ABCD Track
ID 3: BBOmix: A Tabular Benchmark for Hyperparameter Optimization in Unsupervised Biological Representation Learning
Luca Thale-Bombien, Jan Ewald, Ralf König, Aaron Klein
ID 5: SAMLB: A Streaming AutoML Benchmark
Nilesh Verma, Albert Bifet, Bernhard Pfahringer, Maroua Bahri
ID 8: MLJAR AutoML: Transparent and Fairness-Aware AutoML for Tabular Data with Automatic Documentation
Piotr Płoński
ID 11: OpenGNS: An Open Dataset of the Gradient Noise Scale across Vision, Language, and Diffusion Models
Marcel Aach, Jiangtao Wang, Stefan Kesselheim, Andreas Lintermann
ID 12: Does Preprocessing Quality Matter for AutoML? A Case Study on Time Series Forecasting with Environmental Data
Guilherme Castro Dallasta, Jan N. van Rijn, Andre Carlos Ponce de Leon Ferreira De Carvalho
ID 14: SNAC-Pack 2.0: Scaled-Out Surrogate Neural Architecture Codesign
Jason Weitz, Dmitri Demler, Benjamin Hawks, Aaron Wang, Nhan Tran, Javier Duarte
ID 15: Equally Good, Yet Different: Benchmarking Rashomon sets in AutoML packages
Katarzyna Woźnica, Katarzyna Rogalska, Zuzanna Sieńko, Mustafa Cavus
ID 16: whittle: A Library for Sub-Network Extraction from Large Language Models
Arjun Krishnakumar, Rhea Sanjay Sukthanker, Hannan Javed Mahadik, Gabriela Kadlecová, Vladyslav Moroshan, Timur Carstensen, Frank Hutter, Aaron Klein
ID 17: Tabular-DC-Bench: A Large-Scale Meta-Dataset for Hyperparameter Optimization in Deep Clustering
Mamdouh Aljoud, Gabriel Marques Tavares, Sandra Gilhuber, Collin Leiber, Thomas Seidl
ID 19: NATS-Bench-MCU: A Tabular Hardware Benchmark for Neural Architecture Search on Microcontrollers
Sebastian Zimmermann, René Groh, Andreas M Kist
Methods Track
ID 6: FS-DCM: Frequency-Separated Dual-Context Modeling with Dynamic Local Volatility Weighting for Time-Series AutoML
Jonghyun Lee, Moonsu Kim
ID 12: Algorithm Selection with Zero Domain Knowledge via Text Embeddings
Stefan Szeider
ID 14: A Large-Scale Study of Overtuning Mitigation Strategies in Hyperparameter Optimization
Sietse Schröder, Zubin Zellmann, Matthias Feurer, Mitra Baratchi, Jan N. van Rijn, Bernd Bischl
ID 16: Evolutionary Architecture Search Through Grammar-Based Sequence Alignment
Adri Gómez, Felix Möller, Steven McDonagh, Monica Abella, Manuel Desco, Elliot J. Crowley, Aaron Klein, Linus Ericsson
ID 17: Towards Predicting Hyperparameter Importance from Dataset Meta-Features
Maha Ksouri, Daphne Theodorakopoulos, Marius Lindauer
ID 18: Synthesizing Feature Extractors: An Agentic Approach for Algorithm Selection
Hai Xia, Carlos Ansótegui, Stefan Szeider
ID 20: TACTICL: Task-Aware Compression of Tabular ICL Models
Mykhailo Koshil, Matthias Feurer, Katharina Eggensperger
ID 31: Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization
Lukas Fehring, Marcel Wever, Maximilian Spliethöver, Leona Hennig, Henning Wachsmuth, Marius Lindauer
ID 33: ONNX-Net: Towards Universal Representations and Instant Performance Prediction for Neural Architectures
Shiwen Qin, Alexander Auras, Shay B Cohen, Elliot J. Crowley, Michael Moeller, Linus Ericsson, Jovita Lukasik
ID 35: Transfer Learning of Robustness for Image Classification: An Experimental Study using Robustness Distributions
David P. Wünsch, Annelot Willemijn Bosman, Holger H. Hoos, Jan N. van Rijn
ID 37: Efficient ensemble inference
Nick Kocher, Janek Paeßens, Anja Jankovic, Holger H. Hoos
ID 39: Large Language Models for Automated Cross-Domain Machine Learning Task Type Identification: A Benchmark Dataset and Evaluation
Petros Tsialis, Steffen Limmer, Tobias Rodemann, Martin Heckmann
ID 44: Zero-Shot Bayesian Optimization with TabPFN: Competitive with State-of-the-Art without Per-Task Training
Theodore Rogers, Srividya Ponnada
ID 49: IBUS: Overcoming Structural Biases in Hierarchical NAS with Iterative Bottom-Up Sampling
Abay Artykbayev, Martin Rapp, Benedikt Staffler, Margret Keuper
ID 51: From Tables to Runtime: Predicting Algorithm Runtime Distributions with TabPFN
Hagverdi Ibrahimli, Katharina Eggensperger, Steven Adriaensen
Late-Breaking Abstract Track
Auto-prepper: An Automated Data Preparation Toolkit for Real-world AutoML
Sasa Mladenovic, Marius Lindauer, Carola Doerr
Automated Structure Learning for Predicting Influence Spread from Observed Cascades
Matic Požar, Miklós Krész
Automating Privacy-Constrained RAG Pipelines with AIP-1 and AIP-2
Gregor Molan
CarBOHB: Carbon-Aware Bayesian Optimisation with Hyperband for Sustainable Automated Machine Learning
Oscar Licciardi, Marc Ravaine
Fail-Closed Gates: Automated Adversarial Verification for LLM-Agent Research Pipelines
Robert Sneiderman
Flow of solution procedure in DARTS for robust neural architecture search
Yitong Guo, Marie Anastacio, Jan N. van Rijn, Holger Hoos
Heterogeneous Structured Pruning under Resource Constraints for Hardware Efficient LLMs
Cameron Barker
MOEns: Multi-Objective Post-Hoc Ensembling for Predictive Accuracy and Adversarial Robustness in AutoML
Berkay Akay, Marie Anastacio, Jan N. van Rijn, Holger Hoos
Resource-Efficient Iterative LLM-Based Neural Architecture Search with Feedback Memory
Xiaojie Gu, Dmitry Ignatov, Radu Timofte
TabPFN-ScAn-Bench: A Surrogate Benchmark for Scaling Analysis Algorithms
Nastaran Alipour, Donat Sinani, Artin Sermaxhaj, Johannes Hog, Danny Stoll
The Repairability Gap: Why Uncertainty Is Not Enough for Model Selection Utility
Mohammed Karim, Pooyan Jamshidi
Towards Unifying AutoML: Introducing Neural Pipeline Search Spaces
Lum Birinxhiku, Anton Geburek, Steven Adriaensen, Danny Stoll