{"id":1406,"date":"2024-05-07T00:53:18","date_gmt":"2024-05-07T00:53:18","guid":{"rendered":"https:\/\/2024.automl.cc\/?page_id=1406"},"modified":"2024-10-16T12:24:08","modified_gmt":"2024-10-16T12:24:08","slug":"accepted-papers","status":"publish","type":"page","link":"https:\/\/2024.automl.cc\/?page_id=1406","title":{"rendered":"Accepted Papers"},"content":{"rendered":"\n

Best paper awards<\/h2>\n\n\n\n

Winner:<\/h4>\n\n\n\n

\u2022 FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search<\/span>
<\/strong>\u2022<\/strong> <\/span>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
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

Runners up:<\/h4>\n\n\n\n

\u2022 <\/strong>HPO-RL-Bench: A Zero-Cost Benchmark for HPO in Reinforcement Learning<\/strong><\/span>
\u2022<\/strong> <\/span>Gresa Shala, Sebastian Pineda Arango, Andr\u00e9 Biedenkapp, Frank Hutter, Josif Grabocka
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Training and Cross-Validating Machine Learning Pipelines with Limited Memory<\/strong><\/span>
\u2022<\/strong> <\/span>Martin Hirzel, Kiran Kate, Louis Mandel, Avraham Shinnar 
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\n\n\n\n
\n

Methods Track<\/h2>\n\n\n\n

Poster session 1<\/em><\/h4>\n\n\n\n

\u2022 Sequence Alignment-based Similarity Metric in Evolutionary Neural Architecture Search<\/span>
<\/strong>\u2022<\/strong> <\/span>Mateo Avila Pava, Ren\u00e9 Groh, Andreas M. Kist
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Don’t Waste Your Time: Early Stopping Cross-Validation<\/strong><\/span>
\u2022<\/strong> <\/span>Edward Bergman, Lennart Purucker, Frank Hutter
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/p>\n\n\n\n

\u2022 <\/strong>Analyzing Few-Shot Neural Architecture Search in a Metric-Driven Framework<\/strong><\/span>
\u2022<\/strong> <\/span>Timot\u00e9e Ly-Manson, Mathieu L\u00e9onardon, Abdeldjalil Aissa El Bey, Ghouthi Boukli Hacene, Lukas Mauch
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Bi-Level One-Shot Architecture Search for Probabilistic Time Series Forecasting<\/strong><\/span>
Jonas Seng, Fabian Kalter, Zhongjie Yu, Fabrizio Ventola, Kristian Kersting
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>HPOD: Hyperparameter Optimization for Unsupervised Outlier Detection<\/strong><\/span>
\u2022<\/strong> <\/span>Yue Zhao, Leman Akoglu
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Weight-Entanglement Meets Gradient-Based Neural Architecture Search<\/strong><\/span>
\u2022<\/strong> <\/span>Rhea Sanjay Sukthanker, Arjun Krishnakumar, Mahmoud Safari, Frank Hutter
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n<\/div><\/div>\n\n\n\n

Poster session 2<\/em><\/h4>\n\n\n\n

\u2022 FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search<\/span>
<\/strong>\u2022<\/strong> <\/span>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
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Training and Cross-Validating Machine Learning Pipelines with Limited Memory<\/strong><\/span>
\u2022<\/strong> <\/span>Martin Hirzel, Kiran Kate, Louis Mandel, Avraham Shinnar 
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Confidence Interval Estimation of Predictive Performance in the Context of AutoML<\/strong><\/span>
\u2022<\/strong> <\/span>Konstantinos Paraschakis, Andrea Castellani, Giorgos Borboudakis, Ioannis Tsamardinos
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Is Mamba Capable of In-Context Learning?<\/strong><\/span>
\u2022<\/strong> <\/span>Riccardo Grazzi, Julien Niklas Siems, Simon Schrodi, Thomas Brox, Frank Hutter
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>ASML: A Scalable and Efficient AutoML Solution for Data Streams<\/strong><\/span>
\u2022<\/strong> <\/span>Nilesh Verma, Albert Bifet, Bernhard Pfahringer, Maroua Bahri
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Improving Transfer Learning by means of Ensemble Learning and Swarm Intelligence-based Neuroevolution<\/strong><\/span>
\u2022<\/strong> <\/span>Adri G\u00f3mez, Monica Abella, Manuel Desco
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Speeding up NAS with Adaptive Subset Selection<\/strong><\/span>
\u2022<\/strong> <\/span>Vishak Prasad C, Colin White, Sibasis Nayak, Paarth Jain, Aziz Shameem, Prateek Garg, Ganesh Ramakrishnan
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\n
\n\n\n\n

