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Viewing Publication 1 - 46 from 46

2023

Cost-Effective Hyperparameter Optimization for Large Language Model Generation InferenceChi Wang 0001, Xueqing Liu 0001, Ahmed Hassan Awadallah. automl 2023: [doi]

Optimal Resource Allocation for Early Stopping-based Neural Architecture Search MethodsMarcel Aach, Eray Inanc, Rakesh Sarma, Morris Riedel, Andreas Lintermann. automl 2023: [doi]

Multi-Predict: Few Shot Predictors For Efficient Neural Architecture SearchYash Akhauri, Mohamed S. Abdelfattah. automl 2023: [doi]

Self-Adjusting Weighted Expected Improvement for Bayesian OptimizationCarolin Benjamins, Elena Raponi, Anja Jankovic 0001, Carola Doerr, Marius Lindauer. automl 2023: [doi]

"No Free Lunch" in Neural Architectures? A Joint Analysis of Expressivity, Convergence, and GeneralizationWuyang Chen, Wei Huang, Zhangyang Wang. automl 2023: [doi]

MEOW - Multi-Objective Evolutionary Weapon DetectionDaniel Dimanov, Colin Singleton, Shahin Rostami, Emili Balaguer-Ballester. automl 2023: [doi]

Better Practices for Domain AdaptationLinus Ericsson, Da Li 0001, Timothy M. Hospedales. automl 2023: [doi]

International Conference on Automated Machine Learning, 12-15 November 2023, Hasso Plattner Institute, Potsdam, GermanyAleksandra Faust, Roman Garnett, Colin White, Frank Hutter, Jacob R. Gardner, editors, Volume 228 of Proceedings of Machine Learning Research, PMLR, 2023. [doi]

Searching for Fairer Machine Learning EnsemblesMichael Feffer, Martin Hirzel, Samuel C. Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar. automl 2023: [doi]

ABLATOR: Robust Horizontal-Scaling of Machine Learning Ablation ExperimentsIordanis Fostiropoulos, Laurent Itti. automl 2023: [doi]

Neural Architecture Search for Visual Anomaly SegmentationTommie Kerssies, Joaquin Vanschoren. automl 2023: [doi]

PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box OptimizationAna Kostovska, Gjorgjina Cenikj, Diederick Vermetten, Anja Jankovic 0001, Ana Nikolikj, Urban Skvorc, Peter Korosec, Carola Doerr, Tome Eftimov. automl 2023: [doi]

Learning Activation Functions for Sparse Neural NetworksMohammad Loni, Aditya Mohan, Mehdi Asadi, Marius Lindauer. automl 2023: [doi]

AlphaD3M: An Open-Source AutoML Library for Multiple ML TasksRoque Lopez, Raoni Lourenço, Rémi Rampin, Sonia Castelo, Aécio S. R. Santos, Jorge Henrique Piazentin Ono, Cláudio T. Silva, Juliana Freire. automl 2023: [doi]

AutoRL Hyperparameter LandscapesAditya Mohan, Carolin Benjamins, Konrad Wienecke, Alexander Dockhorn, Marius Lindauer. automl 2023: [doi]

Meta-Learning for Fast Model Recommendation in Unsupervised Multivariate Time Series Anomaly DetectionJosé Manuel Navarro, Alexis Huet, Dario Rossi 0001. automl 2023: [doi]

CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable FailureLennart Oswald Purucker, Joeran Beel. automl 2023: [doi]

Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoMLLennart Oswald Purucker, Lennart Schneider, Marie Anastacio, Joeran Beel, Bernd Bischl, Holger H. Hoos. automl 2023: [doi]

Balanced Mixture of Supernets for Learning the CNN Pooling ArchitectureMehraveh Javan Roshtkhari, Matthew Toews, Marco Pedersoli. automl 2023: [doi]

Symbolic Explanations for Hyperparameter OptimizationSarah Segel, Helena Graf, Alexander Tornede, Bernd Bischl, Marius Lindauer. automl 2023: [doi]

AutoGluon-TimeSeries: AutoML for Probabilistic Time Series ForecastingOleksandr Shchur, Ali Caner Türkmen, Nick Erickson, Huibin Shen, Alexander Shirkov, Tony Hu, Bernie Wang 0001. automl 2023: [doi]

Computationally Efficient High-Dimensional Bayesian Optimization via Variable SelectionYihang Shen, Carl Kingsford. automl 2023: [doi]

MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization ContextsDiederick Vermetten, Furong Ye, Thomas Bäck, Carola Doerr. automl 2023: [doi]

