Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
publications
Explaining neural networks without access to training data
Published in Machine Learning, 2024
We extend Interpretation Nets (I-Nets), which interpret models without training data, to decision trees, enhancing model interpretability when data is inaccessible.
Recommended citation: Marton, Sascha, et al. (2024). "Explaining neural networks without access to training data." Machine Learning. 113(6).
Download Paper | Download Slides
GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent
Published in AAAI Conference on Artificial Intelligence, 2024
A novel approach for learning hard, axis-aligned decision trees with gradient descent.
Recommended citation: Marton, Sascha, et al. (2024). "GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data." Proceedings of the AAAI Conference on Artificial Intelligence. 38(13).
Download Paper | Download Slides
GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data
Published in International Conference on Learning Representations, 2024
A novel ensemble method for hard, axis-aligned decision trees learned end-to-end with gradient descent.
Recommended citation: Marton, Sascha, et al. (2024). "GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data." The Twelfth International Conference on Learning Representations. 1(1).
Download Paper | Download Slides
Decision Trees That Remember: Gradient-Based Learning of Recurrent Decision Trees with Memory
Published in New Frontiers in Associative Memory workshop at ICLR 2025, 2025
We propose ReMeDe trees, a recurrent decision tree architecture with internal memory, enabling efficient learning for sequential data through hard, axis-aligned decision rules trained via gradient descent.
Recommended citation: Marton, Sascha, et al. (2025). "Decision Trees That Remember: Gradient-Based Learning of Recurrent Decision Trees with Memory." New Frontiers in Associative Memory workshop at ICLR 2025. 1(1).
Download Paper
Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization
Published in International Conference on Learning Representations (Spotlight), 2025
We propose a novel method for symbolic RL that enables end-to-end gradient-based learning of interpretable, axis-aligned decision trees, combining policy gradient optimization with symbolic decision-making.
Recommended citation: Marton, Sascha, et al. (2025). "Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization." The Thirteenth International Conference on Learning Representations. 1(1).
Download Paper | Download Slides
talks
Explaining neural networks without access to training data
Published:
We consider generating explanations for neural networks in cases where the network’s training data is not accessible, for instance due to privacy or safety issues. Recently, Interpretation Nets (I-Nets) have been proposed as a sample-free approach to post-hoc, global model interpretability that does not require access to training data. They formulate interpretation as a machine learning task that maps network representations (parameters) to a representation of an interpretable function. In this paper, we extend the I-Net framework to the cases of standard and soft decision trees as surrogate models. We propose a suitable decision tree representation and design of the corresponding I-Net output layers. Furthermore, we make I-Nets applicable to real-world tasks by considering more realistic distributions when generating the I-Net’s training data. We empirically evaluate our approach against traditional global, post-hoc interpretability approaches and show that it achieves superior results when the training data is not accessible.
GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data
Published:
Despite the success of deep learning for text and image data, tree-based ensemble models are still state-of-the-art for machine learning with heterogeneous tabular data. However, there is a significant need for tabular-specific gradient-based methods due to their high flexibility. In this paper, we propose GRANDE, GRAdieNt-based Decision tree Ensembles, a novel approach for learning hard, axis-aligned decision tree ensembles using end-to-end gradient descent. GRANDE is based on a dense representation of tree ensembles, which affords to use backpropagation with a straight-through operator to jointly optimize all model parameters. Our method combines axis-aligned splits, which is a useful inductive bias for tabular data, with the flexibility of gradient-based optimization. Furthermore, we introduce an advanced instance-wise weighting that facilitates learning representations for both, simple and complex relations, within a single model. We conducted an extensive evaluation on a predefined benchmark with 19 classification datasets and demonstrate that our method outperforms existing gradient-boosting and deep learning frameworks on most datasets. The method is available under: https://github.com/s-marton/GRANDE