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 Differentiable 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, Fabio Ferreira, Sebastian Pineda Arango, 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
• PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applications
• Ricardo Knauer, Marvin Grimm, Erik Rodner