ML Anthology

Dimitrov, Dimitar Iliev

11 publications

ICLR 2025 GRAIN: Exact Graph Reconstruction from Gradients Maria Drencheva, Ivo Petrov, Maximilian Baader, Dimitar Iliev Dimitrov, Martin Vechev
NeurIPS 2025 MixAT: Combining Continuous and Discrete Adversarial Training for LLMs Csaba Dékány, Stefan Balauca, Dimitar Iliev Dimitrov, Robin Staab, Martin Vechev
NeurIPSW 2024 Constraint-Based Synthetic Data Generation for LLM Mathematical Reasoning Timofey Fedoseev, Dimitar Iliev Dimitrov, Timon Gehr, Martin Vechev
ICLR 2024 Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning Kostadin Garov, Dimitar Iliev Dimitrov, Nikola Jovanović, Martin Vechev
ICML 2023 FARE: Provably Fair Representation Learning with Practical Certificates Nikola Jovanović, Mislav Balunovic, Dimitar Iliev Dimitrov, Martin Vechev
ICMLW 2023 Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning Kostadin Garov, Dimitar Iliev Dimitrov, Nikola Jovanović, Martin Vechev
ICML 2023 TabLeak: Tabular Data Leakage in Federated Learning Mark Vero, Mislav Balunovic, Dimitar Iliev Dimitrov, Martin Vechev
ICLR 2022 Bayesian Framework for Gradient Leakage Mislav Balunovic, Dimitar Iliev Dimitrov, Robin Staab, Martin Vechev
TMLR 2022 Data Leakage in Federated Averaging Dimitar Iliev Dimitrov, Mislav Balunovic, Nikola Konstantinov, Martin Vechev
NeurIPSW 2022 FARE: Provably Fair Representation Learning Nikola Jovanović, Mislav Balunovic, Dimitar Iliev Dimitrov, Martin Vechev
ICLR 2022 Provably Robust Adversarial Examples Dimitar Iliev Dimitrov, Gagandeep Singh, Timon Gehr, Martin Vechev