Lukas Balles

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 23

Top Topics

Principal Curvatures

Stochastic Optimization

Motion Segmentation

Top Venues

Publications

Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles, Giovanni Zappella
Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need. CoRR (2024)
Charlie Blake, Constantin Eichenberg, Josef Dean, Lukas Balles, Luke Y. Prince, Björn Deiseroth, Andrés Felipe Cruz-Salinas, Carlo Luschi, Samuel Weinbach, Douglas Orr
u-μP: The Unit-Scaled Maximal Update Parametrization. CoRR (2024)
Lukas Balles, Prabhu Teja Sivaprasad, Cédric Archambeau
On the Choice of Learning Rate for Local SGD. Trans. Mach. Learn. Res. 2024 (2024)
Lukas Balles, Cédric Archambeau, Giovanni Zappella
A Negative Result on Gradient Matching for Selective Backprop. CoRR (2023)
Martin Wistuba, Martin Ferianc, Lukas Balles, Cédric Archambeau, Giovanni Zappella
Renate: A Library for Real-World Continual Learning. CoRR (2023)
Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles, Giovanni Zappella
Continual Learning with Low Rank Adaptation. CoRR (2023)
Ondrej Bohdal, Lukas Balles, Martin Wistuba, Beyza Ermis, Cédric Archambeau, Giovanni Zappella
PASHA: Efficient HPO and NAS with Progressive Resource Allocation. ICLR (2023)
Ondrej Bohdal, Lukas Balles, Beyza Ermis, Cédric Archambeau, Giovanni Zappella
PASHA: Efficient HPO with Progressive Resource Allocation. CoRR (2022)
Lukas Balles, Giovanni Zappella, Cédric Archambeau
Gradient-Matching Coresets for Rehearsal-Based Continual Learning. CoRR (2022)
Lukas Balles, Giovanni Zappella, Cédric Archambeau
Gradient-matching coresets for continual learning. CoRR (2021)
Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. ICBINB@NeurIPS (2020)
Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. CoRR (2020)
Lukas Balles, Fabian Pedregosa, Nicolas Le Roux
The Geometry of Sign Gradient Descent. CoRR (2020)
Frederik Kunstner, Philipp Hennig, Lukas Balles
Limitations of the empirical Fisher approximation for natural gradient descent. NeurIPS (2019)
Anurag Ranjan, Varun Jampani, Lukas Balles, Kihwan Kim, Deqing Sun, Jonas Wulff, Michael J. Black
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation. CVPR (2019)
Frank Schneider, Lukas Balles, Philipp Hennig
DeepOBS: A Deep Learning Optimizer Benchmark Suite. ICLR (Poster) (2019)
Frederik Kunstner, Lukas Balles, Philipp Hennig
Limitations of the Empirical Fisher Approximation. CoRR (2019)
Frank Schneider, Lukas Balles, Philipp Hennig
DeepOBS: A Deep Learning Optimizer Benchmark Suite. CoRR (2019)
Lukas Balles, Philipp Hennig
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients. ICML (2018)
Lukas Balles, Philipp Hennig
Follow the Signs for Robust Stochastic Optimization. CoRR (2017)
Lukas Balles, Javier Romero, Philipp Hennig
Coupling Adaptive Batch Sizes with Learning Rates. UAI (2017)
Maren Mahsereci, Lukas Balles, Christoph Lassner, Philipp Hennig
Early Stopping without a Validation Set. CoRR (2017)
Lukas Balles, Javier Romero, Philipp Hennig
Coupling Adaptive Batch Sizes with Learning Rates. CoRR (2016)