7 Publications

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[7]
2022 | Thesis | IST-REx-ID: 10799 | OA
Konstantinov, N. H. (2022). Robustness and fairness in machine learning. IST Austria. https://doi.org/10.15479/at:ista:10799
View | Files available | DOI
 
[6]
2022 | Journal Article | IST-REx-ID: 10802 | OA
Konstantinov, N. H., & Lampert, C. (2022). Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research.
View | Files available | arXiv
 
[5]
2021 | Preprint | IST-REx-ID: 10803 | OA
Konstantinov, N. H., & Lampert, C. (n.d.). Fairness through regularization for learning to rank. arXiv.
View | Files available | Download Preprint (ext.) | arXiv
 
[4]
2020 | Conference Paper | IST-REx-ID: 8724 | OA
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.
View | Files available | arXiv
 
[3]
2019 | Conference Paper | IST-REx-ID: 6590 | OA
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: PMLR.
View | Files available | Download Preprint (ext.) | arXiv
 
[2]
2018 | Conference Paper | IST-REx-ID: 5962 | OA
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
View | DOI | Download Preprint (ext.) | arXiv
 
[1]
2018 | Conference Paper | IST-REx-ID: 6589 | OA
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.
View | Download Preprint (ext.) | arXiv
 

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7 Publications

Mark all

[7]
2022 | Thesis | IST-REx-ID: 10799 | OA
Konstantinov, N. H. (2022). Robustness and fairness in machine learning. IST Austria. https://doi.org/10.15479/at:ista:10799
View | Files available | DOI
 
[6]
2022 | Journal Article | IST-REx-ID: 10802 | OA
Konstantinov, N. H., & Lampert, C. (2022). Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research.
View | Files available | arXiv
 
[5]
2021 | Preprint | IST-REx-ID: 10803 | OA
Konstantinov, N. H., & Lampert, C. (n.d.). Fairness through regularization for learning to rank. arXiv.
View | Files available | Download Preprint (ext.) | arXiv
 
[4]
2020 | Conference Paper | IST-REx-ID: 8724 | OA
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.
View | Files available | arXiv
 
[3]
2019 | Conference Paper | IST-REx-ID: 6590 | OA
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: PMLR.
View | Files available | Download Preprint (ext.) | arXiv
 
[2]
2018 | Conference Paper | IST-REx-ID: 5962 | OA
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
View | DOI | Download Preprint (ext.) | arXiv
 
[1]
2018 | Conference Paper | IST-REx-ID: 6589 | OA
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.
View | Download Preprint (ext.) | arXiv
 

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