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
Aleksandra Płońska, 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 ModelsBest Paper
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 ClusteringBest Paper Runner-up
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 EmbeddingsBest Paper Runner-up
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 SamplingBest Paper
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
An Open-Source Training Dataset for Foundation Models for Hyperparameter Optimization
Aaron Klein, Herilalaina Rakotoarison, Luca Thale-Bombien, David Salinas
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, Martin Molan
Efficient Leakage-Free Neural Architecture Search under Leave-One-Subject-Out Evaluation
Heinke Hihn
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, Andreas Paraskeva, Jan N. van Rijn, Elliot J. Crowley, Henry Gouk
Hyperparameter Optimization in the Age of Large Models
Erlis Lushtaku, Ali Elganzory, Danny Stoll
Invariance of Violation-Ranking Maps in Constrained Black-Box Optimization
Kaichi Irie, Shuhei Watanabe, Kaito Baba
Learning to Solve and Optimize by Evolving Code
Veronika Semmelrock, Benedetta Strizzolo, Francesco Zuccato, Gerhard Friedrich, Patrick Rodler, Konstantin Schekotihin
MOEns: Multi-Objective Post-Hoc Ensembling for Predictive Accuracy and Adversarial Robustness in AutoML
Berkay Akay, Marie Anastacio, Jan N. van Rijn, Holger Hoos
Search More or Confirm Better? Reliable Selection in Small-Budget LoRA Hyperparameter Optimization
Pece Georgiev, Biljana Tojtovska
TabPFN-ScAn-Bench: A Surrogate Benchmark for Scaling Analysis Algorithms
Nastaran Alipour, Donat Sinani, Artin Sermaxhaj, Johannes Hog, Danny Stoll
Towards Unifying AutoML: Introducing Neural Pipeline Search Spaces
Lum Birinxhiku, Anton Geburek, Steven Adriaensen, Danny Stoll
Hot-Off-the-Press Track
α-PFN: Fast Entropy Search via In-Context Learning
Herilalaina Rakotoarison, Steven Adriaensen, Tom Julian Viering, Carl Hvarfner, Samuel Müller, Frank Hutter, Eytan Bakshy
International Conference on Machine Learning (ICML)
https://openreview.net/forum?id=7Oonij8oLU
The Cost of Consensus: Isolated Self-Correction Prevails Over Unguided Homogeneous Multi-Agent Debate
Blaž Bertalanič, Carolina Fortuna
ACM Conference on AI and Agentic Systems (CAIS)
https://dl.acm.org/doi/10.1145/3786335.3813137
Semi-supervised learning from tabular data with autoencoders: when does it work?
Sintija Stevanoska, Jurica Levatić, Sašo Džeroski
Machine Learning Journal
https://link.springer.com/article/10.1007/s10994-025-06898-8
ShaplEIG: Bayesian Experimental Design for Shapley Value Estimation
David Rundel, Fabian Fumagalli, Maximilian Muschalik, Bernd Bischl, Matthias Feurer
International Conference on Machine Learning (ICML)
https://icml.cc/virtual/2026/poster/61009
FIT-GNN: Faster Inference Time for GNNs that ‘FIT’ in Memory Using Coarsening
Shubhajit Roy, Hrriday Ruparel, Kishan Ved, Anirban Dasgupta
Transactions on Machine Learning Research (TMLR)
https://openreview.net/forum?id=g7r7y2I7Sz