Poisson Process for Bayesian OptimizationXiaoxing Wang, Jiaxing Li, Chao Xue, Wei Liu, Weifeng Liu, Xiaokang Yang, Junchi Yan, Dacheng Tao. automl 2023: [doi]

Exploiting Network Compressibility and Topology in Zero-Cost NASLichuan Xiang, Rosco Hunter, Minghao Xu, Lukasz Dudziak, Hongkai Wen 0001. automl 2023: [doi]

2022

BERT-Sort: A Zero-shot MLM Semantic Encoder on Ordinal Features for AutoMLMehdi Bahrami, Wei-Peng Chen, Lei Liu, Mukul Prasad. automl 2022: [doi]

Meta-Adapters: Parameter Efficient Few-shot Fine-tuning through Meta-LearningTrapit Bansal, Salaheddin Alzubi, Tong Wang, Jay Yoon Lee, Andrew McCallum. automl 2022: [doi]

ScaleNAS: Multi-Path One-Shot NAS for Scale-Aware High-Resolution RepresentationHsin-Pai Cheng, Feng Liang, Meng Li, Bowen Cheng, Feng Yan, Hai Li, Vikas Chandra, Yiran Chen 0001. automl 2022: [doi]

International Conference on Automated Machine Learning, AutoML 2022, 25-27 July 2022, Johns Hopkins University, Baltimore, MD, USAIsabelle Guyon, Marius Lindauer, Mihaela van der Schaar, Frank Hutter, Roman Garnett, editors, Volume 188 of Proceedings of Machine Learning Research, PMLR, 2022. [doi]

AutoCoG: A Unified Data-Model Co-Search Framework for Graph Neural NetworksDuc N. M. Hoang, Kaixiong Zhou, Tianlong Chen, Xia Hu, Zhangyang Wang. automl 2022: [doi]

What to expect of hardware metric predictors in NASKevin Alexander Laube, Maximus Mutschler, Andreas Zell. automl 2022: [doi]

When, where, and how to add new neurons to ANNsKaitlin Maile, Emmanuel Rachelson, Hervé Luga, Dennis George Wilson. automl 2022: [doi]

Automatic Termination for Hyperparameter OptimizationAnastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause 0001, Matthias W. Seeger, Cédric Archambeau. automl 2022: [doi]

Differentiable Architecture Search for Reinforcement LearningYingjie Miao, Xingyou Song, John D. Co-Reyes, Daiyi Peng, Summer Yue, Eugene Brevdo, Aleksandra Faust. automl 2022: [doi]

Automated Super-Network Generation for Scalable Neural Architecture SearchJuan Pablo Muñoz, Nikolay Lyalyushkin, Chaunte Willetta Lacewell, Anastasia Senina, Daniel Cummings, Anthony Sarah, Alexander Kozlov, Nilesh Jain. automl 2022: [doi]

YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter OptimizationFlorian Pfisterer, Lennart Schneider, Julia Moosbauer, Martin Binder, Bernd Bischl. automl 2022: [doi]

Opening the Black Box: Automated Software Analysis for Algorithm SelectionDamir Pulatov, Marie Anastacio, Lars Kotthoff, Holger H. Hoos. automl 2022: [doi]

On the Optimality Gap of Warm-Started Hyperparameter OptimizationParikshit Ram. automl 2022: [doi]

Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible ResearchDavid Salinas, Matthias W. Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cédric Archambeau. automl 2022: [doi]

Tackling Neural Architecture Search With Quality Diversity OptimizationLennart Schneider, Florian Pfisterer, Paul Kent, Jürgen Branke, Bernd Bischl, Janek Thomas. automl 2022: [doi]

LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for LassoKenan Sehic, Alexandre Gramfort, Joseph Salmon, Luigi Nardi. automl 2022: [doi]

Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox OptimizationXingyou Song, Sagi Perel, Chansoo Lee, Greg Kochanski, Daniel Golovin. automl 2022: [doi]

Bayesian Generational Population-Based TrainingXingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael A. Osborne. automl 2022: [doi]

A Tree-Structured Multi-Task Model RecommenderLijun Zhang, Xiao Liu, Hui Guan. automl 2022: [doi]

Non-Uniform Adversarially Robust PruningQi Zhao, Tim Köonigl, Christian Wressnegger. automl 2022: [doi]

DIFER: Differentiable Automated Feature EngineeringGuanghui Zhu, Zhuoer Xu, Chunfeng Yuan, Yihua Huang. automl 2022: [doi]

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