19 citations · 78 across the 10 of their papers we have counts for
11 papers
Differentially Private Federated -Means Clustering with Server-Side Data
Jonathan Scott, Christoph H. Lampert, David Saulpic
Clustering is a cornerstone of data analysis that is particularly suited to identifying coherent subgroups or substructures in unlabeled data, as are generated continuously in larg…
DP-KAN: Differentially Private Kolmogorov-Arnold Networks
Nikita P. Kalinin, Simone Bombari, Hossein Zakerinia +1
We study the Kolmogorov-Arnold Network (KAN), recently proposed as an alternative to the classical Multilayer Perceptron (MLP), in the application for differentially private model…
Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model
Peter Súkeník, Marco Mondelli, Christoph Lampert
Neural collapse (NC) refers to the surprising structure of the last layer of deep neural networks in the terminal phase of gradient descent training. Recently, an increasing amount…
Generalization In Multi-Objective Machine Learning
Peter Súkeník, Christoph H. Lampert
Modern machine learning tasks often require considering not just one but multiple objectives. For example, besides the prediction quality, this could be the efficiency, robustness…
Extrapolation and learning equations
Georg Martius, Christoph H. Lampert
In classical machine learning, regression is treated as a black box process of identifying a suitable function from a hypothesis set without attempting to gain insight into the mec…
Curriculum Learning of Multiple Tasks
Anastasia Pentina, Viktoriia Sharmanska, Christoph H. Lampert
Sharing information between multiple tasks enables algorithms to achieve good generalization performance even from small amounts of training data. However, in a realistic scenario…