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ORCIDDimitar I. Dimitrov
Person information
- affiliation: ETH Zürich, Switzerland
- not to be confused with: Dimitar K. Dimitrov 0002
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2020 – today
- 2024
[j2]Hristo Venev
, Timon Gehr, Dimitar I. Dimitrov, Martin T. Vechev:
Modular Synthesis of Efficient Quantum Uncomputation. Proc. ACM Program. Lang. 8(OOPSLA2): 2097-2124 (2024)- [c11]Kostadin Garov, Dimitar Iliev Dimitrov, Nikola Jovanovic, Martin T. Vechev:
Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning. ICLR 2024 - [c10]Dimitar I. Dimitrov, Maximilian Baader, Mark Niklas Müller, Martin T. Vechev:
SPEAR: Exact Gradient Inversion of Batches in Federated Learning. NeurIPS 2024 - [c9]Ivo Petrov, Dimitar I. Dimitrov, Maximilian Baader, Mark Niklas Müller, Martin T. Vechev:
DAGER: Exact Gradient Inversion for Large Language Models. NeurIPS 2024 - [i12]Dimitar I. Dimitrov, Maximilian Baader, Mark Niklas Müller, Martin T. Vechev:
SPEAR: Exact Gradient Inversion of Batches in Federated Learning. CoRR abs/2403.03945 (2024) - [i11]Ivo Petrov, Dimitar I. Dimitrov, Maximilian Baader, Mark Niklas Müller, Martin T. Vechev:
DAGER: Exact Gradient Inversion for Large Language Models. CoRR abs/2405.15586 (2024) - [i10]Hristo Venev, Timon Gehr, Dimitar I. Dimitrov, Martin T. Vechev:
Modular Synthesis of Efficient Quantum Uncomputation. CoRR abs/2406.14227 (2024) - [i9]Anton Alexandrov, Veselin Raychev, Dimitar I. Dimitrov, Ce Zhang, Martin T. Vechev, Kristina Toutanova:
BgGPT 1.0: Extending English-centric LLMs to other languages. CoRR abs/2412.10893 (2024) - 2023
- [c8]Johan Lokna, Anouk Paradis, Dimitar I. Dimitrov, Martin T. Vechev:
Group and Attack: Auditing Differential Privacy. CCS 2023: 1905-1918 - [c7]Nikola Jovanovic, Mislav Balunovic, Dimitar Iliev Dimitrov, Martin T. Vechev:
FARE: Provably Fair Representation Learning with Practical Certificates. ICML 2023: 15401-15420 - [c6]Mark Vero, Mislav Balunovic, Dimitar Iliev Dimitrov, Martin T. Vechev:
TabLeak: Tabular Data Leakage in Federated Learning. ICML 2023: 35051-35083 - [i8]Kostadin Garov, Dimitar I. Dimitrov, Nikola Jovanovic, Martin T. Vechev:
Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning. CoRR abs/2306.03013 (2023) - 2022
- [j1]Dimitar Iliev Dimitrov, Mislav Balunovic, Nikola Konstantinov, Martin T. Vechev:
Data Leakage in Federated Averaging. Trans. Mach. Learn. Res. 2022 (2022) - [c5]Marc Fischer, Christian Sprecher, Dimitar I. Dimitrov, Gagandeep Singh, Martin T. Vechev:
Shared Certificates for Neural Network Verification. CAV (1) 2022: 127-148 - [c4]Mislav Balunovic, Dimitar Iliev Dimitrov, Robin Staab, Martin T. Vechev:
Bayesian Framework for Gradient Leakage. ICLR 2022 - [c3]Dimitar Iliev Dimitrov, Gagandeep Singh, Timon Gehr, Martin T. Vechev:
Provably Robust Adversarial Examples. ICLR 2022 - [c2]Mislav Balunovic, Dimitar I. Dimitrov, Nikola Jovanovic, Martin T. Vechev:
LAMP: Extracting Text from Gradients with Language Model Priors. NeurIPS 2022 - [i7]Dimitar I. Dimitrov, Mislav Balunovic, Nikola Jovanovic, Martin T. Vechev:
LAMP: Extracting Text from Gradients with Language Model Priors. CoRR abs/2202.08827 (2022) - [i6]Dimitar I. Dimitrov, Mislav Balunovic, Nikola Konstantinov, Martin T. Vechev:
Data Leakage in Federated Averaging. CoRR abs/2206.12395 (2022) - [i5]Mark Vero, Mislav Balunovic, Dimitar I. Dimitrov, Martin T. Vechev:
Data Leakage in Tabular Federated Learning. CoRR abs/2210.01785 (2022) - [i4]Nikola Jovanovic, Mislav Balunovic, Dimitar I. Dimitrov, Martin T. Vechev:
FARE: Provably Fair Representation Learning. CoRR abs/2210.07213 (2022) - 2021
- [c1]Gregory Bonaert, Dimitar I. Dimitrov, Maximilian Baader, Martin T. Vechev:
Fast and precise certification of transformers. PLDI 2021: 466-481 - [i3]Christian Sprecher, Marc Fischer, Dimitar I. Dimitrov, Gagandeep Singh, Martin T. Vechev:
Shared Certificates for Neural Network Verification. CoRR abs/2109.00542 (2021) - [i2]Mislav Balunovic, Dimitar I. Dimitrov, Robin Staab, Martin T. Vechev:
Bayesian Framework for Gradient Leakage. CoRR abs/2111.04706 (2021) - 2020
- [i1]Dimitar I. Dimitrov, Gagandeep Singh, Timon Gehr, Martin T. Vechev:
Scalable Inference of Symbolic Adversarial Examples. CoRR abs/2007.12133 (2020)
Coauthor Index
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