mining System
Fidencio, A., Naro, D., & Glasmachers, T.
In 19th IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI′2021)

@inproceedings{FidencioNaroGlasmachers2021, author = {Fidencio, Aline and Naro, Daniele and Glasmachers, Tobias}, title = {Application of Reinforcement Learning to a Mining System}, booktitle = {19th IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI′2021)}, year = {2021}, }

Fidencio, A., Naro, D., & Glasmachers, T.. (2021). Application of Reinforcement Learning to a Mining System. In 19th IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI′2021).

2020

Improving the performance of EEG decoding using anchored-STFT in conjunction with gradient norm adversarial augmentation
Ali, O., Saif-ur-Rehman, M., Dyck, S., Glasmachers, T., Iossifidis, I., & Klaes, C.
arXiv.org

arXiv

@techreport{AliSaif-ur-RehmanDyckEtAl2020, author = {Ali, Omair and Saif-ur-Rehman, Muhammad and Dyck, Susanne and Glasmachers, Tobias and Iossifidis, Ioannis and Klaes, Christian}, title = {Improving the performance of EEG decoding using anchored-STFT in conjunction with gradient norm adversarial augmentation}, institution = {arXiv.org}, number = {2011.14694}, year = {2020}, }

Ali, O., Saif-ur-Rehman, M., Dyck, S., Glasmachers, T., Iossifidis, I., & Klaes, C. (2020). Improving the performance of EEG decoding using anchored-STFT in conjunction with gradient norm adversarial augmentation (No. 2011.14694). arXiv.org.
The Hessian Estimation Evolution Strategy
Glasmachers, T., & Krause, O.
In Parallel Problem Solving from Nature (PPSN XVII) Springer

paper code

@inproceedings{GlasmachersKrause2020, author = {Glasmachers, Tobias and Krause, Oswin}, title = {The Hessian Estimation Evolution Strategy}, booktitle = {Parallel Problem Solving from Nature (PPSN XVII)}, publisher = {Springer}, year = {2020}, }

Glasmachers, T., & Krause, O. (2020). The Hessian Estimation Evolution Strategy. In Parallel Problem Solving from Nature (PPSN XVII). Springer.
Convergence Analysis of the Hessian Estimation Evolution Strategy
Glasmachers, T., & Krause, O.
arxiv.org

arXiv

@techreport{GlasmachersKrause2020b, author = {Glasmachers, Tobias and Krause, Oswin}, title = {Convergence Analysis of the Hessian Estimation Evolution Strategy}, number = {2009.02732}, year = {2020}, }

Glasmachers, T., & Krause, O. (2020). Convergence Analysis of the Hessian Estimation Evolution Strategy (No. 2009.02732). arxiv.org.
Latent Representation Prediction Networks
Hlynsson, H. D., Schüler, M., Schiewer, R., Glasmachers, T., & Wiskott, L.
arXiv preprint arXiv:2009.09439

Latent Representation Prediction Networks

@article{HlynssonSchülerSchiewerEtAl2020, author = {Hlynsson, Hlynur Davíð and Schüler, Merlin and Schiewer, Robin and Glasmachers, Tobias and Wiskott, Laurenz}, title = {Latent Representation Prediction Networks}, journal = {arXiv preprint arXiv:2009.09439}, year = {2020}, }

Hlynsson, H. D., Schüler, M., Schiewer, R., Glasmachers, T., & Wiskott, L.. (2020). Latent Representation Prediction Networks. arXiv preprint arXiv:2009.09439.
Non-local Optimization: Imposing Structure on Optimization Problems by Relaxation
Müller, N., & Glasmachers, T.
arXiv.org

link

@techreport{MüllerGlasmachers2020, author = {Müller, Nils and Glasmachers, Tobias}, title = {Non-local Optimization: Imposing Structure on Optimization Problems by Relaxation}, institution = {arXiv.org}, number = {2011.06064}, year = {2020}, }

Müller, N., & Glasmachers, T.. (2020). Non-local Optimization: Imposing Structure on Optimization Problems by Relaxation (No. 2011.06064). arXiv.org.
Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing
Oller, D., Cuccu, G., & Glasmachers, T.
In International Conference on Autonomous Agents and Multi-Agent Systems

pdf

@inproceedings{OllerCuccuGlasmachers2020, author = {Oller, Declan and Cuccu, Giuseppe and Glasmachers, Tobias}, title = {Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing}, booktitle = {International Conference on Autonomous Agents and Multi-Agent Systems}, year = {2020}, }

Oller, D., Cuccu, G., & Glasmachers, T.. (2020). Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing. In International Conference on Autonomous Agents and Multi-Agent Systems.

SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm

Saif-ur-Rehman, M., Ali, O., Dyck, S., Lienkämper, R., Metzler, M., Parpaley, Y., et al.

Journal of Neural Engineering

@article{Saif-ur-RehmanAliDyckEtAl2020,
	author		=	{Saif-ur-Rehman, Muhammad and Ali, Omair and Dyck, Susanne and Lienkämper, Robin and Metzler, Marita and Parpaley, Yaroslav and Wellmer, Jörg and Liu, Charles and Lee, Brian and Kellis, Spencer and Andersen, Richard and Iossifidis, Ioannis and Glasmachers, Tobias and Klaes, Christian},
	title		=	{SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm},
	journal		=	{Journal of Neural Engineering},
	year		=	{2020},
}

AI for Social Good: Unlocking the Opportunity for Positive Impact

Tomašev, N., Cornebise, J., Hutter, F., Picciariello, A., Connelly, B., Belgrave, D. C. M., et al.

Nature Communications, (2468)

@article{TomaševCornebiseHutterEtAl2020,
	author		=	{Tomašev, Nenad and Cornebise, Julien and Hutter, Frank and Picciariello, Angela and Connelly, Bec and Belgrave, Danielle C. M. and Ezer, Daphne and Cachat van der Haert, Fanny and Mugisha, Frank and , Gerald Abila and Arai, Hiromi and Almiraat, Hisham and Proskurnia, Julia and , Kyle Snyder and Otake, Mihoko and Othman, Mustafa and Mohamed, Shakir and Glasmachers, Tobias and de Wever, Wilfried and Teh, Yee Whye and Khan, Mohammad Emtiyaz and De Winne, Ruben and 1, Tom Schaul and Clopath, Claudia},
	title		=	{AI for Social Good: Unlocking the Opportunity for Positive Impact},
	journal		=	{Nature Communications},
	number		=	{2468},
	year		=	{2020},
}

2019

Moment Vector Encoding of Protein Sequences for Supervised Classification
Altartouri, H., & Glasmachers, T.
In Practical Applications of Computational Biology and Bioinformatics, 13th International Conference (pp. 25–35) Springer International Publishing

@incollection{AltartouriGlasmachers2019, author = {Altartouri, Haneen and Glasmachers, Tobias}, title = {Moment Vector Encoding of Protein Sequences for Supervised Classification}, booktitle = {Practical Applications of Computational Biology and Bioinformatics, 13th International Conference}, pages = {25–35}, publisher = {Springer International Publishing}, month = {June}, year = {2019}, doi = {10.1007/978-3-030-23873-5_4}, }

