:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Athanasiadis, Theodoros, Adriaensen, Steven, Müller, Samuel, Hutter, Frank
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2508.19733
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Gompertz Linear Units: Leveraging Asymmetry for Enhanced Learning Dynamics
by: Das, Indrashis, et al.
Published: (2025)

In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
by: Rakotoarison, Herilalaina, et al.
Published: (2024)

Bayes' Power for Explaining In-Context Learning Generalizations
by: Müller, Samuel, et al.
Published: (2024)

From Tables to Time: Extending TabPFN-v2 to Time Series Forecasting
by: Hoo, Shi Bin, et al.
Published: (2025)

Causal Data Augmentation for Robust Fine-Tuning of Tabular Foundation Models
by: Bühler, Magnus, et al.
Published: (2026)

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks
by: Lee, Dongwoo, et al.
Published: (2025)

Transformers Can Do Bayesian Inference
by: Müller, Samuel, et al.
Published: (2021)

Extracting Moore Machines from Transformers using Queries and Counterexamples
by: Adriaensen, Rik, et al.
Published: (2024)

FairPFN: A Tabular Foundation Model for Causal Fairness
by: Robertson, Jake, et al.
Published: (2025)

Position: The Future of Bayesian Prediction Is Prior-Fitted
by: Müller, Samuel, et al.
Published: (2025)

Cost-Sensitive Freeze-thaw Bayesian Optimization for Efficient Hyperparameter Tuning
by: Lee, Dong Bok, et al.
Published: (2025)

Tuning LLM Judge Design Decisions for 1/1000 of the Cost
by: Salinas, David, et al.
Published: (2025)

Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data
by: Helli, Kai, et al.
Published: (2024)

Quickly Tuning Foundation Models for Image Segmentation
by: Das, Breenda, et al.
Published: (2025)

Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How
by: Arango, Sebastian Pineda, et al.
Published: (2023)

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data
by: Garg, Anurag, et al.
Published: (2025)

Model Stitching by Functional Latent Alignment
by: Athanasiadis, Ioannis, et al.
Published: (2025)

Prior Learning in Introspective VAEs
by: Athanasiadis, Ioannis, et al.
Published: (2024)

Can Muon Fine-tune Adam-Pretrained Models?
by: Qu, Xingyu, et al.
Published: (2026)

Hybrid Phenology Modeling for Predicting Temperature Effects on Tree Dormancy
by: van Bree, Ron, et al.
Published: (2025)

c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization
by: Watanabe, Shuhei, et al.
Published: (2022)

TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks
by: Feuer, Benjamin, et al.
Published: (2024)

To Measure or Not: A Cost-Sensitive, Selective Measuring Environment for Agricultural Management Decisions with Reinforcement Learning
by: Baja, Hilmy, et al.
Published: (2025)

A Few Moments Please: Scalable Graphon Learning via Moment Matching
by: Ramezanpour, Reza, et al.
Published: (2025)

Early Stopping Tabular In-Context Learning
by: Küken, Jaris, et al.
Published: (2025)

A General Framework for User-Guided Bayesian Optimization
by: Hvarfner, Carl, et al.
Published: (2023)

DP-Adam-AC: Privacy-preserving Fine-Tuning of Localizable Language Models Using Adam Optimization with Adaptive Clipping
by: Yang, Ruoxing
Published: (2025)

Pimp My LLM: Leveraging Variability Modeling to Tune Inference Hyperparameters
by: Zine, Nada, et al.
Published: (2026)

CaAdam: Improving Adam optimizer using connection aware methods
by: Genet, Remi, et al.
Published: (2024)

A Llama walks into the 'Bar': Efficient Supervised Fine-Tuning for Legal Reasoning in the Multi-state Bar Exam
by: Fernandes, Rean, et al.
Published: (2025)

EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Networks
by: Arbel, Michael, et al.
Published: (2025)

Agentic NL2SQL to Reduce Computational Costs
by: Jehle, Dominik, et al.
Published: (2025)

Efficient Search for Customized Activation Functions with Gradient Descent
by: Strack, Lukas, et al.
Published: (2024)

Large Language Models Engineer Too Many Simple Features For Tabular Data
by: Küken, Jaris, et al.
Published: (2024)

Don't Waste Your Time: Early Stopping Cross-Validation
by: Bergman, Edward, et al.
Published: (2024)

HomeAdam: Adam and AdamW Algorithms Sometimes Go Home to Obtain Better Provable Generalization
by: Huang, Feihu, et al.
Published: (2026)

Adam-HNAG: A Convergent Reformulation of Adam with Accelerated Rate
by: Yu, Yaxin, et al.
Published: (2026)

How Usable is Automated Feature Engineering for Tabular Data?
by: Schäfer, Bastian, et al.
Published: (2025)

nanoTabPFN: A Lightweight and Educational Reimplementation of TabPFN
by: Pfefferle, Alexander, et al.
Published: (2025)

Self-Correcting Bayesian Optimization through Bayesian Active Learning
by: Hvarfner, Carl, et al.
Published: (2023)