Coupling Adaptive Batch Sizes with Learning Rates. - Lukas Balles; Javier Romero 002; Philipp Hennig (UAI, 2017)

Follow the Signs for Robust Stochastic Optimization. - Lukas Balles; Philipp Hennig (CoRR, 2017)

Early Stopping without a Validation Set. - Maren Mahsereci; Lukas Balles; Christoph Lassner; Philipp Hennig (CoRR, 2017)

Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients. - Lukas Balles; Philipp Hennig (ICML, 2018)

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 (CVPR, 2019)

DeepOBS: A Deep Learning Optimizer Benchmark Suite. - Frank Schneider 001; Lukas Balles; Philipp Hennig (ICLR (Poster), 2019)

Limitations of the empirical Fisher approximation for natural gradient descent. - Frederik Kunstner; Philipp Hennig; Lukas Balles (NeurIPS, 2019)

Limitations of the Empirical Fisher Approximation. - Frederik Kunstner; Lukas Balles; Philipp Hennig (CoRR, 2019)

Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. - Ricky T. Q. Chen; Dami Choi; Lukas Balles; David Duvenaud; Philipp Hennig (ICBINB@NeurIPS, 2020)

The Geometry of Sign Gradient Descent. - Lukas Balles; Fabian Pedregosa; Nicolas Le Roux (CoRR, 2020)

Gradient-matching coresets for continual learning. - Lukas Balles; Giovanni Zappella; Cédric Archambeau (CoRR, 2021)

Gradient-Matching Coresets for Rehearsal-Based Continual Learning. - Lukas Balles; Giovanni Zappella; Cédric Archambeau (CoRR, 2022)

PASHA: Efficient HPO with Progressive Resource Allocation. - Ondrej Bohdal; Lukas Balles; Beyza Ermis; Cédric Archambeau; Giovanni Zappella (CoRR, 2022)

PASHA: Efficient HPO and NAS with Progressive Resource Allocation. - Ondrej Bohdal; Lukas Balles; Martin Wistuba; Beyza Ermis; Cédric Archambeau; Giovanni Zappella (ICLR, 2023)

Renate: A Library for Real-World Continual Learning. - Martin Wistuba; Martin Ferianc; 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)

A Negative Result on Gradient Matching for Selective Backprop. - Lukas Balles; Cédric Archambeau; Giovanni Zappella (CoRR, 2023)

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)

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 Yuri Prince; Björn Deiseroth; Andrés Felipe Cruz-Salinas; Carlo Luschi; Samuel Weinbach; Douglas Orr (ICLR, 2025)

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models. - Pit Neitemeier; Björn Deiseroth; Constantin Eichenberg; Lukas Balles (ICLR, 2025)