7 Publications

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[7]
2022 | Thesis | IST-REx-ID: 10799 | OA
Konstantinov, Nikola H. Robustness and Fairness in Machine Learning. IST Austria, 2022, doi:10.15479/at:ista:10799.
View | Files available | DOI
 
[6]
2022 | Journal Article | IST-REx-ID: 10802 | OA
Konstantinov, Nikola H., and Christoph Lampert. “Fairness-Aware PAC Learning from Corrupted Data.” Journal of Machine Learning Research, vol. 23, 2022, pp. 1–60.
View | Files available | arXiv
 
[5]
2021 | Preprint | IST-REx-ID: 10803 | OA
Konstantinov, Nikola H., and Christoph Lampert. “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, Nikola H., et al. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.” Proceedings of the 37th International Conference on Machine Learning, vol. 119, ML Research Press, 2020, pp. 5416–25.
View | Files available | arXiv
 
[3]
2019 | Conference Paper | IST-REx-ID: 6590 | OA
Konstantinov, Nikola H., and Christoph Lampert. “Robust Learning from Untrusted Sources.” Proceedings of the 36th International Conference on Machine Learning, vol. 97, PMLR, 2019, pp. 3488–98.
View | Files available | Download Preprint (ext.) | arXiv
 
[2]
2018 | Conference Paper | IST-REx-ID: 5962 | OA
Alistarh, Dan-Adrian, et al. “The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory.” Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, ACM Press, 2018, pp. 169–78, doi:10.1145/3212734.3212763.
View | DOI | Download Preprint (ext.) | arXiv
 
[1]
2018 | Conference Paper | IST-REx-ID: 6589 | OA
Alistarh, Dan-Adrian, et al. “The Convergence of Sparsified Gradient Methods.” Advances in Neural Information Processing Systems 31, vol. Volume 2018, Neural information processing systems, 2018, pp. 5973–83.
View | Download Preprint (ext.) | arXiv
 

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7 Publications

Mark all

[7]
2022 | Thesis | IST-REx-ID: 10799 | OA
Konstantinov, Nikola H. Robustness and Fairness in Machine Learning. IST Austria, 2022, doi:10.15479/at:ista:10799.
View | Files available | DOI
 
[6]
2022 | Journal Article | IST-REx-ID: 10802 | OA
Konstantinov, Nikola H., and Christoph Lampert. “Fairness-Aware PAC Learning from Corrupted Data.” Journal of Machine Learning Research, vol. 23, 2022, pp. 1–60.
View | Files available | arXiv
 
[5]
2021 | Preprint | IST-REx-ID: 10803 | OA
Konstantinov, Nikola H., and Christoph Lampert. “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, Nikola H., et al. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.” Proceedings of the 37th International Conference on Machine Learning, vol. 119, ML Research Press, 2020, pp. 5416–25.
View | Files available | arXiv
 
[3]
2019 | Conference Paper | IST-REx-ID: 6590 | OA
Konstantinov, Nikola H., and Christoph Lampert. “Robust Learning from Untrusted Sources.” Proceedings of the 36th International Conference on Machine Learning, vol. 97, PMLR, 2019, pp. 3488–98.
View | Files available | Download Preprint (ext.) | arXiv
 
[2]
2018 | Conference Paper | IST-REx-ID: 5962 | OA
Alistarh, Dan-Adrian, et al. “The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory.” Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, ACM Press, 2018, pp. 169–78, doi:10.1145/3212734.3212763.
View | DOI | Download Preprint (ext.) | arXiv
 
[1]
2018 | Conference Paper | IST-REx-ID: 6589 | OA
Alistarh, Dan-Adrian, et al. “The Convergence of Sparsified Gradient Methods.” Advances in Neural Information Processing Systems 31, vol. Volume 2018, Neural information processing systems, 2018, pp. 5973–83.
View | Download Preprint (ext.) | arXiv
 

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Citation Style: MLA

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