ABCD Track<\/h2>\n\n\n\n

Poster session 1<\/em><\/h4>\n\n\n\n

\u2022 <\/strong>TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications<\/strong><\/span>
\u2022<\/strong> <\/span>David Salinas, Nick Erickson
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Fast Benchmarking of Asynchronous Multi-Fidelity Optimization on Zero-Cost Benchmarks<\/strong><\/span>
\u2022<\/strong> <\/span>Shuhei Watanabe, Neeratyoy Mallik, Edward Bergman, Frank Hutter
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Introducing HoNCAML: Holistic No-Code Auto Machine Learning<\/strong><\/span>
\u2022<\/strong> <\/span>Luca Piras, Joan Albert Err\u00e1ez Castelltort, Jordi Casals Grifell, Xavier de Juan Pulido, Cirus Iniesta, Marina Rosell Murillo, Cristina Soler Arenys
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models<\/strong><\/span>
\u2022<\/strong> <\/span>Zhiqiang Tang, Haoyang Fang, Su Zhou, Taojiannan Yang, Zihan Zhong, Cuixiong Hu, Katrin Kirchhoff, George Karypis
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n<\/div><\/div>\n\n\n\n

Poster session 2<\/em><\/h4>\n\n\n\n

\u2022 <\/strong>HPO-RL-Bench: A Zero-Cost Benchmark for HPO in Reinforcement Learning<\/strong><\/span>
\u2022<\/strong> <\/span>Gresa Shala, Sebastian Pineda Arango, Andr\u00e9 Biedenkapp, Frank Hutter, Josif Grabocka
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Automated Deep Learning for load forecasting<\/strong><\/span>
\u2022<\/strong> <\/span>Julie Keisler, Sandra Claudel, Gilles Cabriel, Margaux Br\u00e9g\u00e8re
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\n
\n\n\n\n

Journal Track<\/h2>\n\n\n\n

Poster session 1<\/em><\/h4>\n\n\n\n

\u2022 <\/strong>Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML<\/strong><\/span>
\u2022<\/strong> <\/span>Hilde Jacoba Petronella Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>AMLB: an AutoML Benchmark<\/strong><\/span>
\u2022<\/strong> <\/span>Pieter Gijsbers, Marcos L P Bueno, Stefan Coors, Erin LeDell, Janek Thomas, Bernd Bischl, Joaquin Vanschoren
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n<\/div><\/div>\n\n\n\n

Poster session <\/em>2<\/h4>\n\n\n\n

\u2022 <\/strong>Controlling Federated Learning for Covertness<\/strong><\/span>
\u2022<\/strong> <\/span>Adit Jain, Vikram Krishnamurthy
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/p>\n\n\n\n

\u2022 <\/strong>AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks<\/strong><\/span>
\u2022<\/strong> <\/span>Alexander Tornede, Difan Deng, Theresa Eimer, Joseph Giovanelli, Aditya Mohan, Tim Ruhkopf, Sarah Segel, Daphne Theodorakopoulos, Tanja Tornede, Henning Wachsmuth, Marius Lindauer
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\n\n\n\n

