Welcome

I am a tenure-track faculty member at INSAIT, in Sofia, Bulgaria, where I work on machine learning.

My research interests lie in the area of trustworthy machine learning and I am especially interested in providing mathematical guarantees for machine learning models. Sample of specific topics I am currently working on are:

• Making collaborative learning methods, such as federated learning, more robust and reliable.
• Studying topics at the intersection of machine learning and game theory, in particular incentives for data sharing.
• Understanding the impact of machine learning on society, in particular by analysing the fairness and long-term impact of machine learning models.

Before INSAIT I was a postdoctoral fellow at the ETH AI Center, working under the supervision of Prof. Martin Vechev and Prof. Fanny Yang. Prior to that I was a PhD student at IST Austria, working in the group of Prof. Christoph Lampert. I was also part of the ELLIS PhD Program.

Links: Google Scholar profile, LinkedIn, a full CV .

Papers

The order of the authors is by contribution, unless specified otherwise.

Preprints

Nikita Tsoy, Nikola Konstantinov
Strategic Data Sharing between Competitors
To be presented in: FL@ICML , 2023

Florian E. Dorner, Nikola Konstantinov, Giorgi Pashaliev, Martin Vechev
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization
To be presented in: AdvML@ICML , 2023

Nikola Konstantinov, Christoph H. Lampert
Fairness Through Regularization for Learning to Rank
Preprint, 2021

Publications

Florian E. Dorner, Momchil Peychev, Nikola Konstantinov, Naman Goel, Elliott Ash, Martin Vechev
Human-Guided Fair Classification for Natural Language Processing
In: International Conference on Learning Representations (ICLR), Spotlight , 2023
Short version presented in: TSRML@NeurIPS , 2022

Dimitar I. Dimitrov, Mislav Balunović, Nikola Konstantinov, Martin Vechev
Data Leakage in Federated Averaging
To appear in: Transactions of Machine Learning Research (TMLR) , 2022

Eugenia Iofinova*, Nikola Konstantinov*, Christoph H. Lampert
FLEA: Provably Fair Multisource Learning from Unreliable Training Data
To appear in: Transactions of Machine Learning Research (TMLR) , 2022
* Denotes equal contribution

Nikola Konstantinov, Christoph H. Lampert
Fairness-Aware PAC Learning from Corrupted Data
In: Journal of Machine Learning Research (JMLR) , 2022

Nikola Konstantinov, Christoph H. Lampert
On the Impossibility of Fairness-Aware Learning from Corrupted Data
Contributed talk + in proceedings of AFCR@NeurIPS , 2021

Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph H. Lampert 
On the Sample Complexity of Adversarial Multi-Source PAC Learning
In: International Conference on Machine Learning (ICML), 2020

Nikola Konstantinov, Christoph H. Lampert
Robust Learning from Untrusted Sources
In: International Conference on Machine Learning (ICML), 2019; Long Talk

Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov*, Sarit Khirirat, Cedric Renggli
The Convergence of Sparsified Gradient Methods
In: Conference on Neural Information Processing Systems (NeurIPS) , 2018
* Authors' order is alphabetical.

Dan Alistarh, Chris De Sa, Nikola Konstantinov*
The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory
In: ACM Symposium of Principles of Distributed Computing (PODC) , 2018
* Authors' order is alphabetical.