Molei Tao
@MoleiTaoMath
Georgia Tech Prof; Tsinghua, Caltech, NYU Courant * deep learning theory * (diffusion) generative model, probabilistic ML * AI4Science * applied & comput. math
What is variational optimization? Why can continuous dynamics help? Optimization is already a profound field, what can it bring in? Check out blog itsdynamical.github.io/article/2023/0… Comment/Retweet/Like will be deeply appreciated! 1/6
I wish I could go to #ICML2025, but plz consider dropping by my student Yuchen Zhu's @YuchenZhu_ZYC fun posters! * Diffuse Everything * Learning to Stop
Interested in some foundation aspects? Waiting or unhappy about NeurIPS reviews? Plz consider NeurIPS workshop DynaFront: Dynamics at the Frontiers of Optimization, Sampling, and Games sites.google.com/view/dynafront… @yuejiec @Andrea__M @btreetaiji @T_Chavdarova ++ Sponsor appreciated!
I will present * accelerated manifold optimization, in LA (ICCOPT) Wed 7/23 * fine tuning of diffusion model and stochastic optimal control for sampling, in Montreal (SIAM) Tue 7/29 * fast sampling under nonconvex constraints, in Chicago (MCM) Thu 7/31 Love to chat and learn!
If still around #ICML2025, plz consider checking out my collaborator @qu_1006 's Oral in the MemFM Workshop, 11am Sat West Meeting Room 223-224, on A Closer Look at Model Collapse (in diffusion model): From a Generalization-to-Memorization Perspective
What if AI isn’t about building solo geniuses, but designing social systems? Michael Jordan advocates blending ML, economics, and uncertainty management to prioritize social welfare over mere prediction. A must-read rethink. arxiv.org/abs/2507.06268…
New lecture recordings on RL+LLM! 📺 This spring, I gave a lecture series titled **Reinforcement Learning of Large Language Models**. I have decided to re-record these lectures and share them on YouTube. (1/7)
Big thanks to the COLT 2025 organizers for an awesome event in Lyon! Here are the slides from my keynote this morning in case you’re curious about the references I mentioned: di.ens.fr/~fbach/fbach_o…
Fantastic reading, written by Prof. Damek Davis.
i wrote some notes on GPUs > how are they organized, what are the bottlenecks, how to measure and increase performance. they're based on what i learned reading @cHHillee and @Si_Boehm's blog posts. i wrote this just so I don't forget what i read.
Durastante, Gnazzo, Meini: A Riemannian Optimization Approach for Finding the Nearest Rev... arxiv.org/abs/2505.16762 arxiv.org/pdf/2505.16762 arxiv.org/html/2505.16762
Course notes: "Optimal Transport for Machine Learners" (by Gabriel Peyré): arxiv.org/abs/2505.06589
Will be at #ICLR2025 and love to chat about any of our 5 papers, or probabilistic ML / diffusion model, deep learning theory / optimization, and AI4Science in general. Feel free to DM!
Scientific Knowledge Emerges in LLMs and YOU CAN Access It (via sampling)! 🔥🔥🔥New blog to summarize what we have learned from evaluating LLMs for several optimization, decision-making, and planning problems in science with truly impressive performances!