14 citations · 26 across the 11 of their papers we have counts for
12 papers
On Finite-sample Concentration of Median of Incomplete U-Statistics
Nong Minh Hieu, Antoine Ledent
Median-of-means (MoM) is a powerful technique that theoretically enables near sub-Gaussian finite-sample rate for parameter estimation when the underlying data distribution is heav…
A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning
Nong Minh Hieu, Antoine Ledent
Contrastive Representation Learning (CRL) has achieved strong empirical success in multiple machine learning disciplines, yet its theoretical sample complexity remains poorly under…
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…
Generalization Analysis for Supervised Contrastive Representation Learning under Non-IID Settings
Nong Minh Hieu, Antoine Ledent
Contrastive Representation Learning (CRL) has achieved impressive success in various domains in recent years. Nevertheless, the theoretical understanding of the generalization beha…
Generalization Analysis for Deep Contrastive Representation Learning
Nong Minh Hieu, Antoine Ledent, Yunwen Lei +1
In this paper, we present generalization bounds for the unsupervised risk in the Deep Contrastive Representation Learning framework, which employs deep neural networks as represent…
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…
Learning Interpretable Concept Groups in CNNs
Saurabh Varshneya, Antoine Ledent, Robert A. Vandermeulen +4
We propose a novel training methodology -- Concept Group Learning (CGL) -- that encourages training of interpretable CNN filters by partitioning filters in each layer into concept…
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…