PhD Student, Vanier Scholar
University of Toronto, Department of Statistical Sciences
Vector Institute
negrea AT utstat.toronto.edu
Biography
I’m a PhD candidate and Vanier Scholar at the University of Toronto Department of Statistical Sciences working with professors Daniel Roy and Jeffrey Rosenthal on machine learning, online learning, and computational statistics.
Interests
- Online Learning
- Computational Statistics
- Statistical Learning Theory
- Applied Probability
- Stochastic Analysis
- Markov Chains
Education
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PhD in Statistical Sciences, (Ongoing)
University of Toronto
-
MSc in Statistical Sciences, 2017
University of Toronto
-
BMath Mathematical Finance, 2014
University of Waterloo
Journal Articles
(2021).
Approximations of Geometrically Ergodic Reversible Markov Chains.
Advances in Applied Probability (forthcoming).
Refereed Conference Papers
(2020).
In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors.
Proceedings of the 37th International Conference on Machine Learning.
(2020).
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms.
Advances in Neural Information Processing Systems 33.
(2019).
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates.
Advances in Neural Information Processing Systems 32.