Altartouri, H., & Glasmachers, T.. (2019). Moment Vector Encoding of Protein Sequences for Supervised Classification. In Practical Applications of Computational Biology and Bioinformatics, 13th International Conference (pp. 25–35). Springer International Publishing. http://doi.org/10.1007/978-3-030-23873-5_4
Challenges of Convex Quadratic Bi-objective Benchmark Problems
Glasmachers, T.
In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (pp. 559–567) ACM

arxiv code

@inproceedings{Glasmachers2019b, author = {Glasmachers, Tobias}, title = {Challenges of Convex Quadratic Bi-objective Benchmark Problems}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)}, pages = {559–567}, publisher = {ACM}, year = {2019}, }

Glasmachers, T. (2019). Challenges of Convex Quadratic Bi-objective Benchmark Problems. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (pp. 559–567). ACM.
Global Convergence of the (1+1) Evolution Strategy
Glasmachers, T.
Evolutionary Computation Journal (ECJ)

arXiv.org pdf ECJ website

@article{Glasmachers2019, author = {Glasmachers, T.}, title = {Global Convergence of the (1+1) Evolution Strategy}, journal = {Evolutionary Computation Journal (ECJ)}, year = {2019}, }

Glasmachers, T. (2019). Global Convergence of the (1+1) Evolution Strategy. Evolutionary Computation Journal (ECJ).
Boosting Reinforcement Learning with Unsupervised Feature Extraction
Hakenes, S., & Glasmachers, T.
In Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation (pp. 555–566) Springer International Publishing

link

@incollection{HakenesGlasmachers2019, author = {Hakenes, Simon and Glasmachers, Tobias}, title = {Boosting Reinforcement Learning with Unsupervised Feature Extraction}, booktitle = {Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation}, pages = {555–566}, publisher = {Springer International Publishing}, year = {2019}, doi = {10.1007/978-3-030-30487-4_43}, }

Hakenes, S., & Glasmachers, T.. (2019). Boosting Reinforcement Learning with Unsupervised Feature Extraction. In Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation (pp. 555–566). Springer International Publishing. http://doi.org/10.1007/978-3-030-30487-4_43
Vehicle Shape and Color Classification Using Convolutional NeuralNetwork
Nafzi, M., Brauckmann, M., & Glasmachers, T.
arxiv.org

@techreport{NafziBrauckmannGlasmachers2019, author = {Nafzi, Mohamed and Brauckmann, Michael and Glasmachers, Tobias}, title = {Vehicle Shape and Color Classification Using Convolutional NeuralNetwork}, number = {1905.08612}, year = {2019}, }

Nafzi, M., Brauckmann, M., & Glasmachers, T.. (2019). Vehicle Shape and Color Classification Using Convolutional NeuralNetwork (No. 1905.08612). arxiv.org.
Dual SVM Training on a Budget
Qaadan, S., Schüler, M., & Glasmachers, T.
In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods SCITEPRESS - Science and Technology Publications

link

@inproceedings{QaadanSchülerGlasmachers2019, author = {Qaadan, Sahar and Schüler, Merlin and Glasmachers, Tobias}, title = {Dual SVM Training on a Budget}, booktitle = {Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods}, publisher = {SCITEPRESS - Science and Technology Publications}, year = {2019}, }

Qaadan, S., Schüler, M., & Glasmachers, T.. (2019). Dual SVM Training on a Budget. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications.
Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations
Reimann, D., Nidadavolu, K., ul Hassan, H., Vajragupta, N., Glasmachers, T., Junker, P., & Hartmaier, A.
Frontiers in Materials, 6, 181

link

@article{ReimannNidadavoluul HassanEtAl2019, author = {Reimann, Denise and Nidadavolu, Kapil and ul Hassan, Hamad and Vajragupta, Napat and Glasmachers, Tobias and Junker, Philipp and Hartmaier, Alexander}, title = {Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations}, journal = {Frontiers in Materials}, volume = {6}, pages = {181}, year = {2019}, doi = {10.3389/fmats.2019.00181}, }

Reimann, D., Nidadavolu, K., ul Hassan, H., Vajragupta, N., Glasmachers, T., Junker, P., & Hartmaier, A. (2019). Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations. Frontiers in Materials, 6, 181. http://doi.org/10.3389/fmats.2019.00181

SpikeDeeptector: A deep-learning based method for detection of neural spiking activity

Saif-ur-Rehman, M., Lienkämper, R., Parpaley, Y., Wellmer, J., Liu, C., Lee, B., et al.

Journal of Neural Engineering

@article{Saif-ur-RehmanLienkämperParpaleyEtAl2019,
	author		=	{Saif-ur-Rehman, Muhammad and Lienkämper, Robin and Parpaley, Yaroslav and Wellmer, Jörg and Liu, Charles and Lee, Brian and Kellis, Spencer and Andersen, Richard and Iossifidis, Ioannis and Glasmachers, Tobias and Klaes, Christian},
	title		=	{SpikeDeeptector: A deep-learning based method for detection of neural spiking activity},
	journal		=	{Journal of Neural Engineering},
	year		=	{2019},
}

2018

Drift Theory in Continuous Search Spaces: Expected Hitting Time of the (1+1)-ES with 1/5 Success Rule
Akimoto, Y., Auger, A., & Glasmachers, T.
In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) ACM

link 1802.03209.pdf

@inproceedings{AkimotoAugerGlasmachers2018, author = {Akimoto, Youhei and Auger, Anne and Glasmachers, Tobias}, title = {Drift Theory in Continuous Search Spaces: Expected Hitting Time of the (1+1)-ES with 1/5 Success Rule}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)}, publisher = {ACM}, year = {2018}, }

Akimoto, Y., Auger, A., & Glasmachers, T.. (2018). Drift Theory in Continuous Search Spaces: Expected Hitting Time of the (1+1)-ES with 1/5 Success Rule. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO). ACM.
Speeding Up Budgeted Dual SVM Training with Precomputed GSS
Glasmachers, T., & Qaadan, S.
(M. M. -y-G. Ruben Vera-Rodriguez Sergio Velastin & Morales, A., Eds.), The 23rd Iberoamerican Congress on Pattern Recognition

@postproceedings{GlasmachersQaadan2018b, author = {Glasmachers, Tobias and Qaadan, Sahar}, title = {Speeding Up Budgeted Dual SVM Training with Precomputed GSS}, year = {2018}, }

