Dr. Nikola Konstantinov

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


Contact: [email protected]
Google Scholar: link


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 ML on society, in particular by analysing the fairness and long-term impact of ML models.


Academic degrees


Awards and recognitions


While at Oxford, I was also the President of the Oxford University Bulgarian Society 2015-2016.


Publications


At INSAIT

  1. Nikita Tsoy and Nikola Konstantinov
    Simplicity Bias of Two-Layer Networks beyond Linearly Separable Data
    To appear in: International Conference on Machine Learning (ICML), 2024.
  2. Nikita Tsoy, Anna Mihalkova, Teodora Todorova and Nikola Konstantinov
    Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains
    In: International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
  3. Nikita Tsoy and Nikola Konstantinov
    Strategic Data Sharing between Competitors.
    In: Conference on Neural Information Processing Systems (NeurIPS), 2023.
  4. Florian E. Dorner, Nikola Konstantinov, Georgi Pashaliev, Martin Vechev
    Incentivizing Honesty among Competitors in Collaborative Learning and Optimization.
    In: Conference on Neural Information Processing Systems (NeurIPS), 2023.


Before INSAIT

  1. 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
  2. Dimitar I. Dimitrov, Mislav Balunović, Nikola Konstantinov, Martin Vechev
    Data Leakage in Federated Averaging
    In: Transactions of Machine Learning Research (TMLR) , 2022
  3. Eugenia Iofinova*, Nikola Konstantinov*, Christoph H. Lampert
    FLEA: Provably Fair Multisource Learning from Unreliable Training Data
    In: Transactions of Machine Learning Research (TMLR) , 2022
    * Denotes equal contribution
  4. Nikola Konstantinov, Christoph H. Lampert
    Fairness-Aware PAC Learning from Corrupted Data
    In: Journal of Machine Learning Research (JMLR) , 2022
  5. Nikola Konstantinov, Christoph H. Lampert
    On the Impossibility of Fairness-Aware Learning from Corrupted Data
    Contributed talk + in proceedings of AFCR@NeurIPS , 2021
  6. 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
  7. Nikola Konstantinov, Christoph H. Lampert
    Robust Learning from Untrusted Sources
    In: International Conference on Machine Learning (ICML), 2019; Long Talk
  8. 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.
  9. 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.