14 citations · 26 across the 11 of their papers we have counts for
7 papers · 1 filter
Scalable Multi-Task Low-Rank Model Adaptation
Zichen Tian, Antoine Ledent, Qianru Sun
Scaling multi-task low-rank adaptation (LoRA) to a large number of tasks induces catastrophic performance degradation, such as an accuracy drop from 88.2% to 2.0% on DOTA when scal…
Generalization Bounds for Semi-supervised Matrix Completion with Distributional Side Information
Antoine Ledent, Mun Chong Soo, Nong Minh Hieu
We study a matrix completion problem where both the ground truth matrix and the unknown sampling distribution over observed entries are low-rank matrices, and \textit{share…
Interpretable Tensor Fusion
Saurabh Varshneya, Antoine Ledent, Philipp Liznerski +4
Conventional machine learning methods are predominantly designed to predict outcomes based on a single data type. However, practical applications may encompass data of diverse type…
Fine-grained Generalization Analysis of Structured Output Prediction
Waleed Mustafa, Yunwen Lei, Antoine Ledent +1
In machine learning we often encounter structured output prediction problems (SOPPs), i.e. problems where the output space admits a rich internal structure. Application domains whe…
Fine-grained Generalization Analysis of Vector-valued Learning
Liang Wu, Antoine Ledent, Yunwen Lei +1
Many fundamental machine learning tasks can be formulated as a problem of learning with vector-valued functions, where we learn multiple scalar-valued functions together. Although…
Orthogonal Inductive Matrix Completion
Antoine Ledent, Rodrigo Alves, Marius Kloft
We propose orthogonal inductive matrix completion (OMIC), an interpretable approach to matrix completion based on a sum of multiple orthonormal side information terms, together wit…
Norm-based generalisation bounds for multi-class convolutional neural networks
Antoine Ledent, Waleed Mustafa, Yunwen Lei +1
We show generalisation error bounds for deep learning with two main improvements over the state of the art. (1) Our bounds have no explicit dependence on the number of classes exce…