Glasmachers, T., & Qaadan, S.. (2018). Speeding Up Budgeted Dual SVM Training with Precomputed GSS. (M. M. -y-G. Ruben Vera-Rodriguez Sergio Velastin & Morales, A., Eds.), The 23rd Iberoamerican Congress on Pattern Recognition.
Speeding Up Budgeted Stochastic Gradient Descent SVM Training with Precomputed Golden Section Search
Glasmachers, T., & Qaadan, S.
In G. Nicosia, Pardalos, P., Giuffrida, G., Umeton, R., & Sciacca, V. (Eds.), The 4th International Conference on machine Learning, Optimization and Data science - LOD 2018

link

@inproceedings{GlasmachersQaadan2018, author = {Glasmachers, Tobias and Qaadan, Sahar}, title = {Speeding Up Budgeted Stochastic Gradient Descent SVM Training with Precomputed Golden Section Search}, booktitle = {The 4th International Conference on machine Learning, Optimization and Data science - LOD 2018}, editor = {Nicosia, Giuseppe and Pardalos, Panos and Giuffrida, Giovanni and Umeton, Renato and Sciacca, Vincenzo}, year = {2018}, }

Glasmachers, T., & Qaadan, S.. (2018). Speeding Up Budgeted Stochastic Gradient Descent SVM Training with Precomputed Golden Section Search. In G. Nicosia, Pardalos, P., Giuffrida, G., Umeton, R., & Sciacca, V. (Eds.), The 4th International Conference on machine Learning, Optimization and Data science - LOD 2018.
Large Scale Black-box Optimization by Limited-Memory Matrix Adaptation
Loshchilov, I., Glasmachers, T., & Beyer, H. -G.
IEEE Transactions on Evolutionary Computation, 99

link link

@article{LoshchilovGlasmachersBeyer2018, author = {Loshchilov, Ilya and Glasmachers, Tobias and Beyer, Hans-Georg}, title = {Large Scale Black-box Optimization by Limited-Memory Matrix Adaptation}, journal = {IEEE Transactions on Evolutionary Computation}, volume = {99}, year = {2018}, }

Loshchilov, I., Glasmachers, T., & Beyer, H. -G. (2018). Large Scale Black-box Optimization by Limited-Memory Matrix Adaptation. IEEE Transactions on Evolutionary Computation, 99.
Challenges in High-dimensional Controller Design with Evolution Strategies
Müller, N., & Glasmachers, T.
In Parallel Problem Solving from Nature (PPSN XVI) Springer

arXiv software

@inproceedings{MüllerGlasmachers2018, author = {Müller, Nils and Glasmachers, Tobias}, title = {Challenges in High-dimensional Controller Design with Evolution Strategies}, booktitle = {Parallel Problem Solving from Nature (PPSN XVI)}, publisher = {Springer}, year = {2018}, }

Müller, N., & Glasmachers, T.. (2018). Challenges in High-dimensional Controller Design with Evolution Strategies. In Parallel Problem Solving from Nature (PPSN XVI). Springer.
Multi-Merge Budget Maintenance for Stochastic Gradient Descent SVM Training
Qaadan, S., & Glasmachers, T.
13th WiML Workshop, Co-located with NeurIPS, Montreal, QC, Canada

@inproceeding{QaadanGlasmachers2018c, author = {Qaadan, Sahar and Glasmachers, Tobias}, title = {Multi-Merge Budget Maintenance for Stochastic Gradient Descent SVM Training}, year = {2018}, }

Qaadan, S., & Glasmachers, T.. (2018). Multi-Merge Budget Maintenance for Stochastic Gradient Descent SVM Training. 13th WiML Workshop, Co-located with NeurIPS, Montreal, QC, Canada.
Multi-Merge Budget Maintenance for Stochastic Gradient Descent SVM Training
Qaadan, S., & Glasmachers, T.
arXiv.org

link

@techreport{QaadanGlasmachers2018, author = {Qaadan, Sahar and Glasmachers, Tobias}, title = {Multi-Merge Budget Maintenance for Stochastic Gradient Descent SVM Training}, institution = {arXiv.org}, number = {arXiv:1806.10179}, year = {2018}, }

Qaadan, S., & Glasmachers, T.. (2018). Multi-Merge Budget Maintenance for Stochastic Gradient Descent SVM Training (No. arXiv:1806.10179). arXiv.org.
Multi-Merge Budget Maintenance for Stochastic Coordinate Ascent SVM Training
Qaadan, S., & Glasmachers, T.
Artificial Intelligence International Conference – A2IC 2018

@postproceedings{QaadanGlasmachers2018b, author = {Qaadan, Sahar and Glasmachers, Tobias}, title = {Multi-Merge Budget Maintenance for Stochastic Coordinate Ascent SVM Training}, year = {2018}, }

Qaadan, S., & Glasmachers, T.. (2018). Multi-Merge Budget Maintenance for Stochastic Coordinate Ascent SVM Training. Artificial Intelligence International Conference – A2IC 2018.
User-Centered Development of a Pedestrian Assistance System Using End-to-End Learning
Qureshi, H. S., Glasmachers, T., & Wiczorek, R.
In 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 808–813) IEEE

link

@inproceedings{QureshiGlasmachersWiczorek2018, author = {Qureshi, Hasham Shahid and Glasmachers, Tobias and Wiczorek, Rebecca}, title = {User-Centered Development of a Pedestrian Assistance System Using End-to-End Learning}, booktitle = {17th IEEE International Conference on Machine Learning and Applications (ICMLA)}, pages = {808–813}, organization = {IEEE}, year = {2018}, }

Qureshi, H. S., Glasmachers, T., & Wiczorek, R. (2018). User-Centered Development of a Pedestrian Assistance System Using End-to-End Learning. In 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 808–813). IEEE.

2017

A Fast Incremental BSP Tree Archive for Non-dominated Points
Glasmachers, T.
In Evolutionary Multi-Criterion Optimization (EMO) Springer

pdf code

@inproceedings{Glasmachers2017, author = {Glasmachers, T.}, title = {A Fast Incremental BSP Tree Archive for Non-dominated Points}, booktitle = {Evolutionary Multi-Criterion Optimization (EMO)}, publisher = {Springer}, year = {2017}, }

Glasmachers, T. (2017). A Fast Incremental BSP Tree Archive for Non-dominated Points. In Evolutionary Multi-Criterion Optimization (EMO). Springer.
Limits of End-to-End Learning
Glasmachers, T.
In Proceedings of the 9th Asian Conference on Machine Learning (ACML)

pdf code proceedings

@inproceedings{Glasmachers2017b, author = {Glasmachers, T.}, title = {Limits of End-to-End Learning}, booktitle = {Proceedings of the 9th Asian Conference on Machine Learning (ACML)}, year = {2017}, }

Glasmachers, T. (2017). Limits of End-to-End Learning. In Proceedings of the 9th Asian Conference on Machine Learning (ACML).
Texture attribute synthesis and transfer using feed-forward CNNs
Irmer, T., Glasmachers, T., & Maji, S.
In Winter Conference on Applications of Computer Vision (WACV) IEEE

pdf

@inproceedings{IrmerGlasmachersMaji2017, author = {Irmer, T. and Glasmachers, T. and Maji, S.}, title = {Texture attribute synthesis and transfer using feed-forward CNNs}, booktitle = {Winter Conference on Applications of Computer Vision (WACV)}, publisher = {IEEE}, year = {2017}, }

Irmer, T., Glasmachers, T., & Maji, S. (2017). Texture attribute synthesis and transfer using feed-forward CNNs. In Winter Conference on Applications of Computer Vision (WACV). IEEE.
Qualitative and Quantitative Assessment of Step Size Adaptation Rules
Krause, O., Glasmachers, T., & Igel, C.
In Conference on Foundations of Genetic Algorithms (FOGA) ACM

pdf

@inproceedings{KrauseGlasmachersIgel2017, author = {Krause, O. and Glasmachers, T. and Igel, C.}, title = {Qualitative and Quantitative Assessment of Step Size Adaptation Rules}, booktitle = {Conference on Foundations of Genetic Algorithms (FOGA)}, publisher = {ACM}, year = {2017}, }

Krause, O., Glasmachers, T., & Igel, C. (2017). Qualitative and Quantitative Assessment of Step Size Adaptation Rules. In Conference on Foundations of Genetic Algorithms (FOGA). ACM.

