Nikola Konstantinov

According to our database, Nikola Konstantinov authored at least 15 papers between 2018 and 2024.

Collaborative distances:

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

2024

Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains.

Nikita Tsoy, Anna Mihalkova, Teodora Todorova, Nikola Konstantinov

CoRR, 2024

2023

Strategic Data Sharing between Competitors.

Nikita Tsoy, Nikola Konstantinov

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Incentivizing Honesty among Competitors in Collaborative Learning and Optimization.

Florian E. Dorner, Nikola Konstantinov, Georgi Pashaliev, Martin T. Vechev

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Human-Guided Fair Classification for Natural Language Processing.

Florian E. Dorner, Momchil Peychev, Nikola Konstantinov, Naman Goel, Elliott Ash, Martin T. Vechev

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data.

Eugenia Iofinova, Nikola Konstantinov, Christoph H. Lampert

Trans. Mach. Learn. Res., 2022

Data Leakage in Federated Averaging.

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

Trans. Mach. Learn. Res., 2022

Fairness-Aware PAC Learning from Corrupted Data.

Nikola Konstantinov, Christoph H. Lampert

J. Mach. Learn. Res., 2022

2021

FLEA: Provably Fair Multisource Learning from Unreliable Training Data.

Eugenia Iofinova, Nikola Konstantinov, Christoph H. Lampert

CoRR, 2021

Fairness-Aware Learning from Corrupted Data.

Nikola Konstantinov, Christoph H. Lampert

CoRR, 2021

Fairness Through Regularization for Learning to Rank.

Nikola Konstantinov, Christoph H. Lampert

CoRR, 2021

On the Impossibility of Fairness-Aware Learning from Corrupted Data.

Nikola Konstantinov, Christoph H. Lampert

Proceedings of the Algorithmic Fairness through the Lens of Causality and Robustness Workshop, 2021

2020

On the Sample Complexity of Adversarial Multi-Source PAC Learning.

Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph Lampert

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Robust Learning from Untrusted Sources.

Nikola Konstantinov, Christoph Lampert

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory.

Dan Alistarh, Christopher De Sa, Nikola Konstantinov

Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing, 2018

The Convergence of Sparsified Gradient Methods.

Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, Cédric Renggli

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018