:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Marton, Sascha
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2603.11117
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent
by: Marton, Sascha, et al.
Published: (2023)

Decision Trees That Remember: Gradient-Based Learning of Recurrent Decision Trees with Memory
by: Marton, Sascha, et al.
Published: (2025)

GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data
by: Marton, Sascha, et al.
Published: (2023)

Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization
by: Marton, Sascha, et al.
Published: (2024)

Towards Learning Stochastic Population Models by Gradient Descent
by: Kreikemeyer, Justin N., et al.
Published: (2024)

Distributed Gradient Descent for Functional Learning
by: Yu, Zhan, et al.
Published: (2023)

A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data
by: Tschalzev, Andrej, et al.
Published: (2024)

Occam Gradient Descent
by: Kausik, B. N.
Published: (2024)

Learning Associative Memories with Gradient Descent
by: Cabannes, Vivien, et al.
Published: (2024)

In-context Learning and Gradient Descent Revisited
by: Deutch, Gilad, et al.
Published: (2023)

Bolstering Stochastic Gradient Descent with Model Building
by: Birbil, S. Ilker, et al.
Published: (2021)

Partially Lazy Gradient Descent for Smoothed Online Learning
by: Mhaisen, Naram, et al.
Published: (2026)

Personalized Federated Learning with Exact Stochastic Gradient Descent
by: Nikoloutsopoulos, Sotirios, et al.
Published: (2022)

Gradient Descent with Provably Tuned Learning-rate Schedules
by: Sharma, Dravyansh
Published: (2025)

Learning Curves of Stochastic Gradient Descent in Kernel Regression
by: Zhang, Haihan, et al.
Published: (2025)

Stochastic Adaptive Gradient Descent Without Descent
by: Aujol, Jean-François, et al.
Published: (2025)

Stacking as Accelerated Gradient Descent
by: Agarwal, Naman, et al.
Published: (2024)

Corner Gradient Descent
by: Yarotsky, Dmitry
Published: (2025)

Learning Provably Improves the Convergence of Gradient Descent
by: Song, Qingyu, et al.
Published: (2025)

Enhancing Fractional Gradient Descent with Learned Optimizers
by: Sobotka, Jan, et al.
Published: (2025)

Hybrid Coordinate Descent for Efficient Neural Network Learning Using Line Search and Gradient Descent
by: Hsiao, Yen-Che, et al.
Published: (2024)

Is All Learning (Natural) Gradient Descent?
by: Shoji, Lucas, et al.
Published: (2024)

Vanilla Gradient Descent for Oblique Decision Trees
by: Panda, Subrat Prasad, et al.
Published: (2024)

Attacking Large Language Models with Projected Gradient Descent
by: Geisler, Simon, et al.
Published: (2024)

Compact Rule-Based Classifier Learning via Gradient Descent
by: Fumanal-Idocin, Javier, et al.
Published: (2025)

Interactive Learning of Single-Index Models via Stochastic Gradient Descent
by: Rajaraman, Nived, et al.
Published: (2026)

Which LIME should I trust? Concepts, Challenges, and Solutions
by: Knab, Patrick, et al.
Published: (2025)

Interpreting Outliers in Time Series Data through Decoding Autoencoder
by: Knab, Patrick, et al.
Published: (2024)

On the Convergence of Gradient Descent for Large Learning Rates
by: Crăciun, Alexandru, et al.
Published: (2024)

Continuum Transformers Perform In-Context Learning by Operator Gradient Descent
by: Mishra, Abhiti, et al.
Published: (2025)

A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent
by: Wang, Shuche, et al.
Published: (2024)

Robust Gradient Descent for Phase Retrieval
by: Buna, Alex, et al.
Published: (2024)

On the Generalization of Stochastic Gradient Descent with Momentum
by: Ramezani-Kebrya, Ali, et al.
Published: (2018)

Gauss-Newton Natural Gradient Descent for Shape Learning
by: King, James, et al.
Published: (2026)

Elastic Multi-Gradient Descent for Parallel Continual Learning
by: Lyu, Fan, et al.
Published: (2024)

How Transformers Learn Causal Structure with Gradient Descent
by: Nichani, Eshaan, et al.
Published: (2024)

On the Theory of Continual Learning with Gradient Descent for Neural Networks
by: Taheri, Hossein, et al.
Published: (2025)

The Initialization Determines Whether In-Context Learning Is Gradient Descent
by: Xie, Shifeng, et al.
Published: (2025)

Conflict-Averse Gradient Descent for Multi-task Learning
by: Liu, Bo, et al.
Published: (2021)

On the Convergence of Gradient Descent on Learning Transformers with Residual Connections
by: Qin, Zhen, et al.
Published: (2025)