2016

Fast model selection by limiting SVM training times
Demircioğlu, A., Horn, D., Glasmachers, T., Bischl, B., & Weihs, C.
arxiv.org

pdf arXiv.org

@techreport{DemircioğluHornGlasmachersEtAl2016, author = {Demircioğlu, A. and Horn, D. and Glasmachers, T. and Bischl, B. and Weihs, C.}, title = {Fast model selection by limiting SVM training times}, institution = {arxiv.org}, number = {arxiv:1302.1602.03368v1}, year = {2016}, }

Demircioğlu, A., Horn, D., Glasmachers, T., Bischl, B., & Weihs, C. (2016). Fast model selection by limiting SVM training times (No. arxiv:1302.1602.03368v1). arxiv.org.
A Unified View on Multi-class Support Vector Classification
Doğan, Ü., Glasmachers, T., & Igel, C.
Journal of Machine Learning Research, 17(45), 1–32

pdf jmlr.org

@article{DoğanGlasmachersIgel2016, author = {Doğan, Ü. and Glasmachers, T. and Igel, C.}, title = {A Unified View on Multi-class Support Vector Classification}, journal = {Journal of Machine Learning Research}, volume = {17}, number = {45}, pages = {1–32}, year = {2016}, }

Doğan, Ü., Glasmachers, T., & Igel, C. (2016). A Unified View on Multi-class Support Vector Classification. Journal of Machine Learning Research, 17(45), 1–32.
Finite Sum Acceleration vs. Adaptive Learning Rates for the Training of Kernel Machines on a Budget
Glasmachers, T.
In NIPS workshop on Optimization for Machine Learning

pdf code

@inproceedings{Glasmachers2016, author = {Glasmachers, T.}, title = {Finite Sum Acceleration vs. Adaptive Learning Rates for the Training of Kernel Machines on a Budget}, booktitle = {NIPS workshop on Optimization for Machine Learning}, year = {2016}, }

Glasmachers, T. (2016). Finite Sum Acceleration vs. Adaptive Learning Rates for the Training of Kernel Machines on a Budget. In NIPS workshop on Optimization for Machine Learning.
Small Stochastic Average Gradient Steps
Glasmachers, T.
In NIPS workshop on Optimizing the Optimizers

pdf

@inproceedings{Glasmachers2016b, author = {Glasmachers, T.}, title = {Small Stochastic Average Gradient Steps}, booktitle = {NIPS workshop on Optimizing the Optimizers}, year = {2016}, }

Glasmachers, T. (2016). Small Stochastic Average Gradient Steps. In NIPS workshop on Optimizing the Optimizers.
A Comparative Study on Large Scale Kernelized Support Vector Machines
Horn, D., Demircioğlu, A., Bischl, B., Glasmachers, T., & Weihs, C.
Advances in Data Analysis and Classification (ADAC), 1–17

springer.com

@article{HornDemircioğluBischlEtAl2016, author = {Horn, D. and Demircioğlu, A. and Bischl, B. and Glasmachers, T. and Weihs, C.}, title = {A Comparative Study on Large Scale Kernelized Support Vector Machines}, journal = {Advances in Data Analysis and Classification (ADAC)}, pages = {1–17}, year = {2016}, }

Horn, D., Demircioğlu, A., Bischl, B., Glasmachers, T., & Weihs, C. (2016). A Comparative Study on Large Scale Kernelized Support Vector Machines. Advances in Data Analysis and Classification (ADAC), 1–17.
Unbounded Population MO-CMA-ES for the Bi-Objective BBOB Test Suite
Krause, O., Glasmachers, T., Hansen, N., & Igel, C.
In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)

pdf

@inproceedings{KrauseGlasmachersHansenEtAl2016, author = {Krause, O. and Glasmachers, T. and Hansen, N. and Igel, C.}, title = {Unbounded Population MO-CMA-ES for the Bi-Objective BBOB Test Suite}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)}, year = {2016}, }

Krause, O., Glasmachers, T., Hansen, N., & Igel, C. (2016). Unbounded Population MO-CMA-ES for the Bi-Objective BBOB Test Suite. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).
Multi-objective Optimization with Unbounded Solution Sets
Krause, O., Glasmachers, T., & Igel, C.
In NIPS workshop on Bayesian Optimization

pdf

@inproceedings{KrauseGlasmachersIgel2016, author = {Krause, O. and Glasmachers, T. and Igel, C.}, title = {Multi-objective Optimization with Unbounded Solution Sets}, booktitle = {NIPS workshop on Bayesian Optimization}, year = {2016}, }

Krause, O., Glasmachers, T., & Igel, C. (2016). Multi-objective Optimization with Unbounded Solution Sets. In NIPS workshop on Bayesian Optimization.
Anytime Bi-Objective Optimization with a Hybrid Multi-Objective CMA-ES (HMO-CMA-ES)
Loshchilov, I., & Glasmachers, T.
In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)

pdf

@inproceedings{LoshchilovGlasmachers2016, author = {Loshchilov, I. and Glasmachers, T.}, title = {Anytime Bi-Objective Optimization with a Hybrid Multi-Objective CMA-ES (HMO-CMA-ES)}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)}, year = {2016}, }

Loshchilov, I., & Glasmachers, T.. (2016). Anytime Bi-Objective Optimization with a Hybrid Multi-Objective CMA-ES (HMO-CMA-ES). In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).
Supervised Classification
Weihs, C., & Glasmachers, T.
In C. Weihs, Jannach, D., Vatolkin, I., & Rudolph, G. (Eds.), Music Data Analysis: Foundations and Applications

@incollection{WeihsGlasmachers2016, author = {Weihs, C. and Glasmachers, T.}, title = {Supervised Classification}, booktitle = {Music Data Analysis: Foundations and Applications}, editor = {Weihs, C. and Jannach, D. and Vatolkin, I. and Rudolph, G.}, year = {2016}, }

Weihs, C., & Glasmachers, T.. (2016). Supervised Classification. In C. Weihs, Jannach, D., Vatolkin, I., & Rudolph, G. (Eds.), Music Data Analysis: Foundations and Applications.

