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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

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Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

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Blog Post number 2

less than 1 minute read

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Blog Post number 1

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Generalized smooth variational inequalities: Methods with adaptive stepsizes

Published in International Conference on Machine Learning (ICML) 2024, 2024

We introduce clipped first-order methods for solving non-monotone generalized smoothn variational inequalities, establishing the first convergence guarantees for gradient descent-ascent, extragarient, and optimistic methods that match or improve upon standard smooth rates. We also propose extragradient method with backtracking line-search that matches the same rates wihtout additional knoledge on the parameters.

Recommended citation: Vankov, Daniil, Angelia Nedich, and Lalitha Sankar. "Generalized smooth variational inequalities: Methods with adaptive stepsizes." In Forty-first International Conference on Machine Learning. 2024.
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Optimizing ($L_0, L_1$)-Smooth Functions by Gradient Methods

Published in ICLR 2025, 2025

Our analysis significantly improves the best-known complexity bounds of gradient method for convex optimization under generalized smoothness and establishes new best-known guarantees for accelerated gradient method.

Recommended citation: Vankov, Daniil, Anton Rodomanov, Angelia Nedich, Lalitha Sankar, and Sebastian U. Stich. "Optimizing ($L_0, L_1$)-Smooth Functions by Gradient Methods." In ICLR. 2025.
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Generalized Smooth Stochastic Variational Inequalities: Almost Sure Convergence and Convergence Rates

Published in Transactions on Machine Learning Research (accepted with J2C certification, top 10 % contribution), 2025

We show that clipped first-order methods converge a.s. and in expectation to a solution of stochastic variational inequalities without relying on standard smoothness or boundedness assumptions.

Recommended citation: Vankov, Daniil, Angelia Nedić, and Lalitha Sankar. "Generalized smooth stochastic variational inequalities: Almost sure convergence and convergence rates." Transactions on Machine Learning Research (2025).
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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.