Research Scientist, Google DeepMind. Previous: @Xaira_Thera, PhD @oxcsml
Joined August 2021
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I will be attending @iclr_conf this week, please reach out if you'd like to chat about generative models or AI for science!
Very excited to share our preprint: Self-Speculative Masked Diffusions
We speed up sampling of masked diffusion models by ~2x by using speculative sampling and a hybrid non-causal / causal transformer
arxiv.org/abs/2510.03929
w/ @ValentinDeBort1 @thjashin @ArnaudDoucet1
Andrew Campbell retweeted
Super excited to preprint our work on developing a Biomolecular Emulator (BioEmu): Scalable emulation of protein equilibrium ensembles with generative deep learning from @MSFTResearch AI for Science.
#ML #AI #NeuralNetworks #Biology #AI4Science
biorxiv.org/content/10.1101/…
Andrew Campbell retweeted
Discrete generative models use denoisers for generation, but they can slip up. What if generation *isn’t only* about denoising?🤔
Introducing DDPD: Discrete Diffusion with Planned Denoising🤗🧵(1/11)
w/ @junonam_ @AndrewC_ML @HannesStaerk @xuyilun2 Tommi Jaakkola @RGBLabMIT
Andrew Campbell retweeted
Combining discrete and continuous data is an important capability for generative models. To address this for protein design, we introduce Multiflow, a generative model for structure and sequence generation.
Preprint: arxiv.org/abs/2402.04997
Code: github.com/jasonkyuyim/multi…
1/8
New paper: how to do flow matching on discrete data.
Flows give a simple generative framework and better performance than discrete diffusion models.
Discrete flows are easily combined with continuous flow matching for multimodal models.
arxiv.org/abs/2402.04997
A thread 1/7
We are giving a talk about the work this Tuesday 11am EST/4pm GMT @valence_ai portal.valencelabs.com/event…
Code for the pure discrete model: github.com/andrew-cr/discret…
Code for protein co-design experiments: github.com/jasonkyuyim/multi…
6/7
Big thanks to amazing co-lead @json_yim and our advisors @BarzilayRegina , @tom_rainforth , Tommi Jaakkola.
7/7
How can we apply diffusion models to data with varying dimensionality? We use jump diffusions to simultaneously generate the size and state values for varying size data e.g. molecules
arxiv.org/abs/2305.16261
w/ @willarvey @wh1lo @ValentinDeBort1 @tom_rainforth @ArnaudDoucet1
📰A Continuous Time Framework for Discrete Denoising Models
Operating in continuous time gives us higher performance generative samplers and error bounds on discrete spaces.
arxiv.org/abs/2205.14987
w/ @JoeJBenton @ValentinDeBort1 @tom_rainforth @GeorgeDeligian9 @ArnaudDoucet1
I have written a blog post describing our use of Reinforcement Learning to create an online objective for sequential Variational Inference
🌐 andrew-cr.github.io/posts/RL…
📰 arxiv.org/abs/2110.13549
Andrew Campbell retweeted
Online Variational Filtering and Parameter Learning arxiv.org/abs/2110.13549 with @AndrewC_ML @YuyangShi0 & @tom_rainforth accepted as Oral #NeurIPS2021.
We exploit forward Bellman recursions for the ELBO & its gradients in state-space models and RL ideas.