2015

A CMA-ES with Multiplicative Covariance Matrix Updates
Krause, O., & Glasmachers, T.
In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)

pdf code

@inproceedings{KrauseGlasmachers2015, author = {Krause, O. and Glasmachers, T.}, title = {A CMA-ES with Multiplicative Covariance Matrix Updates}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)}, year = {2015}, }

Krause, O., & Glasmachers, T.. (2015). A CMA-ES with Multiplicative Covariance Matrix Updates. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).

2014

Testing Hypotheses by Regularized Maximum Mean Discrepancy
Danafar, S., Rancoita, P. M. V., Glasmachers, T., Whittingstall, K., & Schmidhuber, J.
International Journal of Computer and Information Technology (IJCIT), 3(2)

pdf arXiv.org

@article{DanafarRancoitaGlasmachersEtAl2014, author = {Danafar, S. and Rancoita, P.M.V. and Glasmachers, T. and Whittingstall, K. and Schmidhuber, J.}, title = {Testing Hypotheses by Regularized Maximum Mean Discrepancy}, journal = {International Journal of Computer and Information Technology (IJCIT)}, volume = {3}, number = {2}, year = {2014}, }

Danafar, S., Rancoita, P. M. V., Glasmachers, T., Whittingstall, K., & Schmidhuber, J. (2014). Testing Hypotheses by Regularized Maximum Mean Discrepancy. International Journal of Computer and Information Technology (IJCIT), 3(2).
Handling Sharp Ridges with Local Supremum Transformations
Glasmachers, T.
In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)

pdf

@inproceedings{Glasmachers2014, author = {Glasmachers, T.}, title = {Handling Sharp Ridges with Local Supremum Transformations}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)}, year = {2014}, }

Glasmachers, T. (2014). Handling Sharp Ridges with Local Supremum Transformations. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).
Optimized Approximation Sets for Low-dimensional Benchmark Pareto Fronts
Glasmachers, T.
In Parallel Problem Solving from Nature (PPSN) Springer

pdf code

@inproceedings{Glasmachers2014b, author = {Glasmachers, T.}, title = {Optimized Approximation Sets for Low-dimensional Benchmark Pareto Fronts}, booktitle = {Parallel Problem Solving from Nature (PPSN)}, publisher = {Springer}, year = {2014}, }

Glasmachers, T. (2014). Optimized Approximation Sets for Low-dimensional Benchmark Pareto Fronts. In Parallel Problem Solving from Nature (PPSN). Springer.
Start Small, Grow Big - Saving Multiobjective Function Evaluations
Glasmachers, T., Naujoks, B., & Rudolph, G.
In Parallel Problem Solving from Nature (PPSN) Springer

pdf

@inproceedings{GlasmachersNaujoksRudolph2014, author = {Glasmachers, T. and Naujoks, B. and Rudolph, G.}, title = {Start Small, Grow Big - Saving Multiobjective Function Evaluations}, booktitle = {Parallel Problem Solving from Nature (PPSN)}, publisher = {Springer}, year = {2014}, }

Glasmachers, T., Naujoks, B., & Rudolph, G. (2014). Start Small, Grow Big - Saving Multiobjective Function Evaluations. In Parallel Problem Solving from Nature (PPSN). Springer.
Natural Evolution Strategies
Wierstra, D., Schaul, T., Glasmachers, T., Sun, Y., Peters, J., & Schmidhuber, J.
Journal of Machine Learning Research, 15, 949–980

pdf jmlr.org

@article{WierstraSchaulGlasmachersEtAl2014, author = {Wierstra, D. and Schaul, T. and Glasmachers, T. and Sun, Y. and Peters, J. and Schmidhuber, J.}, title = {Natural Evolution Strategies}, journal = {Journal of Machine Learning Research}, volume = {15}, pages = {949–980}, year = {2014}, }

Wierstra, D., Schaul, T., Glasmachers, T., Sun, Y., Peters, J., & Schmidhuber, J. (2014). Natural Evolution Strategies. Journal of Machine Learning Research, 15, 949–980.

2013

A Natural Evolution Strategy with Asynchronous Strategy Updates
Glasmachers, T.
In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)

pdf code

@inproceedings{Glasmachers2013, author = {Glasmachers, T.}, title = {A Natural Evolution Strategy with Asynchronous Strategy Updates}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)}, year = {2013}, }

Glasmachers, T. (2013). A Natural Evolution Strategy with Asynchronous Strategy Updates. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).

The Planning-ahead SMO Algorithm

Glasmachers, T.

arxiv.org

@techreport{Glasmachers2013b,
	author		=	{Glasmachers, T.},
	title		=	{The Planning-ahead SMO Algorithm},
	institution	=	{arxiv.org},
	number		=	{arxiv:1305.0423v1},
	year		=	{2013},
}

Accelerated Coordinate Descent with Adaptive Coordinate Frequencies
Glasmachers, T., & Doğan, Ü.
In Proceedings of the fifth Asian Conference on Machine Learning (ACML)

pdf jmlr.org code (LASSO) code (LibLinear)

@inproceedings{GlasmachersDoğan2013, author = {Glasmachers, T. and Doğan, Ü.}, title = {Accelerated Coordinate Descent with Adaptive Coordinate Frequencies}, booktitle = {Proceedings of the fifth Asian Conference on Machine Learning (ACML)}, year = {2013}, }

Glasmachers, T., & Doğan, Ü. (2013). Accelerated Coordinate Descent with Adaptive Coordinate Frequencies. In Proceedings of the fifth Asian Conference on Machine Learning (ACML).
Approximation properties of DBNs with binary hidden units and real-valued visible units
Krause, O., Fischer, A., Glasmachers, T., & Igel, C.
In Proceedings of the International Conference on Machine Learning (ICML)

pdf

@inproceedings{KrauseFischerGlasmachersEtAl2013, author = {Krause, O. and Fischer, A. and Glasmachers, T. and Igel, C.}, title = {Approximation properties of DBNs with binary hidden units and real-valued visible units}, booktitle = {Proceedings of the International Conference on Machine Learning (ICML)}, year = {2013}, }

Krause, O., Fischer, A., Glasmachers, T., & Igel, C. (2013). Approximation properties of DBNs with binary hidden units and real-valued visible units. In Proceedings of the International Conference on Machine Learning (ICML).

