Presentation Schedule
Session I (Monday)
- AutoPDL: Automatic Prompt Optimization for LLM Agents
Claudio Spiess, Mandana Vaziri, Louis Mandel, Martin Hirzel
OpenReview – PDF - Hyperparameter Optimization via Interacting with Probabilistic Circuits
Jonas Seng, Fabrizio Ventola, Zhongjie Yu, Kristian Kersting
OpenReview – PDF - Feasibility-Driven Trust Region Bayesian Optimization
Paolo Ascia, Elena Raponi, Thomas Bäck, Duddeck
OpenReview – PDF - Transferrable Surrogates in Expressive Neural Architecture Search Spaces
Shiwen Qin, Gabriela Kadlecová, Martin Pilát, Shay B Cohen, Roman Neruda, Elliot J. Crowley, Jovita Lukasik, Linus Ericsson
OpenReview – PDF
Session II (Tuesday)
- CAPO: Cost-Aware Prompt Optimization
Tom Zehle, Moritz Schlager, Timo Heiß, Matthias Feurer
OpenReview – PDF – YouTube - Fast Bayesian Optimization of Function Networks with Partial Evaluations
Poompol Buathong, Peter I. Frazier
OpenReview – PDF - CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization
Jacob O Tørring, Carl Hvarfner, Luigi Nardi, Magnus Själander
OpenReview – PDF
Session III (Tuesday)
- Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimization
Sigrid Passano Hellan, Huibin Shen, Francois-Xavier Aubet, David Salinas, Aaron Klein
OpenReview – PDF - Iterative Monte Carlo Tree Search for Neural Architecture Search
Mehraveh Javan Roshtkhari, Matthew Toews, Marco Pedersoli
OpenReview – PDF - Ax: A Platform for Adaptive Experimentation
Miles Olson, Elizabeth Santorella, Louis C. Tiao, Sait Cakmak, Mia Garrard, Samuel Daulton, Zhiyuan Jerry Lin, Sebastian Ament, Bernard Beckerman, Eric Onofrey, Paschal Igusti, Cristian Lara, Benjamin Letham, Cesar Cardoso, Shiyun Sunny Shen, Andy Chenyuan Lin, Matthew Grange, Elena Kashtelyan, David Eriksson, Maximilian Balandat, Eytan Bakshy
OpenReview – PDF - syftr: Pareto-Optimal Generative AI
Alexander Conway, Debadeepta Dey, Stefan Hackmann, Matthew Hausknecht, Michael Douglas Schmidt, Mark Lewis Steadman, Nick Volynets
OpenReview – PDF – YouTube
Session IV (Wednesday)
- confopt: A Library for Implementation and Evaluation of Gradient-based One-Shot NAS Methods
Abhash Kumar Jha, Shakiba Moradian, Arjun Krishnakumar, Martin Rapp, Frank Hutter
OpenReview – PDF - PiML: Automated Machine Learning Workflow Optimisation using LLM Agents
Abhishek Chopde , Fardeen Pettiwala, Sankar Kirubananth, Sai Kiran Botla, Pachipulusu Ayyappa Kethan
OpenReview – PDF - What Makes Freezing Layers in Deep Neural Networks Effective? A Linear Separability Perspective
Collin Coil, Nick Cheney
OpenReview – PDF – YouTube
Session V (Wednesday)
- Frozen Layers: Memory-efficient Many-fidelity Hyperparameter Optimization
Timur Carstensen, Neeratyoy Mallik, Frank Hutter, Martin Rapp
OpenReview – PDF - Multi-layer Stack Ensembles for Time Series Forecasting
Nathanael Bosch, Oleksandr Shchur, Nick Erickson, Michael Bohlke-Schneider, Ali Caner Turkmen
OpenReview – PDF - Revisiting Learning Rate Control
Micha Henheik, Theresa Eimer, Marius Lindauer
OpenReview – PDF - Regularized Neural Ensemblers
Sebastian Pineda Arango, Maciej Janowski, Lennart Purucker, Arber Zela, Frank Hutter, Josif Grabocka
OpenReview – PDF - Auto-nnU-Net: Towards Automated Medical Image Segmentation
Jannis Becktepe, Leona Hennig, Steffen Oeltze-Jafra, Marius Lindauer
OpenReveiw – PDF
Online Only (September 25th, 8:00 – 12:00 EST)
For information about the online post-conference gathering please checkout this page.
- AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting
Keshav Das, Julie Keisler, Margaux Brégère, Amaury Durand
OpenReview – PDF - Overtuning in Hyperparameter Optimization
Lennart Schneider, Bernd Bischl, Matthias Feurer
OpenReview – PDF - EG-ENAS: Efficient and Generalizable Evolutionary Neural Architecture Search for Image Classification
Mateo Avila Pava, René Groh, Andreas M Kist
OpenReview – PDF – YouTube - The Ranking Trick: A Simple and Robust Alternative to Score-Based Regression for AutoML
Hernan Ceferino Vazquez, Jorge Sánchez, Verónica Bogado, Pucci Romero Tobias
OpenReveiw – PDF - SmartCal: A Novel Automated Approach to Classifier Probability Calibration
Mohamed Maher Abdelrahman, Osama Fayez Oun, Youssef Medhat, Mariam Magdy Elseedawy, Yara Mostafa Marei, Abdullah Ibrahim, Radwa Mohamed El Shawi
OpenReview – PDF – YouTube - Exploring One Million Machine Learning Pipelines: A Benchmarking Study
Edesio Alcobaça, Andre Carlos Ponce de Leon Ferreira De Carvalho
OpenReview – PDF
Papers marked with 🛜 will be presented online only in the virtual post-conference gathering.
