Nikola Konstantinov

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 25

Top Topics

Interprocess Communication

Sample Complexity

Natural Language Processing

Top Venues

Trans. Mach. Learn. Res.

Publications

Nikita Tsoy, Nikola Konstantinov
Simplicity Bias of Two-Layer Networks beyond Linearly Separable Data. CoRR (2024)
Nikita Tsoy, Anna Mihalkova, Teodora N. Todorova, Nikola Konstantinov
Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains. AISTATS (2024)
Nikita Tsoy, Anna Mihalkova, Teodora Todorova, Nikola Konstantinov
Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains. CoRR (2024)
Florian E. Dorner, Nikola Konstantinov, Georgi Pashaliev, Martin T. Vechev
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization. NeurIPS (2023)
Florian E. Dorner, Momchil Peychev, Nikola Konstantinov, Naman Goel, Elliott Ash, Martin T. Vechev
Human-Guided Fair Classification for Natural Language Processing. ICLR (2023)
Florian E. Dorner, Nikola Konstantinov, Georgi Pashaliev, Martin T. Vechev
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization. CoRR (2023)
Nikita Tsoy, Nikola Konstantinov
Strategic Data Sharing between Competitors. NeurIPS (2023)
Nikita Tsoy, Nikola Konstantinov
Strategic Data Sharing between Competitors. CoRR (2023)
Nikola Konstantinov, Christoph H. Lampert
Fairness-Aware PAC Learning from Corrupted Data. J. Mach. Learn. Res. 23 (2022)
Dimitar Iliev Dimitrov, Mislav Balunovic, Nikola Konstantinov, Martin T. Vechev
Data Leakage in Federated Averaging. Trans. Mach. Learn. Res. 2022 (2022)
Florian E. Dorner, Momchil Peychev, Nikola Konstantinov, Naman Goel, Elliott Ash, Martin T. Vechev
Human-Guided Fair Classification for Natural Language Processing. CoRR (2022)
Dimitar I. Dimitrov, Mislav Balunovic, Nikola Konstantinov, Martin T. Vechev
Data Leakage in Federated Averaging. CoRR (2022)
Eugenia Iofinova, Nikola Konstantinov, Christoph H. Lampert
FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data. Trans. Mach. Learn. Res. 2022 (2022)
Nikola Konstantinov, Christoph H. Lampert
On the Impossibility of Fairness-Aware Learning from Corrupted Data. AFCR (2021)
Nikola Konstantinov, Christoph H. Lampert
Fairness-Aware Learning from Corrupted Data. CoRR (2021)
Nikola Konstantinov, Christoph H. Lampert
Fairness Through Regularization for Learning to Rank. CoRR (2021)
Eugenia Iofinova, Nikola Konstantinov, Christoph H. Lampert
FLEA: Provably Fair Multisource Learning from Unreliable Training Data. CoRR (2021)
Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph Lampert
On the Sample Complexity of Adversarial Multi-Source PAC Learning. ICML (2020)
Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph H. Lampert
On the Sample Complexity of Adversarial Multi-Source PAC Learning. CoRR (2020)
Nikola Konstantinov, Christoph Lampert
Robust Learning from Untrusted Sources. ICML (2019)
Nikola Konstantinov, Christoph Lampert
Robust Learning from Untrusted Sources. CoRR (2019)
Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, Cédric Renggli
The Convergence of Sparsified Gradient Methods. NeurIPS (2018)
Dan Alistarh, Christopher De Sa, Nikola Konstantinov
The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory. CoRR (2018)
Dan Alistarh, Torsten Hoefler, Mikael Johansson, Sarit Khirirat, Nikola Konstantinov, Cédric Renggli
The Convergence of Sparsified Gradient Methods. CoRR (2018)
Dan Alistarh, Christopher De Sa, Nikola Konstantinov
The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory. PODC (2018)