42 citations · 55 across the 13 of their papers we have counts for
13 papers
Automated Machine Learning for Unsupervised Tabular Tasks
Prabhant Singh, Pieter Gijsbers, Elif Ceren Gok Yildirim +2
In this work, we present LOTUS (Learning to Learn with Optimal Transport for Unsupervised Scenarios), a simple yet effective method to perform model selection for multiple unsuperv…
How NOT to benchmark your SITE metric: Beyond Static Leaderboards and Towards Realistic Evaluation
Prabhant Singh, Sibylle Hess, Joaquin Vanschoren
Transferability estimation metrics are used to find a high-performing pre-trained model for a given target task without fine-tuning models and without access to the source dataset.…
Analysis of Transferability Estimation Metrics for Surgical Phase Recognition
Prabhant Singh, Yiping Li, Yasmina Al Khalil
Fine-tuning pre-trained models has become a cornerstone of modern machine learning, allowing practitioners to achieve high performance with limited labeled data. In surgical video…
Meta-Learning Transformers to Improve In-Context Generalization
Lorenzo Braccaioli, Anna Vettoruzzo, Prabhant Singh +3
In-context learning enables transformer models to generalize to new tasks based solely on input prompts, without any need for weight updates. However, existing training paradigms t…
Occam's model: Selecting simpler representations for better transferability estimation
Prabhant Singh, Sibylle Hess, Joaquin Vanschoren
Fine-tuning models that have been pre-trained on large datasets has become a cornerstone of modern machine learning workflows. With the widespread availability of online model repo…
On dataset transferability in medical image classification
Dovile Juodelyte, Enzo Ferrante, Yucheng Lu +3
Current transferability estimation methods designed for natural image datasets are often suboptimal in medical image classification. These methods primarily focus on estimating the…
CLAMS: A System for Zero-Shot Model Selection for Clustering
Prabhant Singh, Pieter Gijsbers, Murat Onur Yildirim +2
We propose an AutoML system that enables model selection on clustering problems by leveraging optimal transport-based dataset similarity. Our objective is to establish a comprehens…
On Supernet Transfer Learning for Effective Task Adaptation
Prabhant Singh, Joaquin Vanschoren
Neural Architecture Search (NAS) methods have been shown to outperform hand-designed models and help to democratize AI. However, NAS methods often start from scratch with each new…
Automated Imbalanced Learning
Prabhant Singh, Joaquin Vanschoren
Automated Machine Learning has grown very successful in automating the time-consuming, iterative tasks of machine learning model development. However, current methods struggle when…
Meta-Learning for Unsupervised Outlier Detection with Optimal Transport
Prabhant Singh, Joaquin Vanschoren
Automated machine learning has been widely researched and adopted in the field of supervised classification and regression, but progress in unsupervised settings has been limited.…
EmProx: Neural Network Performance Estimation For Neural Architecture Search
G. G. H. Franken, P. Singh, J. Vanschoren
Common Neural Architecture Search methods generate large amounts of candidate architectures that need training in order to assess their performance and find an optimal architecture…
Online AutoML: An adaptive AutoML framework for online learning
Bilge Celik, Prabhant Singh, Joaquin Vanschoren
Automated Machine Learning (AutoML) has been used successfully in settings where the learning task is assumed to be static. In many real-world scenarios, however, the data distribu…
A Study of the Learning Progress in Neural Architecture Search Techniques
Prabhant Singh, Tobias Jacobs, Sebastien Nicolas +1
In neural architecture search, the structure of the neural network to best model a given dataset is determined by an automated search process. Efficient Neural Architecture Search…