Methods Track
- PiML: Automated Machine Learning Workflow Optimisation using LLM Agents
Abhishek Chopde , Fardeen Pettiwala, Sankar Kirubananth, Sai Kiran Botla, Pachipulusu Ayyappa Kethan
OpenReview – PDF - Feasibility-Driven Trust Region Bayesian Optimization
Paolo Ascia, Elena Raponi, Thomas Bäck, Duddeck
OpenReview – PDF - Iterative Monte Carlo Tree Search for Neural Architecture Search
Mehraveh Javan Roshtkhari, Matthew Toews, Marco Pedersoli
OpenReview – PDF - Frozen Layers: Memory-efficient Many-fidelity Hyperparameter Optimization
Timur Carstensen, Neeratyoy Mallik, Frank Hutter, Martin Rapp
OpenReview – PDF - Fast Bayesian Optimization of Function Networks with Partial Evaluations
Poompol Buathong, Peter I. Frazier
OpenReview – PDF - syftr: Pareto-Optimal Generative AI
Alexander Conway, Debadeepta Dey, Stefan Hackmann, Matthew Hausknecht, Michael Douglas Schmidt, Mark Lewis Steadman, Nick Volynets
OpenReview – PDF – YouTube - 🛜 The Ranking Trick: A Simple and Robust Alternative to Score-Based Regression for AutoML
Hernan Ceferino Vazquez, Jorge Sánchez, Verónica Bogado, Pucci Romero Tobias
OpenReview – PDF - Regularized Neural Ensemblers
Sebastian Pineda Arango, Maciej Janowski, Lennart Purucker, Arber Zela, Frank Hutter, Josif Grabocka
OpenReview – PDF - What Makes Freezing Layers in Deep Neural Networks Effective? A Linear Separability Perspective
Collin Coil, Nick Cheney
OpenReview – PDF – YouTube - 🛜 EG-ENAS: Efficient and Generalizable Evolutionary Neural Architecture Search for Image Classification
Mateo Avila Pava, René Groh, Andreas M Kist
OpenReview – PDF – YouTube - Hyperparameter Optimization via Interacting with Probabilistic Circuits
Jonas Seng, Fabrizio Ventola, Zhongjie Yu, Kristian Kersting
OpenReview – PDF - 🛜 SmartCal: A Novel Automated Approach to Classifier Probability Calibration
Mohamed Maher Abdelrahman, Osama Fayez Oun, Youssef Medhat, Mariam Magdy Elseedawy, Yara Mostafa Marei, Abdullah Ibrahim, Radwa Mohamed El Shawi
OpenReview – PDF – YouTube - AutoPDL: Automatic Prompt Optimization for LLM Agents
Claudio Spiess, Mandana Vaziri, Louis Mandel, Martin Hirzel
OpenReview – PDF - Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimization
Sigrid Passano Hellan, Huibin Shen, Francois-Xavier Aubet, David Salinas, Aaron Klein
OpenReview – PDF - Multi-layer Stack Ensembles for Time Series Forecasting
Nathanael Bosch, Oleksandr Shchur, Nick Erickson, Michael Bohlke-Schneider, Ali Caner Turkmen
OpenReview – PDF - Transferrable Surrogates in Expressive Neural Architecture Search Spaces
Shiwen Qin, Gabriela Kadlecová, Martin Pilát, Shay B Cohen, Roman Neruda, Elliot J. Crowley, Jovita Lukasik, Linus Ericsson
OpenReview – PDF - 🛜 Overtuning in Hyperparameter Optimization
Lennart Schneider, Bernd Bischl, Matthias Feurer
OpenReview – PDF - CAPO: Cost-Aware Prompt Optimization
Tom Zehle, Moritz Schlager, Timo Heiß, Matthias Feurer
OpenReview – PDF – YouTube - Revisiting Learning Rate Control
Micha Henheik, Theresa Eimer, Marius Lindauer
OpenReview – PDF
ABCD Track
- confopt: A Library for Implementation and Evaluation of Gradient-based One-Shot NAS Methods
Abhash Kumar Jha, Shakiba Moradian, Arjun Krishnakumar, Martin Rapp, Frank Hutter
OpenReview – PDF - Ax: A Platform for Adaptive Experimentation
Miles Olson, Elizabeth Santorella, Louis C. Tiao, Sait Cakmak, Mia Garrard, Samuel Daulton, Zhiyuan Jerry Lin, Sebastian Ament, Bernard Beckerman, Eric Onofrey, Paschal Igusti, Cristian Lara, Benjamin Letham, Cesar Cardoso, Shiyun Sunny Shen, Andy Chenyuan Lin, Matthew Grange, Elena Kashtelyan, David Eriksson, Maximilian Balandat, Eytan Bakshy
OpenReview – PDF - 🛜 Exploring One Million Machine Learning Pipelines: A Benchmarking Study
Edesio Alcobaça, Andre Carlos Ponce de Leon Ferreira De Carvalho
OpenReview – PDF - 🛜 AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting
Keshav Das, Julie Keisler, Margaux Brégère, Amaury Durand
OpenReview – PDF - CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization
Jacob O Tørring, Carl Hvarfner, Luigi Nardi, Magnus Själander
OpenReview – PDF - Auto-nnU-Net: Towards Automated Medical Image Segmentation
Jannis Becktepe, Leona Hennig, Steffen Oeltze-Jafra, Marius Lindauer
OpenReview – PDF