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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
5082018
Robust Learning from Untrusted Sources
N Konstantinov, C Lampert
International Conference on Machine Learning (ICML), 2019
752019
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
442018
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
252020
Fairness-aware PAC learning from corrupted data
N Konstantinov, CH Lampert
Journal of Machine Learning Research 23 (160), 1-60, 2022
232022
Data Leakage in Federated Averaging
DI Dimitrov, M Balunovic, N Konstantinov, M Vechev
Transactions on Machine Learning Research, 2022
142022
FLEA: Provably Fair Multisource Learning from Unreliable Training Data
E Iofinova, N Konstantinov, CH Lampert
arXiv preprint arXiv:2106.11732, 2021
92021
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
82022
Fairness Through Regularization for Learning to Rank
N Konstantinov, CH Lampert
arXiv preprint arXiv:2102.05996, 2021
82021
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization
FE Dorner, N Konstantinov, G Pashaliev, M Vechev
Conference on Neural Information Processing Systems (NeurIPS), 2023, 2023
32023
Strategic Data Sharing between Competitors
N Tsoy, N Konstantinov
Conference on Neural Information Processing Systems (NeurIPS), 2023, 2023
22023
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
22022
Robustness and fairness in machine learning
NH Konstantinov
12022
Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains
N Tsoy, A Mihalkova, T Todorova, N Konstantinov
To appear in: International Conference on Artificial Intelligence and …, 2024
2024
Incentivizing Honesty among Competitors in Collaborative Learning
FE Dorner, N Konstantinov, GS Pashaliev, M Vechev
The Second Workshop on New Frontiers in Adversarial Machine Learning, 2023
2023
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