1 citations · 1 across the 5 of their papers we have counts for
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Tune My Adam, Please!
Theodoros Athanasiadis, Steven Adriaensen, Samuel Müller +1
The Adam optimizer remains one of the most widely used optimizers in deep learning, and effectively tuning its hyperparameters is key to optimizing performance. However, tuning can…
Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data
Anurag Garg, Muhammad Ali, Noah Hollmann +3
Foundation models for tabular data, like TabPFN, achieve strong performance on small datasets when pre-trained solely on synthetic data. We show that this performance can be signif…
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data
Kai Helli, David Schnurr, Noah Hollmann +2
While most ML models expect independent and identically distributed data, this assumption is often violated in real-world scenarios due to distribution shifts, resulting in the deg…
Bayes' Power for Explaining In-Context Learning Generalizations
Samuel Müller, Noah Hollmann, Frank Hutter
Traditionally, neural network training has been primarily viewed as an approximation of maximum likelihood estimation (MLE). This interpretation originated in a time when training…
Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks
Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller +1
Learning curve extrapolation aims to predict model performance in later epochs of training, based on the performance in earlier epochs. In this work, we argue that, while the inher…
In-Loop Meta-Learning with Gradient-Alignment Reward
Samuel Müller, André Biedenkapp, Frank Hutter
At the heart of the standard deep learning training loop is a greedy gradient step minimizing a given loss. We propose to add a second step to maximize training generalization. To…