Private and Reliable Neural Network Inference

1 code implementation • 27 Oct 2022 • Nikola Jovanović, Marc Fischer, Samuel Steffen, Martin Vechev

We employ these building blocks to enable privacy-preserving NN inference with robustness and fairness guarantees in a system called Phoenix.

Fairness Privacy Preserving

FARE: Provably Fair Representation Learning

no code implementations • 13 Oct 2022 • Nikola Jovanović, Mislav Balunović, Dimitar I. Dimitrov, Martin Vechev

In this work we address this challenge and propose Fairness with Restricted Encoders (FARE), the first FRL method with provable fairness guarantees.

Fairness Representation Learning

LAMP: Extracting Text from Gradients with Language Model Priors

1 code implementation • 17 Feb 2022 • Mislav Balunović, Dimitar I. Dimitrov, Nikola Jovanović, Martin Vechev

Recent work shows that sensitive user data can be reconstructed from gradient updates, breaking the key privacy promise of federated learning.

Federated Learning Language Modelling

Towards Robust Graph Contrastive Learning

no code implementations • 25 Feb 2021 • Nikola Jovanović, Zhao Meng, Lukas Faber, Roger Wattenhofer

We study the problem of adversarially robust self-supervised learning on graphs.

Adversarial Robustness Contrastive Learning +1

On the Paradox of Certified Training

no code implementations • 12 Feb 2021 • Nikola Jovanović, Mislav Balunović, Maximilian Baader, Martin Vechev

Certified defenses based on convex relaxations are an established technique for training provably robust models.

Towards Sparse Hierarchical Graph Classifiers

1 code implementation • 3 Nov 2018 • Cătălina Cangea, Petar Veličković, Nikola Jovanović, Thomas Kipf, Pietro Liò

Recent advances in representation learning on graphs, mainly leveraging graph convolutional networks, have brought a substantial improvement on many graph-based benchmark tasks.

Classification General Classification +4