2012

Turning Binary Large-margin Bounds into Multi-class Bounds
Doğan, Ü., Glasmachers, T., & Igel, C.
In ICML workshop on RKHS and kernel-based methods

pdf

@inproceedings{DoğanGlasmachersIgel2012, author = {Doğan, Ü. and Glasmachers, T. and Igel, C.}, title = {Turning Binary Large-margin Bounds into Multi-class Bounds}, booktitle = {ICML workshop on RKHS and kernel-based methods}, year = {2012}, }

Doğan, Ü., Glasmachers, T., & Igel, C. (2012). Turning Binary Large-margin Bounds into Multi-class Bounds. In ICML workshop on RKHS and kernel-based methods.
A Note on Extending Generalization Bounds for Binary Large-margin Classifiers to Multiple Classes
Doğan, Ü., Glasmachers, T., & Igel, C.
In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD)

pdf

@inproceedings{DoğanGlasmachersIgel2012b, author = {Doğan, Ü. and Glasmachers, T. and Igel, C.}, title = {A Note on Extending Generalization Bounds for Binary Large-margin Classifiers to Multiple Classes}, booktitle = {Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD)}, year = {2012}, }

Doğan, Ü., Glasmachers, T., & Igel, C. (2012). A Note on Extending Generalization Bounds for Binary Large-margin Classifiers to Multiple Classes. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD).
Convergence of the IGO-Flow of Isotropic Gaussian Distributions on Convex Quadratic Problems
Glasmachers, T.
In C. C. Coello, Cutello, V., Deb, K., Forrest, S., Nicosia, G., & Pavone, M. (Eds.), Parallel Problem Solving from Nature (PPSN) Springer

pdf

@inproceedings{Glasmachers2012, author = {Glasmachers, T.}, title = {Convergence of the IGO-Flow of Isotropic Gaussian Distributions on Convex Quadratic Problems}, booktitle = {Parallel Problem Solving from Nature (PPSN)}, editor = {Coello, C. Coello and Cutello, V. and Deb, K. and Forrest, S. and Nicosia, G. and Pavone, M.}, publisher = {Springer}, year = {2012}, }

Glasmachers, T. (2012). Convergence of the IGO-Flow of Isotropic Gaussian Distributions on Convex Quadratic Problems. In C. C. Coello, Cutello, V., Deb, K., Forrest, S., Nicosia, G., & Pavone, M. (Eds.), Parallel Problem Solving from Nature (PPSN). Springer.
Kernel Representations for Evolving Continuous Functions
Glasmachers, T., Koutník, J., & Schmidhuber, J.
Journal of Evolutionary Intelligence, 5(3), 171–187

pdf

@article{GlasmachersKoutníkSchmidhuber2012, author = {Glasmachers, T. and Koutník, J. and Schmidhuber, J.}, title = {Kernel Representations for Evolving Continuous Functions}, journal = {Journal of Evolutionary Intelligence}, volume = {5}, number = {3}, pages = {171–187}, year = {2012}, doi = {10.1007/s12065-012-0070-y}, }

Glasmachers, T., Koutník, J., & Schmidhuber, J. (2012). Kernel Representations for Evolving Continuous Functions. Journal of Evolutionary Intelligence, 5(3), 171–187. http://doi.org/10.1007/s12065-012-0070-y

2011

Novelty Restarts for Evolution Strategies
Cuccu, G., Gomez, F., & Glasmachers, T.
In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) IEEE

pdf

@inproceedings{CuccuGomezGlasmachers2011, author = {Cuccu, G. and Gomez, F. and Glasmachers, T.}, title = {Novelty Restarts for Evolution Strategies}, booktitle = {Proceedings of the IEEE Congress on Evolutionary Computation (CEC)}, publisher = {IEEE}, year = {2011}, }

Cuccu, G., Gomez, F., & Glasmachers, T.. (2011). Novelty Restarts for Evolution Strategies. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC). IEEE.
Optimal Direct Policy Search
Glasmachers, T., & Schmidhuber, J.
In Proceedings of the 4th Conference on Artificial General Intelligence (AGI)

pdf

@inproceedings{GlasmachersSchmidhuber2011, author = {Glasmachers, T. and Schmidhuber, J.}, title = {Optimal Direct Policy Search}, booktitle = {Proceedings of the 4th Conference on Artificial General Intelligence (AGI)}, year = {2011}, }

Glasmachers, T., & Schmidhuber, J. (2011). Optimal Direct Policy Search. In Proceedings of the 4th Conference on Artificial General Intelligence (AGI).
Artificial Curiosity for Autonomous Space Exploration
Graziano, V., Glasmachers, T., Schaul, T., Pape, L., Cuccu, G., Leitner, J., & Schmidhuber, J.
ACTA FUTURA

pdf

@article{GrazianoGlasmachersSchaulEtAl2011, author = {Graziano, V. and Glasmachers, T. and Schaul, T. and Pape, L. and Cuccu, G. and Leitner, J. and Schmidhuber, J.}, title = {Artificial Curiosity for Autonomous Space Exploration}, journal = {ACTA FUTURA}, year = {2011}, }

Graziano, V., Glasmachers, T., Schaul, T., Pape, L., Cuccu, G., Leitner, J., & Schmidhuber, J. (2011). Artificial Curiosity for Autonomous Space Exploration. ACTA FUTURA.
High Dimensions and Heavy Tails for Natural Evolution Strategies
Schaul, T., Glasmachers, T., & Schmidhuber, J.
In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)

pdf

@inproceedings{SchaulGlasmachersSchmidhuber2011, author = {Schaul, T. and Glasmachers, T. and Schmidhuber, J.}, title = {High Dimensions and Heavy Tails for Natural Evolution Strategies}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)}, year = {2011}, }

Schaul, T., Glasmachers, T., & Schmidhuber, J. (2011). High Dimensions and Heavy Tails for Natural Evolution Strategies. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).
Coherence Progress: A Measure of Interestingness Based on Fixed Compressors
Schaul, T., Pape, L., Glasmachers, T., Graziano, V., & Schmidhuber, J.
In Proceedings of the 4th Conference on Artificial General Intelligence (AGI)

pdf

@inproceedings{SchaulPapeGlasmachersEtAl2011, author = {Schaul, T. and Pape, L. and Glasmachers, T. and Graziano, V. and Schmidhuber, J.}, title = {Coherence Progress: A Measure of Interestingness Based on Fixed Compressors}, booktitle = {Proceedings of the 4th Conference on Artificial General Intelligence (AGI)}, year = {2011}, }

Schaul, T., Pape, L., Glasmachers, T., Graziano, V., & Schmidhuber, J. (2011). Coherence Progress: A Measure of Interestingness Based on Fixed Compressors. In Proceedings of the 4th Conference on Artificial General Intelligence (AGI).

