Methods Track

• Sequence Alignment-based Similarity Metric in Evolutionary Neural Architecture Search
Mateo Avila Pava, René Groh, Andreas M. Kist

• FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search
Jordan Dotzel, Gang Wu, Andrew Li, Muhammad Umar, Yun Ni, Mohamed S. Abdelfattah, Zhiru Zhang, Liqun Cheng, Martin G. Dixon, Norman P Jouppi, Quoc V. Le, Sheng Li

Don’t Waste Your Time: Early Stopping Cross-Validation
Edward Bergman, Lennart Purucker, Frank Hutter

Training and Cross-Validating Machine Learning Pipelines with Limited Memory
Martin Hirzel, Kiran Kate, Louis Mandel, Avraham Shinnar

Analyzing Few-Shot Neural Architecture Search in a Metric-Driven Framework
Timotée Ly-Manson, Mathieu Léonardon, Abdeldjalil Aissa El Bey, Ghouthi Boukli Hacene, Lukas Mauch

Confidence Interval Estimation of Predictive Performance in the Context of AutoML
Konstantinos Paraschakis, Andrea Castellani, Giorgos Borboudakis, Ioannis Tsamardinos

Is Mamba Capable of In-Context Learning?
Riccardo Grazzi, Julien Niklas Siems, Simon Schrodi, Thomas Brox, Frank Hutter

ASML: A Scalable and Efficient AutoML Solution for Data Streams
Nilesh Verma, Albert Bifet, Bernhard Pfahringer, Maroua Bahri

Bi-Level One-Shot Architecture Search for Probabilistic Time Series Forecasting
Fabian Kalter, Jonas Seng, Zhongjie Yu, Fabrizio Ventola, Kristian Kersting

Improving Transfer Learning by means of Ensemble Learning and Swarm Intelligence-based Neuroevolution
Adri Gómez

HPOD: Hyperparameter Optimization for Unsupervised Outlier Detection
Yue Zhao, Leman Akoglu

Speeding up NAS with Adaptive Subset Selection
Vishak Prasad C, Colin White, Sibasis Nayak, Paarth Jain, Aziz Shameem, Prateek Garg, Ganesh Ramakrishnan

Weight-Entanglement Meets Gradient-Based Neural Architecture Search
Rhea Sanjay Sukthanker, Arjun Krishnakumar, Mahmoud Safari, Frank Hutter

ABCD Track

TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications
David Salinas, Nick Erickson

HPO-RL-Bench: A Zero-Cost Benchmark for HPO in Reinforcement Learning
Gresa Shala, Sebastian Pineda Arango, André Biedenkapp, Frank Hutter, Josif Grabocka

Fast Benchmarking of Asynchronous Multi-Fidelity Optimization on Zero-Cost Benchmarks
Shuhei Watanabe, Neeratyoy Mallik, Edward Bergman, Frank Hutter

Introducing HoNCAML: Holistic No-Code Auto Machine Learning
Luca Piras, Joan Albert Erráez Castelltort, Jordi Casals Grifell, Xavier de Juan Pulido, Cirus Iniesta, Marina Rosell Murillo, Cristina Soler Arenys

Automated Deep Learning for load forecasting
Julie Keisler, Sandra Claudel, Gilles Cabriel, Margaux Brégère

AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models
Zhiqiang Tang, Haoyang Fang, Su Zhou, Taojiannan Yang, Zihan Zhong, Cuixiong Hu, Katrin Kirchhoff, George Karypis

Journal Track

Controlling Federated Learning for Covertness
Adit Jain, Vikram Krishnamurthy

AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks
Alexander Tornede, Difan Deng, Theresa Eimer, Joseph Giovanelli, Aditya Mohan, Tim Ruhkopf, Sarah Segel, Daphne Theodorakopoulos, Tanja Tornede, Henning Wachsmuth, Marius Lindauer

Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
Hilde Jacoba Petronella Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter

AMLB: an AutoML Benchmark
Pieter Gijsbers, Marcos L P Bueno, Stefan Coors, Erin LeDell, Janek Thomas, Bernd Bischl, Joaquin Vanschoren

Workshop Track

Automated Prior Elicitation from Large Language Models for Bayesian Logistic Regression
Henry Gouk, Boyan Gao

In-Context Learning for Latency Estimation
Timur Carstensen, Thomas Elsken, Martin Rapp

Fine-Tuning LLMs for Automated Feature Engineering
Yoichi Hirose, Kento Uchida, Shinichi Shirakawa

LoRA-DARTS: Low Rank Adaptation for Differential Architecture Search
Arjun Krishnakumar, Abhash Kumar Jha, Shakiba Moradian, Martin Rapp, Frank Hutter

In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
Herilalaina Rakotoarison, Steven Adriaensen, Neeratyoy Mallik, Samir Garibov, Edward Bergman, Frank Hutter

