Publications by Valentin Hartmann

Neural Redshift: Random Networks are not Random Functions

Our understanding of the generalization capabilities of neural networks (NNs) is still incomplete. Prevailing explanations are based on implicit biases of gradient descent (GD) but they cannot account for the capabilities of models from gradient-free metho ...

IEEE2024

Privacy and Confidentiality in Machine Learning and Data Analysis: Understanding Risks and Developing Protections

Valentin Hartmann

Without the ability to collect, access and analyze data, most of nowadays research would be impossible. Without data to learn from, the field of machine learning (ML) would not exist. However, much of the particularly useful data---medical records, human b ...

EPFL2024

Language Model Decoding as Likelihood–Utility Alignment

Boi Faltings, Robert West, Maxime Jean Julien Peyrard, Martin Josifoski, Valentin Hartmann, Debjit Paul, Jiheng Wei, Frano Rajic

A critical component of a successful language generation pipeline is the decoding algorithm. However, the general principles that should guide the choice of a decoding algorithm re- main unclear. Previous works only compare decoding algorithms in narrow sc ...

Assoc Computational Linguistics-Acl2023

Distribution Inference Risks: Identifying and Mitigating Sources of Leakage

Robert West, Maxime Jean Julien Peyrard, Valentin Hartmann, Léo Nicolas René Meynent

A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inference (or property inference) attacks is gaining attention. In this attack, the ...

IEEE COMPUTER SOC2023

Semi-discrete optimal transport: a solution procedure for the unsquared Euclidean distance case

Valentin Hartmann

We consider the problem of finding an optimal transport plan between an absolutely continuous measure and a finitely supported measure of the same total mass when the transport cost is the unsquared Euclidean distance. We may think of this problem as close ...

SPRINGER HEIDELBERG2020

Orderings of Data - More Than a Tripping Hazard Visionary

Valentin Hartmann

As data processing techniques get more and more sophisticated every day, many of us researchers often get lost in the details and subtleties of the algorithms we are developing and far too easily seem to forget to look also at the very first steps of every ...

Assoc Computing Machinery2020