Lukas Balles

According to our database, Lukas Balles authored at least 20 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

On the Choice of Learning Rate for Local SGD.

Lukas Balles, Prabhu Teja Sivaprasad, Cédric Archambeau

Trans. Mach. Learn. Res., 2024

u-μP: The Unit-Scaled Maximal Update Parametrization.

Charlie Blake, Constantin Eichenberg, Josef Dean, Lukas Balles, Luke Y. Prince, Björn Deiseroth, Andrés Felipe Cruz-Salinas, Carlo Luschi, Samuel Weinbach, Douglas Orr

CoRR, 2024

Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need.

Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles, Giovanni Zappella

CoRR, 2024

2023

A Negative Result on Gradient Matching for Selective Backprop.

Lukas Balles, Cédric Archambeau, Giovanni Zappella

CoRR, 2023

Continual Learning with Low Rank Adaptation.

Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles, Giovanni Zappella

CoRR, 2023

Renate: A Library for Real-World Continual Learning.

Martin Wistuba, Martin Ferianc, Lukas Balles, Cédric Archambeau, Giovanni Zappella

CoRR, 2023

PASHA: Efficient HPO and NAS with Progressive Resource Allocation.

Ondrej Bohdal, Lukas Balles, Martin Wistuba, Beyza Ermis, Cédric Archambeau, Giovanni Zappella

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

PASHA: Efficient HPO with Progressive Resource Allocation.

Ondrej Bohdal, Lukas Balles, Beyza Ermis, Cédric Archambeau, Giovanni Zappella

CoRR, 2022

Gradient-Matching Coresets for Rehearsal-Based Continual Learning.

Lukas Balles, Giovanni Zappella, Cédric Archambeau

CoRR, 2022

2021

Gradient-matching coresets for continual learning.

Lukas Balles, Giovanni Zappella, Cédric Archambeau

CoRR, 2021

2020

The Geometry of Sign Gradient Descent.

Lukas Balles, Fabian Pedregosa, Nicolas Le Roux

CoRR, 2020

Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering.

Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig

Proceedings of the "I Can't Believe It's Not Better!" at NeurIPS Workshops, 2020

2019

Limitations of the Empirical Fisher Approximation.

Frederik Kunstner, Lukas Balles, Philipp Hennig

CoRR, 2019

Limitations of the empirical Fisher approximation for natural gradient descent.

Frederik Kunstner, Philipp Hennig, Lukas Balles

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

DeepOBS: A Deep Learning Optimizer Benchmark Suite.

Frank Schneider, Lukas Balles, Philipp Hennig

Proceedings of the 7th International Conference on Learning Representations, 2019

Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation.

Anurag Ranjan, Varun Jampani, Lukas Balles, Kihwan Kim, Deqing Sun, Jonas Wulff, Michael J. Black

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients.

Lukas Balles, Philipp Hennig

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Early Stopping without a Validation Set.

Maren Mahsereci, Lukas Balles, Christoph Lassner, Philipp Hennig

CoRR, 2017

Follow the Signs for Robust Stochastic Optimization.

Lukas Balles, Philipp Hennig

CoRR, 2017

Coupling Adaptive Batch Sizes with Learning Rates.

Lukas Balles, Javier Romero, Philipp Hennig

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017