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Lukas Balles
Lukas Balles
Aleph Alpha
Verified email at aleph-alpha.com - Homepage
Title
Cited by
Cited by
Year
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
6532019
Limitations of the empirical fisher approximation for natural gradient descent
F Kunstner, P Hennig, L Balles
Advances in neural information processing systems 32, 2019
2082019
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
1742018
Coupling Adaptive Batch Sizes with Learning Rates
L Balles, J Romero, P Hennig
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial …, 2017
1322017
Early stopping without a validation set
M Mahsereci, L Balles, C Lassner, P Hennig
arXiv preprint arXiv:1703.09580, 2017
1132017
DeepOBS: A Deep Learning Optimizer Benchmark Suite
F Schneider, L Balles, P Hennig
Seventh International Conference on Learning Representations (ICLR), 2019
712019
PASHA: Efficient HPO and NAS with progressive resource allocation
O Bohdal, L Balles, M Wistuba, B Ermis, C Archambeau, G Zappella
arXiv preprint arXiv:2207.06940, 2022
162022
The Geometry of Sign Gradient Descent
L Balles, F Pedregosa, N Le Roux
arXiv preprint arXiv:2002.08056, 2020
152020
Self-tuning stochastic optimization with curvature-aware gradient filtering
RTQ Chen, D Choi, L Balles, D Duvenaud, P Hennig
PMLR, 2020
82020
Holographic and other point set distances for machine learning
L Balles, T Fischbacher
62019
Renate: A library for real-world continual learning
M Wistuba, M Ferianc, L Balles, C Archambeau, G Zappella
arXiv preprint arXiv:2304.12067, 2023
42023
Automating stochastic optimization with gradient variance estimates
L Balles, M Mahsereci, P Hennig
ICML AutoML Workshop, 13, 2017
42017
Continual learning with low rank adaptation
M Wistuba, PT Sivaprasad, L Balles, G Zappella
arXiv preprint arXiv:2311.17601, 2023
22023
Gradient-matching coresets for rehearsal-based continual learning
L Balles, G Zappella, C Archambeau
arXiv preprint arXiv:2203.14544, 2022
22022
Gradient-matching coresets for continual learning
L Balles, G Zappella, C Archambeau
arXiv preprint arXiv:2112.05025, 2021
22021
On the Choice of Learning Rate for Local SGD
L Balles, C Archambeau
Transactions on Machine Learning Research, 2024
12024
Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need
M Wistuba, PT Sivaprasad, L Balles, G Zappella
arXiv preprint arXiv:2406.03216, 2024
2024
Continual machine learning in a provider network
G Zappella, LS Balles, B Ermis, M Wistuba, CP Archambeau
US Patent App. 17/937,319, 2024
2024
A negative result on gradient matching for selective backprop
L Balles, C Archambeau, G Zappella
arXiv preprint arXiv:2312.05021, 2023
2023
Noise-aware stochastic optimization
L Balles
Universität Tübingen, 2022
2022
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