Lukas Balles

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Citations	1000	983
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Co-authors

Philipp HennigUniversity of Tübingen & Max Planck Institute for Intelligent SystemsVerified email at tue.mpg.de
Michael J. BlackMax Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Anurag RanjanResearcher, Apple Machine IntelligenceVerified email at tue.mpg.de
Varun JampaniResearcher, Google ResearchVerified email at google.com
Maren MahsereciAmazon ResearchVerified email at amazon.com
Frederik KunstnerUniversity of British ColumbiaVerified email at cs.ubc.ca
Javier RomeroReality Labs Research, MetaVerified email at tuebingen.mpg.de
Frank SchneiderPostdoctoral Researcher, University of TübingenVerified email at uni-tuebingen.de
Nicolas Le RouxMicrosoft Research, McGill, UdeMVerified email at le-roux.name
Fabian PedregosaGoogleVerified email at google.com
Dami ChoiUniversity of TorontoVerified email at cs.toronto.edu
Ricky Tian Qi ChenResearch Scientist at FAIR, Meta AIVerified email at meta.com
David DuvenaudAssociate Professor, University of TorontoVerified email at cs.toronto.edu

Lukas Balles

Amazon Research

Verified email at amazon.com - Homepage

Machine Learning Optimization Continual Learning


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Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation A Ranjan, V Jampani, L Balles, K Kim, D Sun, J Wulff, MJ Black Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019	485	2019
Limitations of the empirical Fisher approximation for natural gradient descent F Kunstner, P Hennig, L Balles Advances in neural information processing systems 32, 2019	132	2019
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients L Balles, P Hennig Proceedings of the 35th International Conference on Machine Learning (ICML …, 2018	119	2018
Coupling Adaptive Batch Sizes with Learning Rates L Balles, J Romero, P Hennig Proceedings of the Thirty-Third Conference on Uncertainty in Artificial …, 2017	118	2017
Early stopping without a validation set M Mahsereci, L Balles, C Lassner, P Hennig arXiv preprint arXiv:1703.09580, 2017	78	2017
DeepOBS: A Deep Learning Optimizer Benchmark Suite F Schneider, L Balles, P Hennig Seventh International Conference on Learning Representations (ICLR), 2019	45	2019
The Geometry of Sign Gradient Descent L Balles, F Pedregosa, N Le Roux arXiv preprint arXiv:2002.08056, 2020	10	2020
Self-tuning stochastic optimization with curvature-aware gradient filtering RTQ Chen, D Choi, L Balles, D Duvenaud, P Hennig PMLR, 2020	6	2020
Holographic and other point set distances for machine learning L Balles, T Fischbacher	3	2019
Automating stochastic optimization with gradient variance estimates L Balles, M Mahsereci, P Hennig ICML AutoML Workshop, 13, 2017	3	2017
Gradient-Matching Coresets for Rehearsal-Based Continual Learning L Balles, G Zappella, C Archambeau arXiv preprint arXiv:2203.14544, 2022	1	2022
PASHA: Efficient HPO with Progressive Resource Allocation O Bohdal, L Balles, B Ermis, C Archambeau, G Zappella arXiv preprint arXiv:2207.06940, 2022		2022
Noise-Aware Stochastic Optimization L Balles Universität Tübingen, 2022		2022
Gradient-matching coresets for continual learning L Balles, G Zappella, C Archambeau arXiv preprint arXiv:2112.05025, 2021		2021

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