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ORCIDNikola Konstantinov
Person information
- affiliation: INSAIT, Sofia University "St. Kliment Ohridski", Bulgaria
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2020 – today
- 2025
[i19]Nikita Tsoy, Ivan Kirev, Negin Rahimiyazdi, Nikola Konstantinov:
On the Impact of Performative Risk Minimization for Binary Random Variables. CoRR abs/2502.02331 (2025)- [i18]Kristian Minchev, Dimitar Iliev Dimitrov, Nikola Konstantinov:
LARP: Learner-Agnostic Robust Data Prefiltering. CoRR abs/2506.20573 (2025) - [i17]Ivan Kirev, Lyuben Baltadzhiev, Nikola Konstantinov:
PAC Learnability in the Presence of Performativity. CoRR abs/2510.08335 (2025) - [i16]Nikita Tsoy, Nikola Konstantinov:
On Measuring Localization of Shortcuts in Deep Networks. CoRR abs/2510.26560 (2025) - 2024
- [c10]Nikita Tsoy, Anna Mihalkova, Teodora N. Todorova, Nikola Konstantinov:
Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains. AISTATS 2024: 4798-4806 - [c9]Nikita Tsoy, Nikola Konstantinov:
Simplicity Bias of Two-Layer Networks beyond Linearly Separable Data. ICML 2024 - [i15]Nikita Tsoy
, Anna Mihalkova, Teodora Todorova, Nikola Konstantinov:
Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains. CoRR abs/2403.06672 (2024) - [i14]Nikita Tsoy, Nikola Konstantinov:
Simplicity Bias of Two-Layer Networks beyond Linearly Separable Data. CoRR abs/2405.17299 (2024) - [i13]Philipp Guldimann, Alexander Spiridonov, Robin Staab, Nikola Jovanovic, Mark Vero, Velko Vechev, Anna Gueorguieva, Mislav Balunovic, Nikola Konstantinov, Pavol Bielik, Petar Tsankov, Martin T. Vechev:
COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act. CoRR abs/2410.07959 (2024) - [i12]Dimitar Chakarov, Nikita Tsoy, Kristian Minchev, Nikola Konstantinov:
Incentivizing Truthful Collaboration in Heterogeneous Federated Learning. CoRR abs/2412.00980 (2024) - 2023
- [c8]Florian E. Dorner, Momchil Peychev, Nikola Konstantinov, Naman Goel, Elliott Ash, Martin T. Vechev:
Human-Guided Fair Classification for Natural Language Processing. ICLR 2023 - [c7]Florian E. Dorner, Nikola Konstantinov, Georgi Pashaliev, Martin T. Vechev:
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization. NeurIPS 2023 - [c6]Nikita Tsoy, Nikola Konstantinov:
Strategic Data Sharing between Competitors. NeurIPS 2023 - [i11]Nikita Tsoy, Nikola Konstantinov:
Strategic Data Sharing between Competitors. CoRR abs/2305.16052 (2023) - [i10]Florian E. Dorner, Nikola Konstantinov, Georgi Pashaliev, Martin T. Vechev:
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization. CoRR abs/2305.16272 (2023) - 2022
- [j3]Nikola Konstantinov, Christoph H. Lampert:
Fairness-Aware PAC Learning from Corrupted Data. J. Mach. Learn. Res. 23: 160:1-160:60 (2022) - [j2]Dimitar Iliev Dimitrov, Mislav Balunovic, Nikola Konstantinov, Martin T. Vechev:
Data Leakage in Federated Averaging. Trans. Mach. Learn. Res. 2022 (2022) - [j1]Eugenia Iofinova, Nikola Konstantinov, Christoph H. Lampert:
FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data. Trans. Mach. Learn. Res. 2022 (2022) - [i9]Dimitar I. Dimitrov, Mislav Balunovic, Nikola Konstantinov, Martin T. Vechev:
Data Leakage in Federated Averaging. CoRR abs/2206.12395 (2022) - [i8]Florian E. Dorner, Momchil Peychev, Nikola Konstantinov, Naman Goel, Elliott Ash, Martin T. Vechev:
Human-Guided Fair Classification for Natural Language Processing. CoRR abs/2212.10154 (2022) - 2021
- [c5]Nikola Konstantinov, Christoph H. Lampert:
On the Impossibility of Fairness-Aware Learning from Corrupted Data. AFCR 2021: 59-83 - [i7]Nikola Konstantinov, Christoph H. Lampert:
Fairness Through Regularization for Learning to Rank. CoRR abs/2102.05996 (2021) - [i6]Nikola Konstantinov, Christoph H. Lampert:
Fairness-Aware Learning from Corrupted Data. CoRR abs/2102.06004 (2021) - [i5]Eugenia Iofinova, Nikola Konstantinov, Christoph H. Lampert:
FLEA: Provably Fair Multisource Learning from Unreliable Training Data. CoRR abs/2106.11732 (2021) - 2020
- [c4]Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph Lampert:
On the Sample Complexity of Adversarial Multi-Source PAC Learning. ICML 2020: 5416-5425 - [i4]Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph H. Lampert:
On the Sample Complexity of Adversarial Multi-Source PAC Learning. CoRR abs/2002.10384 (2020)
2010 – 2019
- 2019
- [c3]Nikola Konstantinov, Christoph Lampert:
Robust Learning from Untrusted Sources. ICML 2019: 3488-3498 - [i3]Nikola Konstantinov, Christoph Lampert:
Robust Learning from Untrusted Sources. CoRR abs/1901.10310 (2019) - 2018
- [c2]Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, Cédric Renggli:
The Convergence of Sparsified Gradient Methods. NeurIPS 2018: 5977-5987 - [c1]Dan Alistarh, Christopher De Sa, Nikola Konstantinov:
The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory. PODC 2018: 169-178 - [i2]Dan Alistarh, Christopher De Sa, Nikola Konstantinov:
The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory. CoRR abs/1803.08841 (2018) - [i1]Dan Alistarh, Torsten Hoefler, Mikael Johansson, Sarit Khirirat, Nikola Konstantinov, Cédric Renggli:
The Convergence of Sparsified Gradient Methods. CoRR abs/1809.10505 (2018)
Coauthor Index
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last updated on 2025-11-18 23:44 CET by the dblp team
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