Poster Schedule
Tuesday
All papers from Sessions I, II & III will present a poster in this slot. Additionally the following non-archival content will be presented:
- MetaARIMA: Automatic Configuration of ARIMA using Classifier Chains
- Quickly Tuning Foundation Models for Image Segmentation
- A Preliminary Evaluation of Large Language Models for Data Science Code Generation
- Cost-aware Stopping for Bayesian Optimization
- Stitching Disparate Neural Network Layers with Complex Adapters and Spatial Rescaling
- Successive Halving with Learning Curve Prediction via Latent Kronecker Gaussian Processes
- Improved Gaussian Process Hyperparameter Fitting for Bayesian Optimization
- LISTEN to Your Preferences: An LLM Framework for Multi-Objective Selection
- Cost-aware Bayesian Optimization via the Pandora’s Box Gittins Index
Wednesday
All papers from Sessions IV & V will present a poster in this slot. Additionally the following non-archival content will be presented:
- Quantifying Module Interactions in the PSO-X Framework
- Bayesian Optimisation Against Climate Change: Applications and Benchmarks
- Zero-Cost Benchmarks: Towards Lower Reliance on Spearman Rank Correlation
- Stress Testing Classifiers around the Decision Boundary with Latent Diffusion
- The Gittins Index: A Design Principle for Decision-Making Under Uncertainty
- Tune My Adam, Please!
- Multi-objective Hyperparameter Optimization in the Age of Deep Learning
- How Usable is Automated Feature Engineering for Tabular Data?
- Automated Data Preparation for Machine Learning
- Surrogate Benchmarks for Model Merging Optimization
- ParticleML: AutoML Through Electromagnetic Physics Simulation
- Prometheus: A Recursively Self-Improving NAS System
- Data-Efficient Ranking of Recommendation Models
- Object-Flow Machine Learning: Active learning framework utilizing protocols information
- Algorithm Configuration for Structured Pfaffian Settings
Online Only (September 25th, 8:00 – 12:00 EST)
For information about the online post-conference gathering please checkout this page.
- ReLU is all you need for NASWOT
- Towards Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization
- Towards Exploiting Early Termination for Multi-Fidelity Hyperparameter Optimization
Accepted Non-Archival Content
Papers marked with 🛜 will be presented online only in the
virtual post-conference gathering.
🎨 Non-traditional content
🔥 Hot-of-the-press
📔 Short papers
- 🎨 Conversational AutoMLOPs
Paulito Pedregosa Palmes
OpenReview - 📔 MetaARIMA: Automatic Configuration of ARIMA using Classifier Chains
Vitor Cerqueira, Ricardo Inácio, Carlos Soares
OpenReview - 🛜📔 Towards Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization
Lukas Fehring, Maximilian Spliethöver, Marcel Wever, Henning Wachsmuth, Marius Lindauer
OpenReview - 🔥 Algorithm Configuration for Structured Pfaffian Settings
Maria Florina Balcan, Anh Tuan Nguyen, Dravyansh Sharma
OpenReview
- 📔 Quickly Tuning Foundation Models for Image Segmentation
Breenda Das, Lennart Purucker, Timur Carstensen, Frank Hutter
OpenReview - 📔 A Preliminary Evaluation of Large Language Models for Data Science Code Generation
Farshad Ghorbanishovaneh, Lars Kotthoff
OpenReview - 📔 Cost-aware Stopping for Bayesian Optimization
Qian Xie , Linda Cai, Alexander Terenin, Peter I. Frazier, Ziv Scully
OpenReview - 📔 Stitching Disparate Neural Network Layers with Complex Adapters and Spatial Rescaling
Neil Traft, Nick Cheney
OpenReview - 🛜📔 ReLU is all you need for NASWOT
Prit Kanadiya, Raghav Agarwal, Om Doiphode, Sandip Shingade
OpenReview - 📔 Successive Halving with Learning Curve Prediction via Latent Kronecker Gaussian Processes
Jihao Andreas Lin, Nicolas Mayoraz, Steffen Rendle, Dima Kuzmin, Emil Praun, Berivan Isik
OpenReview - 📔 Improved Gaussian Process Hyperparameter Fitting for Bayesian Optimization
Bobby Huggins, Roman Garnett
OpenReview - 📔 LISTEN to Your Preferences: An LLM Framework for Multi-Objective Selection
Adam Jovine, Tinghan Ye, David Shmoys, Peter I. Frazier
OpenReview - 🛜📔 Towards Exploiting Early Termination for Multi-Fidelity Hyperparameter Optimization
Helena Graf, Lukas Fehring, Tanja Tornede, Alexander Tornede, Marcel Wever, Marius Lindauer
OpenReview - 📔 Quantifying Module Interactions in the PSO-X Framework
Christian Leonardo Camacho-Villalón, Ana Nikolikj, Katharina Dost, Eva Tuba, Saso Dzeroski, Tome Eftimov
OpenReview - 🔥 Bayesian Optimisation Against Climate Change: Applications and Benchmarks
Sigrid Passano Hellan, Christopher G. Lucas, Nigel H. Goddard
OpenReview - 📔 Zero-Cost Benchmarks: Towards Lower Reliance on Spearman Rank Correlation
Timotée Ly-Manson, Mathieu Léonardon, Abdeldjalil Aissa El Bey
OpenReview - 🔥 Cost-aware Bayesian Optimization via the Pandora’s Box Gittins Index
Qian Xie, Raul Astudillo, Peter I. Frazier, Ziv Scully, Alexander Terenin
OpenReview - 📔 Stress Testing Classifiers around the Decision Boundary with Latent Diffusion
Inês Gomes, André Restivo, Moisés Rocha dos Santos, Carlos Soares, Jan N. van Rijn, Luis Filipe Teixeira
OpenReview - 🔥 The Gittins Index: A Design Principle for Decision-Making Under Uncertainty
Ziv Scully, Alexander Terenin
OpenReview - 📔 Tune My Adam, Please!
Theodoros Athanasiadis, Steven Adriaensen, Samuel Müller, Frank Hutter
OpenReview - 📔 Multi-objective Hyperparameter Optimization in the Age of Deep Learning
Soham Basu, Danny Stoll
OpenReview - 📔 How Usable is Automated Feature Engineering for Tabular Data?
Bastian Schäfer, Lennart Purucker, Maciej Janowski, Frank Hutter
OpenReview - 🎨 Automated Data Preparation for Machine Learning
Sasa Mladenovic, Marius Lindauer, Carola Doerr
OpenReview - 📔 Surrogate Benchmarks for Model Merging Optimization
Rio Akizuki, Yuya Kudo, Nozomu Yoshinari, Yoichi Hirose, Toshiyuki Nishimoto, Kento Uchida, Shinichi Shirakawa
OpenReview - 🎨 ParticleML: AutoML Through Electromagnetic Physics Simulation
Arya Manjaramkar
OpenReview - 📔 Prometheus: A Recursively Self-Improving NAS System
Alex Zhang, Hui Liu
OpenReview - 📔 Data-Efficient Ranking of Recommendation Models
Berivan Isik, Matthew Fahrbach, Dima Kuzmin, Nicolas Mayoraz, Emil Praun, Steffen Rendle, Raghavendra Vasudeva
OpenReview - 🎨 Object-Flow Machine Learning: Active learning framework utilizing protocols information
Yusuke Ozaki, Kazunari Kaizu, Koichi Takahashi
OpenReview