Dimitar I. Dimitrov

According to our database, Dimitar I. Dimitrov authored at least 19 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

LARP: Learner-Agnostic Robust Data Prefiltering.

Kristian Minchev, Dimitar Iliev Dimitrov, Nikola Konstantinov

CoRR, June, 2025

MixAT: Combining Continuous and Discrete Adversarial Training for LLMs.

Csaba Dékány, Stefan Balauca, Robin Staab, Dimitar I. Dimitrov, Martin T. Vechev

CoRR, May, 2025

GRAIN: Exact Graph Reconstruction from Gradients.

Maria Drencheva, Ivo Petrov, Maximilian Baader, Dimitar Iliev Dimitrov, Martin T. Vechev

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

BgGPT 1.0: Extending English-centric LLMs to other languages.

Anton Alexandrov, Veselin Raychev, Dimitar I. Dimitrov, Ce Zhang, Martin T. Vechev, Kristina Toutanova

CoRR, 2024

DAGER: Exact Gradient Inversion for Large Language Models.

Ivo Petrov, Dimitar I. Dimitrov, Maximilian Baader, Mark Niklas Müller, Martin T. Vechev

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

SPEAR: Exact Gradient Inversion of Batches in Federated Learning.

Dimitar I. Dimitrov, Maximilian Baader, Mark Niklas Müller, Martin T. Vechev

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning.

Kostadin Garov, Dimitar Iliev Dimitrov, Nikola Jovanovic, Martin T. Vechev

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

TabLeak: Tabular Data Leakage in Federated Learning.

Mark Vero, Mislav Balunovic, Dimitar Iliev Dimitrov, Martin T. Vechev

Proceedings of the International Conference on Machine Learning, 2023

FARE: Provably Fair Representation Learning with Practical Certificates.

Nikola Jovanovic, Mislav Balunovic, Dimitar Iliev Dimitrov, Martin T. Vechev

Proceedings of the International Conference on Machine Learning, 2023

Group and Attack: Auditing Differential Privacy.

Johan Lokna, Anouk Paradis, Dimitar I. Dimitrov, Martin T. Vechev

Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, 2023

2022

Data Leakage in Federated Averaging.

Dimitar Iliev Dimitrov, Mislav Balunovic, Nikola Konstantinov, Martin T. Vechev

Trans. Mach. Learn. Res., 2022

FARE: Provably Fair Representation Learning.

Nikola Jovanovic, Mislav Balunovic, Dimitar I. Dimitrov, Martin T. Vechev

CoRR, 2022

Data Leakage in Tabular Federated Learning.

Mark Vero, Mislav Balunovic, Dimitar I. Dimitrov, Martin T. Vechev

CoRR, 2022

LAMP: Extracting Text from Gradients with Language Model Priors.

Mislav Balunovic, Dimitar I. Dimitrov, Nikola Jovanovic, Martin T. Vechev

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Provably Robust Adversarial Examples.

Dimitar Iliev Dimitrov, Gagandeep Singh, Timon Gehr, Martin T. Vechev

Proceedings of the Tenth International Conference on Learning Representations, 2022

Bayesian Framework for Gradient Leakage.

Mislav Balunovic, Dimitar Iliev Dimitrov, Robin Staab, Martin T. Vechev

Proceedings of the Tenth International Conference on Learning Representations, 2022

Shared Certificates for Neural Network Verification.

Marc Fischer, Christian Sprecher, Dimitar I. Dimitrov, Gagandeep Singh, Martin T. Vechev

Proceedings of the Computer Aided Verification - 34th International Conference, 2022

2021

Fast and precise certification of transformers.

Gregory Bonaert, Dimitar I. Dimitrov, Maximilian Baader, Martin T. Vechev

Proceedings of the PLDI '21: 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation, 2021

2020

Scalable Inference of Symbolic Adversarial Examples.

Dimitar I. Dimitrov, Gagandeep Singh, Timon Gehr, Martin T. Vechev

CoRR, 2020