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Nikola Konstantinov
Nikola Konstantinov
Tenure-track faculty, INSAIT
Verified email at insait.ai - Homepage
Title
Cited by
Cited by
Year
The convergence of sparsified gradient methods
D Alistarh, T Hoefler, M Johansson, N Konstantinov, S Khirirat, C Renggli
Advances in Neural Information Processing Systems, 5973-5983, 2018
4142018
Robust Learning from Untrusted Sources
N Konstantinov, C Lampert
International Conference on Machine Learning (ICML), 2019
612019
The convergence of stochastic gradient descent in asynchronous shared memory
D Alistarh, C De Sa, N Konstantinov
Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing …, 2018
392018
On the Sample Complexity of Adversarial Multi-Source PAC Learning
N Konstantinov, E Frantar, D Alistarh, CH Lampert
International Conference on Machine Learning (ICML), 2020
202020
Fairness-aware PAC learning from corrupted data
N Konstantinov, CH Lampert
Journal of Machine Learning Research 23 (160), 1-60, 2022
162022
On the Impossibility of Fairness-Aware Learning from Corrupted Data
N Konstantinov, CH Lampert
Algorithmic Fairness through the Lens of Causality and Robustness workshop …, 2022
62022
FLEA: Provably Fair Multisource Learning from Unreliable Training Data
E Iofinova, N Konstantinov, CH Lampert
arXiv preprint arXiv:2106.11732, 2021
62021
Fairness Through Regularization for Learning to Rank
N Konstantinov, CH Lampert
arXiv preprint arXiv:2102.05996, 2021
52021
Data Leakage in Federated Averaging
DI Dimitrov, M Balunovic, N Konstantinov, M Vechev
Transactions on Machine Learning Research, 2022
32022
Strategic Data Sharing between Competitors
N Tsoy, N Konstantinov
arXiv preprint arXiv:2305.16052, 2023
2023
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization
FE Dorner, N Konstantinov, G Pashaliev, M Vechev
arXiv preprint arXiv:2305.16272, 2023
2023
Human-Guided Fair Classification for Natural Language Processing
FE Dorner, M Peychev, N Konstantinov, N Goel, E Ash, M Vechev
arXiv preprint arXiv:2212.10154, 2022
2022
Robustness and fairness in machine learning
NH Konstantinov
2022
Generating Intuitive Fairness Specifications for Natural Language Processing
FE Dorner, M Peychev, N Konstantinov, N Goel, E Ash, M Vechev
Workshop on Trustworthy and Socially Responsible Machine Learning, NeurIPS 2022, 0
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Articles 1–14