Iofinova, E. B., Konstantinov, N. H., &; Lampert, C. (2022). FLEA: Provably robust fair multisource learning from unreliable training data. Transactions on Machine Learning Research. ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/12495
arXiv: 2106.11732

Konstantinov, N. H., &; Lampert, C. (2022). Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research. ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/10802
arXiv: 2102.06004

Konstantinov, N. H., &; Lampert, C. (2022). On the impossibility of fairness-aware learning from corrupted data. In Proceedings of Machine Learning Research (Vol. 171, pp. 59–83). ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/13241
arXiv: 2102.06004

Konstantinov, N. H. (2022). Robustness and fairness in machine learning. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:10799 
LibreCat: https://research-explorer.ista.ac.at/record/10799
DOI: 10.15479/at:ista:10799

Konstantinov, N. H., &; Lampert, C. (n.d.). Fairness through regularization for learning to rank. arXiv.https://doi.org/10.48550/arXiv.2102.05996 
LibreCat: https://research-explorer.ista.ac.at/record/10803
DOI: 10.48550/arXiv.2102.05996
arXiv: 2102.05996

Konstantinov, N. H., Frantar, E., Alistarh, D.-A., &; Lampert, C. (2020). On the sample complexity of adversarial multi-source PAC learning. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 5416–5425). Online: ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/8724
arXiv: 2002.10384

Konstantinov, N. H., &; Lampert, C. (2019). Robust learning from untrusted sources. In Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 3488–3498). Long Beach, CA, USA: ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/6590
arXiv: 1901.10310

Alistarh, D.-A., De Sa, C., &; Konstantinov, N. H. (2018). The convergence of stochastic gradient descent in asynchronous shared memory. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 169–178). Egham, United Kingdom: ACM Press.https://doi.org/10.1145/3212734.3212763 
LibreCat: https://research-explorer.ista.ac.at/record/5962
DOI: 10.1145/3212734.3212763
WoS: 000458186900022
arXiv: 1803.08841

Alistarh, D.-A., Hoefler, T., Johansson, M., Konstantinov, N. H., Khirirat, S., &; Renggli, C. (2018). The convergence of sparsified gradient methods. In Advances in Neural Information Processing Systems 31 (Vol. Volume 2018, pp. 5973–5983). Montreal, Canada: Neural Information Processing Systems Foundation.
LibreCat: https://research-explorer.ista.ac.at/record/6589
WoS: 000461852000047
arXiv: 1809.10505