From Epoch to Sample Size: Developing New Data-driven Priors for Learning Curve Prior-Fitted Networks
Tom Julian Viering, Steven Adriaensen, Herilalaina Rakotoarison, Frank Hutter

Cost-Efficient Training for Automated Algorithm Selection
Erdem Kuş, Nguyen Dang, Ozgur Akgun, Ian Miguel

Efficient Search for Customized Activation Functions With Gradient Descent
Lukas Strack, Mahmoud Safari, Frank Hutter

LMEMs for post-hoc analysis of HPO Benchmarking
Anton Merlin Geburek, Neeratyoy Mallik, Danny Stoll, Xavier Bouthillier, Frank Hutter

Beyond Graph-Based Modeling for Hierarchical Neural Architecture Search
Lum Birinxhiku, Danny Stoll, Simon Schrodi, Frank Hutter

einspace: Searching for Neural Architectures from Fundamental Operations
Linus Ericsson, Miguel Espinosa, Chenhongyi Yang, Antreas Antoniou, Amos Storkey, Shay B. Cohen, Steven McDonagh, Elliot J. Crowley

Rethinking of Encoder-based Warm-start Methods in Hyperparameter Optimization
Dawid Płudowski, Antoni Zajko, Katarzyna Woźnica, Anna Kozak

Fast Optimizer Benchmark
Simon Blauth, Tobias Bürger, Zacharias Häringer, Jörg K.H. Franke, Frank Hutter

FairPFN: Transformers Can Do Counterfactual Fairness
Jake Robertson, Noah Hollmann, Noor Awad, Frank Hutter

Graph is All You Need? Lightweight Data-agnostic Neural Architecture Search without Training
Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen, Chunheng Jiang, Jianxi Gao

CANDID DAC: Leveraging Coupled Action Dimensions with Importance Differences in DAC
Philipp Bordne, M Asif Hasan, Edward Bergman, Noor Awad, André Biedenkapp

Do Tree-based Models Need Data Preprocessing?
Hubert Ruczyński, Anna Kozak

Quick-Tune-Tool: A Practical Tool and its User Guide for Automatically Finetuning Pretrained Models
Ivo Rapant, Lennart Purucker, Sebastian Pineda Arango, Fabio Ferreira, Arlind Kadra, Josif Grabocka, Frank Hutter

Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and Cost
Jannis Maier, Lennart Purucker, Felix Möller

NOSBench-101: Towards Reproducible Neural Optimizer Search
Goktug Karakasli, Steven Adriaensen, Frank Hutter

Beyond the Threshold: Time Is All You Need
Stefan Dendorfer, Andreas M. Kist

FEATHERS: Federated Architecture and Hyperparameter Search
Jonas Seng, Pooja Prasad, Devendra Singh Dhami, Martin Mundt, Kristian Kersting

Sample-Efficient Bayesian Optimization with Transfer Learning for Heterogeneous Search Spaces
Aryan Deshwal, Sait Cakmak, Yuhou Xia, David Eriksson

Cost-Sensitive Multi-Fidelity Bayesian Optimization with Transfer of Learning Curve Extrapolation
Dong Bok Lee, Aoxuan Silvia Zhang, Byungjoo Kim, Junhyeon Park, Juho Lee, Sung Ju Hwang, Hae Beom Lee

Drift-Resilient TabPFN: In-Context Learning Distribution Shifts on Tabular Data
Kai Helli, David Schnurr, Noah Hollmann, Samuel Müller, Frank Hutter

Selecting Pre-trained Models for Transfer Learning with Data-centric Meta-features
Matt van den Nieuwenhuijzen, Carola Doerr, Henry Gouk, Jan N. van Rijn

Automated Federated Learning via Informed Pruning
Christian Internò, Elena Raponi, Niki van Stein, Thomas Bäck, Markus Olhofer, Yaochu Jin, Barbara Hammer

Investigating the Impact of Hard and Easy Samples on Generalization Reveals In-class Data Imbalance
Pawel Pukowski, Haiping Lu

AutoCD: Automated Machine Learning for Causal Discovery Algorithms
Gerlise Chan, Tom Claassen, Holger Hoos, Tom Heskes, Mitra Baratchi

Hyperparameter Optimization via Interacting with Probabilistic Circuits
Jonas Seng, Fabrizio Ventola, Zhongjie Yu, Kristian Kersting

Benchmarking AutoML Clustering Frameworks
Matheus Camilo da Silva, Biagio Licari, Gabriel Marques Tavares, Sylvio Barbon Junior

Bibat: Batteries-Included Bayesian Analysis Template
Teddy Groves

TabMini: A Tabular Classification Benchmark Suite for Data-Scarce Applications
Ricardo Knauer, Marvin Grimm, Erik Rodner