2010

Universal Consistency of Multi-Class Support Vector Classification
Glasmachers, T.
In Advances in Neural Information Processing Systems (NIPS)

pdf

@inproceedings{Glasmachers2010, author = {Glasmachers, T.}, title = {Universal Consistency of Multi-Class Support Vector Classification}, booktitle = {Advances in Neural Information Processing Systems (NIPS)}, year = {2010}, }

Glasmachers, T. (2010). Universal Consistency of Multi-Class Support Vector Classification. In Advances in Neural Information Processing Systems (NIPS).
Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters
Glasmachers, T., & Igel, C.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8), 1522–1528

pdf

@article{GlasmachersIgel2010, author = {Glasmachers, T. and Igel, C.}, title = {Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {32}, number = {8}, pages = {1522–1528}, year = {2010}, }

Glasmachers, T., & Igel, C. (2010). Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8), 1522–1528.
A Natural Evolution Strategy for Multi-Objective Optimization
Glasmachers, T., Schaul, T., & Schmidhuber, J.
In Parallel Problem Solving from Nature (PPSN) Springer

pdf

@inproceedings{GlasmachersSchaulSchmidhuber2010, author = {Glasmachers, T. and Schaul, T. and Schmidhuber, J.}, title = {A Natural Evolution Strategy for Multi-Objective Optimization}, booktitle = {Parallel Problem Solving from Nature (PPSN)}, publisher = {Springer}, year = {2010}, }

Glasmachers, T., Schaul, T., & Schmidhuber, J. (2010). A Natural Evolution Strategy for Multi-Objective Optimization. In Parallel Problem Solving from Nature (PPSN). Springer.
Exponential Natural Evolution Strategies
Glasmachers, T., Schaul, T., Sun, Y., Wierstra, D., & Schmidhuber, J.
In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)

pdf

@inproceedings{GlasmachersSchaulSunEtAl2010, author = {Glasmachers, T. and Schaul, T. and Sun, Y. and Wierstra, D. and Schmidhuber, J.}, title = {Exponential Natural Evolution Strategies}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)}, year = {2010}, }

Glasmachers, T., Schaul, T., Sun, Y., Wierstra, D., & Schmidhuber, J. (2010). Exponential Natural Evolution Strategies. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).
Frontier Search
Sun, Y., Glasmachers, T., Schaul, T., & Schmidhuber, J.
In Proceedings of the 3rd Conference on Artificial General Intelligence (AGI)

pdf

@inproceedings{SunGlasmachersSchaulEtAl2010, author = {Sun, Y. and Glasmachers, T. and Schaul, T. and Schmidhuber, J.}, title = {Frontier Search}, booktitle = {Proceedings of the 3rd Conference on Artificial General Intelligence (AGI)}, year = {2010}, }

Sun, Y., Glasmachers, T., Schaul, T., & Schmidhuber, J. (2010). Frontier Search. In Proceedings of the 3rd Conference on Artificial General Intelligence (AGI).

2008

On related violating pairs for working set selection in SMO algorithms
Glasmachers, T.
In M. Verleysen (Ed.), Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN) d-side publications

pdf

@inproceedings{Glasmachers2008, author = {Glasmachers, T.}, title = {On related violating pairs for working set selection in SMO algorithms}, booktitle = {Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN)}, editor = {Verleysen, M.}, publisher = {d-side publications}, year = {2008}, }

Glasmachers, T. (2008). On related violating pairs for working set selection in SMO algorithms. In M. Verleysen (Ed.), Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN). d-side publications.
Gradient Based Optimization of Support Vector Machines
Glasmachers, T.
Doctoral thesis, Fakultät für Mathematik, Ruhr-Universität Bochum, Germany

pdf

@phdthesis{Glasmachers2008b, author = {Glasmachers, T.}, title = {Gradient Based Optimization of Support Vector Machines}, school = {Fakultät für Mathematik, Ruhr-Universität Bochum, Germany}, year = {2008}, }

Glasmachers, T. (2008). Gradient Based Optimization of Support Vector Machines. Doctoral thesis, Fakultät für Mathematik, Ruhr-Universität Bochum, Germany.
Second-Order SMO Improves SVM Online and Active Learning
Glasmachers, T., & Igel, C.
Neural Computation, 20(2), 374–382

pdf

@article{GlasmachersIgel2008, author = {Glasmachers, T. and Igel, C.}, title = {Second-Order SMO Improves SVM Online and Active Learning}, journal = {Neural Computation}, volume = {20}, number = {2}, pages = {374–382}, year = {2008}, }

Glasmachers, T., & Igel, C. (2008). Second-Order SMO Improves SVM Online and Active Learning. Neural Computation, 20(2), 374–382.
Uncertainty Handling in Model Selection for Support Vector Machines
Glasmachers, T., & Igel, C.
In G. Rudolph, Jansen, T., Lucas, S., Poloni, C., & Beume, N. (Eds.), Parallel Problem Solving from Nature (PPSN) (pp. 185–194) Springer

pdf

@inproceedings{GlasmachersIgel2008b, author = {Glasmachers, T. and Igel, C.}, title = {Uncertainty Handling in Model Selection for Support Vector Machines}, booktitle = {Parallel Problem Solving from Nature (PPSN)}, editor = {Rudolph, G. and Jansen, T. and Lucas, S. and Poloni, C. and Beume, N.}, pages = {185–194}, publisher = {Springer}, year = {2008}, }

Glasmachers, T., & Igel, C. (2008). Uncertainty Handling in Model Selection for Support Vector Machines. In G. Rudolph, Jansen, T., Lucas, S., Poloni, C., & Beume, N. (Eds.), Parallel Problem Solving from Nature (PPSN) (pp. 185–194). Springer.
Shark
Igel, C., Heidrich-Meisner, V., & Glasmachers, T.
Journal of Machine Learning Research, 9, 993–996

pdf jmlr.org

@article{IgelHeidrich-MeisnerGlasmachers2008, author = {Igel, C. and Heidrich-Meisner, V. and Glasmachers, T.}, title = {Shark}, journal = {Journal of Machine Learning Research}, volume = {9}, pages = {993–996}, year = {2008}, }

Igel, C., Heidrich-Meisner, V., & Glasmachers, T.. (2008). Shark. Journal of Machine Learning Research, 9, 993–996.

2007

Gradient-Based Optimization of Kernel-Target Alignment for Sequence Kernels Applied to Bacterial Gene Start Detection
Igel, C., Glasmachers, T., Mersch, B., Pfeifer, N., & Meinicke, P.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 4(2), 216–226

pdf

@article{IgelGlasmachersMerschEtAl2007, author = {Igel, C. and Glasmachers, T. and Mersch, B. and Pfeifer, N. and Meinicke, P.}, title = {Gradient-Based Optimization of Kernel-Target Alignment for Sequence Kernels Applied to Bacterial Gene Start Detection}, journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)}, volume = {4}, number = {2}, pages = {216–226}, year = {2007}, }

Igel, C., Glasmachers, T., Mersch, B., Pfeifer, N., & Meinicke, P. (2007). Gradient-Based Optimization of Kernel-Target Alignment for Sequence Kernels Applied to Bacterial Gene Start Detection. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 4(2), 216–226.
Evolutionary Optimization of Sequence Kernels for Detection of Bacterial Gene Starts
Mersch, B., Glasmachers, T., Meinicke, P., & Igel, C.
International Journal of Neural Systems, 17(5), 369–381

pdf

@article{MerschGlasmachersMeinickeEtAl2007, author = {Mersch, B. and Glasmachers, T. and Meinicke, P. and Igel, C.}, title = {Evolutionary Optimization of Sequence Kernels for Detection of Bacterial Gene Starts}, journal = {International Journal of Neural Systems}, volume = {17}, number = {5}, pages = {369–381}, year = {2007}, }

Mersch, B., Glasmachers, T., Meinicke, P., & Igel, C. (2007). Evolutionary Optimization of Sequence Kernels for Detection of Bacterial Gene Starts. International Journal of Neural Systems, 17(5), 369–381.

