Tomas Vaškevičius

Publications and Preprints

An up-to-date list of my publications can be found on arXiv and on my Google scholar page.

• L. Chizat and T. Vaškevičius. Computational guarantees for doubly entropic Wasserstein barycenters via damped Sinkhorn Iterations. Conference on Neural Information Processing Systems (NeurIPS), vol. 36, pp. 12368-12388, 2023, arXiv 2307.13370, link to proceedings, link to code.
• J. Mourtada, T. Vaškevičius, N. Zhivotovskiy. Local risk bounds for statistical aggregation. Proceedings of Thirty Sixth Conference on Learning Theory (COLT), PMLR 195:5697-5698, 2023, arXiv 2306.17151, link to proceedings.
• V. Kanade, P. Rebeschini, T. Vaškevičius. Exponential tail local Rademacher complexity risk bounds without the Bernstein condition. Preprint, 2022, arXiv 2202.11461.
• J. Mourtada, T. Vaškevičius, N. Zhivotovskiy, Distribution-free robust linear regression. Mathematical Statistics and Learning, vol. 4, no. 3, pp. 253-292, 2022, arXiv 2102.12919, link to journal.
• T. Vaškevičius, N. Zhivotovskiy. Suboptimality of constrained least squares and improvements via non-linear predictors. Bernoulli, vol. 29, no. 1, pp. 473-495, 2023, arXiv 2009.09304, link to journal.
• V. Kanade, P. Rebeschini, T. Vaškevičius. The statistical complexity of early-stopped mirror descent. Information and Inference: A Journal of the IMA, vol. 12, pp. 3010-3041, 2023, arXiv 2002.00189, link to journal, link to code.
  Earlier version appeared at NeurIPS 2020 (spotlight presentation).
• V. Kanade, P. Rebeschini, T. Vaškevičius. Implicit regularization for optimal sparse recovery. Conference on Neural Information Processing Systems (NeurIPS), vol. 32, pp. 2972-2983, 2019, arXiv 1909.05122, link to proceedings, link to code.

Teaching Experience

• I developed interactive learning materials using Python notebooks for a five-day summer school on mathematics of machine learning (learning materials available for independent study). This summer school was organized by Patrick Rebeschini, and co-sponsored by Google and the Alan Turing Institute.
• At EPFL, I am a teaching assistant for the "Mathematics of Machine Learning" course (MATH-520) during the fall semesters of 2022 and 2023.
• I have been a class tutor and a teaching assistant for the following courses taught at the University of Oxford:
  
  
  • Algorithmic Foundations of Learning (years 2018, 2019, 2020);
  • Computational Learning Theory (years 2019 and 2021; for teaching this course I received a teaching award);
  • Foundations of Statistical Inference (year 2020);
  • Advanced Topics in Statistical Machine Learning (year 2019).