TRIO: Task-agnostic dataset representation optimized for automatic algorithm selection
TRIO: Task-agnostic dataset representation optimized for automatic algorithm selection
Noy Cohen-Shapira is a PhD student at the Software and Information Systems Engineering Department at Ben-Gurion University, under the supervision of prof. Lior Rokach. Her research focuses on automated machine learning (AutoML) and meta-learning. She also serves as a data scientist at Ben-Gurion University of the Negev’s Cyber Security Research Center (CBG).
Noy Cohen-Shapira is a PhD student at the Software and Information Systems Engineering Department at Ben-Gurion University, under the supervision of prof. Lior Rokach. Her research focuses on automated machine learning (AutoML) and meta-learning. She also serves as a data scientist at Ben-Gurion University of the Negev’s Cyber Security Research Center (CBG).
With the growing number of machine learning (ML) algorithms, selecting the top-performing algorithms for a given dataset, task, and evaluation measure is known to be a challenging task. The human expertise required for this task has fueled the demand for automated solutions. Meta-learning is a popular approach for automatic algorithm selection based on dataset characterization. Existing meta-learning methods often represent the datasets using predefined features and thus cannot be generalized for various ML tasks, or alternatively, learn their representations in a supervised fashion, and thus cannot address unsupervised tasks. We first propose a novel learning-based task-agnostic method for dataset representation in this talk. Second, we present TRIO, a meta-learning approach based on the proposed dataset representation, which can accurately recommend top-performing algorithms for unseen datasets. TRIO first learns graphical representations from the datasets and then utilizes a graph convolutional neural network technique to extract their latent representations. An extensive evaluation of 337 datasets and 195 ML algorithms demonstrates the effectiveness of our approach over state-of-the-art methods for algorithm selection for both supervised (classification and regression) and unsupervised (clustering) tasks.
With the growing number of machine learning (ML) algorithms, selecting the top-performing algorithms for a given dataset, task, and evaluation measure is known to be a challenging task. The human expertise required for this task has fueled the demand for automated solutions. Meta-learning is a popular approach for automatic algorithm selection based on dataset characterization. Existing meta-learning methods often represent the datasets using predefined features and thus cannot be generalized for various ML tasks, or alternatively, learn their representations in a supervised fashion, and thus cannot address unsupervised tasks. We first propose a novel learning-based task-agnostic method for dataset representation in this talk. Second, we present TRIO, a meta-learning approach based on the proposed dataset representation, which can accurately recommend top-performing algorithms for unseen datasets. TRIO first learns graphical representations from the datasets and then utilizes a graph convolutional neural network technique to extract their latent representations. An extensive evaluation of 337 datasets and 195 ML algorithms demonstrates the effectiveness of our approach over state-of-the-art methods for algorithm selection for both supervised (classification and regression) and unsupervised (clustering) tasks.
8:45 | Reception |
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9:30 | Opening words by WiDS TLV ambassadors Or Basson and Noah Eyal Altman |
9:40 | Dr. Kira Radinski - Learning to predict the future of healthcare |
10:10 | Prof. Yonina Eldar - Model-Based Deep Learning: Applications to Imaging and Communications |
10:40 | Break |
10:50 | Lightning talks |
12:20 | Lunch & Poster session |
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13:20 | Roundtable session & Poster session |
14:05 | Roundtable closure |
14:20 | Break |
14:30 | Dr. Anna Levant - 3D Metrology: Seeing the Unseen |
15:00 | Aviv Ben-Arie - Counterfactual Explanations: The Future of Explainable AI? |
15:30 | Closing remarks |
15:40 | End |
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