Workshop Track<\/h2>\n\n\n\n

Poster session 1<\/em><\/h4>\n\n\n\n

\u2022 Towards <\/strong>Efficient Search for Customized Activation Functions With Gradient Descent<\/strong><\/span>
\u2022<\/strong> <\/span>Lukas Strack, Mahmoud Safari, Frank Hutter
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>LMEMs for post-hoc analysis of HPO Benchmarking<\/strong><\/span>
\u2022<\/strong> <\/span>Anton Merlin Geburek, Neeratyoy Mallik, Danny Stoll, Xavier Bouthillier, Frank Hutter
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>einspace: Searching for Neural Architectures from Fundamental Operations<\/strong><\/span>
\u2022<\/strong> <\/span>Linus Ericsson, Miguel Espinosa, Chenhongyi Yang, Antreas Antoniou, Amos Storkey, Shay B. Cohen, Steven McDonagh, Elliot J. Crowley
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>FairPFN: Transformers Can Do Counterfactual Fairness<\/strong><\/span>
\u2022<\/strong> <\/span>Jake Robertson, Noah Hollmann, Noor Awad, Frank Hutter
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>CANDID DAC: Leveraging Coupled Action Dimensions with Importance Differences in DAC<\/strong><\/span>
\u2022<\/strong> <\/span>Philipp Bordne, M Asif Hasan, Edward Bergman, Noor Awad, Andr\u00e9 Biedenkapp
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Do Tree-based Models Need Data Preprocessing?<\/strong><\/span>
\u2022<\/strong> <\/span>Hubert Ruczy\u0144ski, Anna Kozak
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Quick-Tune-Tool: A Practical Tool and its User Guide for Automatically Finetuning Pretrained Models<\/strong><\/span>
\u2022<\/strong> <\/span>Ivo Rapant, Lennart Purucker, Fabio Ferreira, Sebastian Pineda Arango, Arlind Kadra, Josif Grabocka, Frank Hutter
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>NOSBench-101: Towards Reproducible Neural Optimizer Search<\/strong><\/span>
\u2022<\/strong> <\/span>Goktug Karakasli, Steven Adriaensen, Frank Hutter
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>FEATHERS: Federated Architecture and Hyperparameter Search<\/strong><\/span>
\u2022<\/strong> <\/span>Jonas Seng, Pooja Prasad, Devendra Singh Dhami, Martin Mundt, Kristian Kersting
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Sample-Efficient Bayesian Optimization with Transfer Learning for Heterogeneous Search Spaces<\/strong><\/span>
\u2022<\/strong> <\/span>Aryan Deshwal, Sait Cakmak, Yuhou Xia, David Eriksson
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Cost-Sensitive Multi-Fidelity Bayesian Optimization with Transfer of Learning Curve Extrapolation<\/strong><\/span>
\u2022<\/strong> <\/span>Dong Bok Lee, Aoxuan Silvia Zhang, Byungjoo Kim, Junhyeon Park, Juho Lee, Sung Ju Hwang, Hae Beom Lee
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/p>\n\n\n\n

\u2022 <\/strong>Drift-Resilient TabPFN: In-Context Learning Distribution Shifts on Tabular Data<\/strong><\/span>
\u2022<\/strong> <\/span>Kai Helli, David Schnurr, Noah Hollmann, Samuel M\u00fcller, Frank Hutter
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Selecting Pre-trained Models for Transfer Learning with Data-centric Meta-features<\/strong><\/span>
\u2022<\/strong> <\/span>Matt van den Nieuwenhuijzen, Carola Doerr, Henry Gouk, Jan N. van Rijn
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Automated Federated Learning via Informed Pruning<\/strong><\/span>
\u2022<\/strong> <\/span>Christian Intern\u00f2, Elena Raponi, Niki van Stein, Thomas B\u00e4ck, Markus Olhofer, Yaochu Jin, Barbara Hammer
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/p>\n\n\n\n

\u2022 <\/strong>AutoCD: Automated Machine Learning for Causal Discovery Algorithms<\/strong><\/span>
\u2022<\/strong> <\/span>Gerlise Chan, Tom Claassen, Holger Hoos, Tom Heskes, Mitra Baratchi
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Bibat: Batteries-Included Bayesian Analysis Template<\/strong><\/span>
\u2022<\/strong> <\/span>Teddy Groves
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

