sardean

@sardean.bsky.social

asst prof @cornellbowers.bsky.social thinking about dynamics, control, machine learning sdean.website

working on some lecture notes to guide my revamped undergraduate reinforcement learning course this fall. (as you can from the page numbers, its still mostly blank.) we are going to dive into policy gradients before uttering the name Markov 😲

screenshot of pdf contents:

1 Introduction and Overview 3
2 Policies, Optimization, and Gradients 4
3 Sequential Decisions 18
4 Value Iteration 20
5 Policy Iteration 21
6 Online Decision-making 22
7 Receding Horizon Planning 23
8 Learning from Experts 24

tomorrow at ICML my student Sunmook will present a poster on this fun paper, led by postdoc Yahya Sattar: arxiv.org/abs/2606.12691 come for a rigorous view of "representation learning" and the latent space of "world models", stay for the linear systems theory and matrix factorization!

Two-Layer Linear Auto-Regressive Models Estimate Latent States

Auto-regressive models have emerged as powerful tools for sequential data, from language to video. Understanding how and why these models learn latent representations remains an open theoretical quest...

arxiv.org

tomorrow (today over in Korea) Haruka will be presenting this paper in the ICML. working on this project with her (and folks at Meta) is part of what convinced me to revamp my undergraduate RL course (more on that later). check out the poster at the Wednesday poster session! arxiv.org/abs/2605.26385

Credit-assigned Policy Gradient for Early Stage Retrieval in Two-stage Ranking

Large-scale search, recommendation, and retrieval-augmented generation (RAG) systems typically employ a two-stage architecture: an early-stage ranker (ESR) generates a candidate set, which is subseque...

arxiv.org

having just finished the lecture portion of my PhD level ML in Feedback Systems course, and it turns out that everything I understand in ML/control is basically linear least squares