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Career

· Assistant Professor, Columbia University2024–
· Ph.D. in Electrical Engineering and Computer Science, UC Berkeley2019–

Publications (27)

Projection Robust Wasserstein Distance and Riemannian Optimization
Neural Information Processing Systems · 2020
78
cited
Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization
Neural Information Processing Systems · 2022
73
cited
On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification
International Conference on Artificial Intelligence and Statistics · 2020
61
cited
Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm
Neural Information Processing Systems · 2020
55
cited
Deterministic Nonsmooth Nonconvex Optimization
Annual Conference Computational Learning Theory · 2023
33
cited
First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems
Journal of machine learning research · 2022
31
cited
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization
International Conference on Artificial Intelligence and Statistics · 2021
28
cited
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces
Neural Information Processing Systems · 2022
22
cited
Perseus: a simple and optimal high-order method for variational inequalities
Mathematical programming · 2022
21
cited
Doubly Optimal No-Regret Online Learning in Strongly Monotone Games with Bandit Feedback
Operational Research · 2021
20
cited
Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization
arXiv.org · 2024
20
cited
Accelerating Adaptive Cubic Regularization of Newton's Method via Random Sampling
Journal of machine learning research · 2018
19
cited
Perseus: A Simple High-Order Regularization Method for Variational Inequalities
arXiv.org · 2022
18
cited
Explicit Second-Order Min-Max Optimization Methods with Optimal Convergence Guarantee
arXiv.org · 2022
16
cited
On the Complexity of Deterministic Nonsmooth and Nonconvex Optimization
arXiv.org · 2022
10
cited
Adaptive, Doubly Optimal No-Regret Learning in Strongly Monotone and Exp-Concave Games with Gradient Feedback
Operational Research · 2023
9
cited
New Proximal Newton-Type Methods for Convex Optimization
IEEE Conference on Decision and Control · 2020
7
cited
A Nonasymptotic Analysis of Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
Social Science Research Network · 2022
5
cited
A Variational Inequality Approach to Bayesian Regression Games
IEEE Conference on Decision and Control · 2021
5
cited
Last-Iterate Convergence of Adaptive Riemannian Gradient Descent for Equilibrium Computation
2023
4
cited
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Tianyi Lin | Researcher Profile | Sotabase | Sotabase