2006

Degeneracy in Model Selection for SVMs with Radial Gaussian Kernel
Glasmachers, T.
In M. Verleysen (Ed.), Proceedings of the 14th European Symposium on Artificial Neural Networks (ESANN) d-side publications

pdf

@inproceedings{Glasmachers2006, author = {Glasmachers, T.}, title = {Degeneracy in Model Selection for SVMs with Radial Gaussian Kernel}, booktitle = {Proceedings of the 14th European Symposium on Artificial Neural Networks (ESANN)}, editor = {Verleysen, M.}, publisher = {d-side publications}, year = {2006}, }

Glasmachers, T. (2006). Degeneracy in Model Selection for SVMs with Radial Gaussian Kernel. In M. Verleysen (Ed.), Proceedings of the 14th European Symposium on Artificial Neural Networks (ESANN). d-side publications.
Maximum-Gain Working Set Selection for Support Vector Machines
Glasmachers, T., & Igel, C.
Journal of Machine Learning Research, 7, 1437–1466

pdf jmlr.org

@article{GlasmachersIgel2006, author = {Glasmachers, T. and Igel, C.}, title = {Maximum-Gain Working Set Selection for Support Vector Machines}, journal = {Journal of Machine Learning Research}, volume = {7}, pages = {1437–1466}, year = {2006}, }

Glasmachers, T., & Igel, C. (2006). Maximum-Gain Working Set Selection for Support Vector Machines. Journal of Machine Learning Research, 7, 1437–1466.
Evolutionary Optimization of Sequence Kernels for Detection of Bacterial Gene Starts
Mersch, B., Glasmachers, T., Meinicke, P., & Igel, C.
In Proceedings of the 16th International Conference on Artificial Neural Networks (ICANN) Springer-Verlag

pdf

@inproceedings{MerschGlasmachersMeinickeEtAl2006, author = {Mersch, B. and Glasmachers, T. and Meinicke, P. and Igel, C.}, title = {Evolutionary Optimization of Sequence Kernels for Detection of Bacterial Gene Starts}, booktitle = {Proceedings of the 16th International Conference on Artificial Neural Networks (ICANN)}, publisher = {Springer-Verlag}, year = {2006}, }

Mersch, B., Glasmachers, T., Meinicke, P., & Igel, C. (2006). Evolutionary Optimization of Sequence Kernels for Detection of Bacterial Gene Starts. In Proceedings of the 16th International Conference on Artificial Neural Networks (ICANN). Springer-Verlag.

2005

Gradient-based Adaptation of General Gaussian Kernels
Glasmachers, T., & Igel, C.
Neural Computation, 17(10), 2099–2105

pdf

@article{GlasmachersIgel2005, author = {Glasmachers, T. and Igel, C.}, title = {Gradient-based Adaptation of General Gaussian Kernels}, journal = {Neural Computation}, volume = {17}, number = {10}, pages = {2099–2105}, year = {2005}, }

Glasmachers, T., & Igel, C. (2005). Gradient-based Adaptation of General Gaussian Kernels. Neural Computation, 17(10), 2099–2105.

2020

Development of a controller for corridor following based on visual inputs with deep reinforcement learning
Bachert, M.
Applied Computer Science, Ruhr University Bochum, Germany

@bachelorsthesis{Bachert2020, author = {Bachert, Marvin}, title = {Development of a controller for corridor following based on visual inputs with deep reinforcement learning}, year = {2020}, }

Bachert, M. (2020). Development of a controller for corridor following based on visual inputs with deep reinforcement learning. Applied Computer Science, Ruhr University Bochum, Germany.
Vergleich von Reinforcement Learning, Generative Adversarial Imitation Learning und Behavioral Cloning in einfachen Computerspielen
Helmig, D.
Applied Computer Science, Ruhr University Bochum, Germany

@bachelorsthesis{Helmig2020, author = {Helmig, Dominik}, title = {Vergleich von Reinforcement Learning, Generative Adversarial Imitation Learning und Behavioral Cloning in einfachen Computerspielen}, year = {2020}, }

Helmig, D. (2020). Vergleich von Reinforcement Learning, Generative Adversarial Imitation Learning und Behavioral Cloning in einfachen Computerspielen. Applied Computer Science, Ruhr University Bochum, Germany.

2019

Einzelbildbasierte Wegplanung in virtuellen Umgebungen
Akkiraz, T.

@bachelorthesis{Akkiraz2019, author = {Akkiraz, Tolga}, title = {Einzelbildbasierte Wegplanung in virtuellen Umgebungen}, year = {2019}, }

Akkiraz, T. (2019). Einzelbildbasierte Wegplanung in virtuellen Umgebungen.

Detektion von Wolken in Satellitendaten

Gergs, L.

@mastersthesis{Gergs2019,
	author		=	{Gergs, Lukas},
	title		=	{Detektion von Wolken in Satellitendaten},
	year		=	{2019},
}

Experimental evaluation of neural monocular depth estimation methods on video games
Gondermann, N.

@bachelorthesis{Gondermann2019, author = {Gondermann, Nils}, title = {Experimental evaluation of neural monocular depth estimation methods on video games}, year = {2019}, }

Gondermann, N. (2019). Experimental evaluation of neural monocular depth estimation methods on video games.

2018

Auxiliary Unsupervised Methods for Deep Reinforcement Learning
Hakenes, S.

@mastersthesis{Hakenes2018, author = {Hakenes, Simon}, title = {Auxiliary Unsupervised Methods for Deep Reinforcement Learning}, year = {2018}, }

Hakenes, S. (2018). Auxiliary Unsupervised Methods for Deep Reinforcement Learning.
Applying Budget Maintenance strategy on the Adaptive Coordinate Frequency-Coordinate Descent
Pendyala, A.

@mastersthesis{Pendyala2018, author = {Pendyala, Abhijeet}, title = {Applying Budget Maintenance strategy on the Adaptive Coordinate Frequency-Coordinate Descent}, year = {2018}, }

Pendyala, A. (2018). Applying Budget Maintenance strategy on the Adaptive Coordinate Frequency-Coordinate Descent.
Predicting the number of incoming groupage shipments using LSTM neural networks
Vakavchiev, V.
Master’s thesis, Applied Computer Science, Ruhr University Bochum, Germany

@mastersthesis{Vakavchiev2018, author = {Vakavchiev, Vasil}, title = {Predicting the number of incoming groupage shipments using LSTM neural networks}, school = {Applied Computer Science, Ruhr University Bochum, Germany}, year = {2018}, }

Vakavchiev, V. (2018). Predicting the number of incoming groupage shipments using LSTM neural networks. Master’s thesis, Applied Computer Science, Ruhr University Bochum, Germany.

About the INI

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Contact

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210

sekretariat@ini.rub.de
www.ini.rub.de