Poster session 2<\/em><\/h4>\n\n\n\n

\u2022 <\/strong>Automated Prior Elicitation from Large Language Models for Bayesian Logistic Regression<\/strong><\/span>
\u2022<\/strong> <\/span>Henry Gouk, Boyan Gao
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>In-Context Learning for Latency Estimation<\/strong><\/span>
\u2022<\/strong> <\/span>Timur Carstensen, Thomas Elsken, Martin Rapp
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Fine-Tuning LLMs for Automated Feature Engineering<\/strong><\/span>
\u2022<\/strong> <\/span>Yoichi Hirose, Kento Uchida, Shinichi Shirakawa
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>LoRA-DARTS: Low Rank Adaptation for Differentiable Architecture Search<\/strong><\/span>
\u2022<\/strong> <\/span>Arjun Krishnakumar, Abhash Kumar Jha, Shakiba Moradian, Martin Rapp, Frank Hutter
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization<\/strong><\/span>
\u2022<\/strong> <\/span>Herilalaina Rakotoarison, Steven Adriaensen, Neeratyoy Mallik, Samir Garibov, Edward Bergman, Frank Hutter
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>From Epoch to Sample Size: Developing New Data-driven Priors for Learning Curve Prior-Fitted Networks<\/strong><\/span>
\u2022<\/strong> <\/span>Tom Julian Viering, Steven Adriaensen, Herilalaina Rakotoarison, Frank Hutter
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Cost-Efficient Training for Automated Algorithm Selection<\/strong><\/span>
\u2022<\/strong> <\/span>Erdem Ku\u015f, Nguyen Dang, Ozgur Akgun, Ian Miguel
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Beyond Graph-Based Modeling for Hierarchical Neural Architecture Search<\/strong><\/span>
\u2022<\/strong> <\/span>Lum Birinxhiku, Danny Stoll, Simon Schrodi, Frank Hutter
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Rethinking of Encoder-based Warm-start Methods in Hyperparameter Optimization<\/strong><\/span>
\u2022<\/strong> <\/span>Dawid P\u0142udowski, Antoni Zajko, Katarzyna Wo\u017anica, Anna Kozak
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Fast Optimizer Benchmark<\/strong><\/span>
\u2022<\/strong> <\/span>Simon Blauth, Tobias B\u00fcrger, Zacharias H\u00e4ringer, J\u00f6rg K.H. Franke, Frank Hutter
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Graph is All You Need? Lightweight Data-agnostic Neural Architecture Search without Training<\/strong><\/span>
\u2022<\/strong> <\/span>Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen, Chunheng Jiang, Jianxi Gao
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and Cost<\/strong><\/span>
\u2022<\/strong> <\/span>Jannis Maier, Felix M\u00f6ller, Lennart Purucker
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Beyond the Threshold: Time Is All You Need<\/strong><\/span>
\u2022<\/strong> <\/span>Stefan Dendorfer, Andreas M. Kist
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Investigating the Impact of Hard and Easy Samples on Generalization Reveals In-class Data Imbalance<\/strong><\/span>
\u2022<\/strong> <\/span>Pawel Pukowski, Haiping Lu
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Hyperparameter Optimization via Interacting with Probabilistic Circuits<\/strong><\/span>
\u2022<\/strong> <\/span>Jonas Seng, Fabrizio Ventola, Zhongjie Yu, Kristian Kersting
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 <\/strong>Benchmarking AutoML Clustering Frameworks<\/strong><\/span>
\u2022<\/strong> <\/span>Matheus Camilo da Silva, Biagio Licari, Gabriel Marques Tavares, Sylvio Barbon Junior
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n\n

\u2022 PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applications<\/strong><\/span>
\u2022<\/strong> <\/span>Ricardo Knauer, Marvin Grimm, Erik Rodner
\u2022<\/strong> <\/span>OpenReview<\/a> \u2013 PDF<\/a> \u2013 Video<\/a><\/p>\n\n\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>","protected":false},"excerpt":{"rendered":"

Best paper awards Winner: \u2022 FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search\u2022 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\u2022 OpenReview \u2013 PDF \u2013 Video Runners up: \u2022 HPO-RL-Bench: A Zero-Cost Benchmark for HPO […]<\/p>\n","protected":false},"author":11,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1406","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/2024.automl.cc\/index.php?rest_route=\/wp\/v2\/pages\/1406","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/2024.automl.cc\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/2024.automl.cc\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/2024.automl.cc\/index.php?rest_route=\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"https:\/\/2024.automl.cc\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1406"}],"version-history":[{"count":0,"href":"https:\/\/2024.automl.cc\/index.php?rest_route=\/wp\/v2\/pages\/1406\/revisions"}],"wp:attachment":[{"href":"https:\/\/2024.automl.cc\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1406"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}