Max Qiushi Lin

I am a PhD student in Computer Science at Simon Fraser University, where I am fortunate to be advised by Prof. Sharan Vaswani. Prior to that, I worked with Prof. Hang Ma.

Email:  maxqslin   [AT]   gmail   [DOT]   com
[Google Scholar]     [Twitter]

Research

I am interested in machine learning theory. My current research explores the theoretical foundations of reinforcement learning and the development of principled and implementable algorithms that enable agents to learn from experience. I am also intrigued by probability theory, optimization, and statistics.

Papers

Provably Efficient Reinforcement Learning with General Utilities in Bilinear MDPs
Max Qiushi Lin, Ahmed Magd, Sharan Vaswani.
In Submission.

Augmented Lagrangian Method for Last-Iterate Convergence for Constrained MDPs
Michael Lu, Max Qiushi Lin, Mo Chen, Sharan Vaswani.
Preprint, 2025.

Optimistic Actor-Critic with Parametric Policies for Linear Markov Decision Processes
Max Qiushi Lin, Reza Asad, Kevin Tan, Haque Ishfaq, Csaba Szepesvári, Sharan Vaswani.
NeurIPS Workshop on Aligning Reinforcement Learning Experimentalists and Theorists (ARLET), 2025.
AISTATS, 2026.

Rethinking the Global Convergence of Softmax Policy Gradient with Linear Function Approximation
Max Qiushi Lin, Jincheng Mei, Matin Aghaei, Michael Lu, Bo Dai, Alekh Agarwal, Dale Schuurmans, Csaba Szepesvári, Sharan Vaswani.
Preprint, 2025.