Jihao Andreas Lin

Excited to share our ICML 2025 paper: "Scalable Gaussian Processes with Latent Kronecker Structure" We unlock efficient linear algebra for your kernel matrix which *almost* has Kronecker product structure. Check out our paper here: arxiv.org/abs/2506.06895

Exited to share our new paper accepted by ICML 2025 👉 “PTSD: Progressive Tempering Sampler with Diffusion” , which aims to make sampling from unnormalised densities more efficient than state-of-the-art methods like parallel tempering. Check our threads below 👇

Looking for Postdoc/Research Assistant to work on deep generative models, with a focus on the domain of molecules. Application deadline 3 July 2025. More info: jobs.cam.ac.uk/job/51666/

New revision of “BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching” 🚀 (1/6) We introduce NEM and BNEM — diffusion&energy-based neural samplers for Boltzmann distributions. 📝 Read our paper at [arxiv.org/pdf/2409.09787]

1/7 Still using Adam? If anyone wants to try a distributed PyTorch implementation of SOAP/eigenvalue-corrected Shampoo with support for low precision data types instead, here you go. github.com/facebookresear…

Diffusion models are so ubiquitous, but it's difficult to find an introduction that is concise, simple and comprehensive. My supervisor Rich Turner (with me & some other students) has written an introduction to diffusion models that fills this gap: arxiv.org/abs/2402.04384

How can we get VLMs to help robots solve complex long-horizon tasks? Introducing VisualPredicator: an agent that leverages VLMs to learn predicates and operators for classical planners. Our system can stack blocks, balance weights on a balance beam, and even pour coffee🦾! [1/9]

Never ask a woman her age, a man his salary or a Cambridge machine learning department why waste taxpayer funds on frameworks that neither work nor scale like on Gaussian processes or Bayesian deep nets. #bayesianism