Current Stack of Interesting Literature

Amir Beck's excellent text: First-Order Methods In Optimization. A really well-written text bringing together a wealth of material on fundamental optimization theory and first order convex optimization algorithms
Nemirovski's recent update to his classical, Lectures on Modern Convex Optimization. It's first three chapters are the natural next step after internalizing the MOSEK modeling cookbook for understanding conic optimization!
An introduction to optimization on smooth manifolds: The title! Written by Nicolas Boumal (current maintaner of PyManOPT!)
Computational Optimal Transport: A reall well-written introduction to Optimal Transport for those with a solid mathematical background (co-authored by none other than Marco Cuturi!)
Semidefinite approximations of the matrix logarithm: Find (an evolving) commentary on the paper HERE
Distributional Reinforcement Learning with Quantile Regression — for a comprehensive introduction to distributional RL see HERE
Practical Near Neighbour Search via Group testing — really cool work that uses the notion of Distance Sensitive Bloom Filters coupled with ideas from Group Testing to devise a really efficient framework for the approximate nearest neighbor search problem (they outperform FAISS by multiple factors!)
Numerical Linear Algebra, Lloyd N. Trefethen and David Bau III: Need to iron out my understanding of NLA, and this classical text is the best there is for a (relatively) self-contained course! Can then graduate to using Matrix Computations as a reference for all my LA needs like every sane applied mathematician XD.
Introduction to Online Convex Optimization: Elad Hazan's text on OCO. Online learning is really cool, and I wanna learn about it!
Convex Optimization: Algorithms and Complexity: A beautiful monograph on the algorithmics of Convex optimization, plan on reading in conjunction with Boyd's theory portion.
Non-Convex optimization for Machine Learning: Prateek Jain's monograph on some broad ideas in non-convex optimization, really exciting stuff!
Tengyu Ma's StatML notes: Already have some background in learning theory from Shai's excellent text — plan to use Ma's notes as a reference while going through the above stuff, his notes also have some material on NTK ideas!
A User's guide to Measure Theoretic Probability Theory, David Pollard: An amazing text giving a self-contained tour of MTPT (ought to probably be sufficient for wannabe applied mathematician like me XD).