1) In practice, ProxDM can provide samples from the data distribution much faster than comparable methods based on the score like DDPM from (Ho et al, 2020) and even comparable to their ODE alternatives (which are much faster) (9/n)

So, in order to implement a Proximal/backward version of diffusion models, we need a (cheap!) way of solving this optimization problem, i.e. computing the proximal of the log densities at every single time step. If only there was a way… oh, in come Learned Proximal Networks (7/n)

What are proximal operators? You can think of them as generalizations of projection operators. For a given (proximable) functional \rho(x), its proximal is defined by the solution of a simple optimization problem: (6/n)

Backward discretization of diff. eqs. has been long studied (c.f. gradient descent vs proximal point method). Let’s go ahead and discretize the same SDE, but backwards! One problem: the update is defined implicitly... But it does admit a close form expression in terms of proximal operators! (5/n)

While elegant in continuous time, one needs to discretize the SDE to implement it in practice. In DF, this has always been done through forward discretization (e.g Euler-Maruyama), which combines a gradient step of the data distribution at the discrete time t (the *score*), and Gaussian noise: (3/n)

First, a (very) brief overview of diffusion models (DM). DM work by simulating a process that converts samples from a distribution (random noise) to samples from target distribution of interest. This process is modeled mathematically with a stochastic differential equation (SDE) (2/n)

Check this out 📢 Score-based diffusion models are powerful—but slow to sample. Could there be something better? Drop the scores, use proximals instead!

We present Proximal Diffusion Models, providing a faster alternative both in theory* and practice. Here’s how it works 🧵(1/n)

Today, on #WomenInScience day, this paper on biomarker discovery for breast cancer, by my amazing student Zhenzhen, has just appeared in @cp-patterns.bsky.social
🎉 Her work shows how to construct fully interpretable biomarkers employing bi-level graph learning! @jhu.edu @hopkinsdsai.bsky.social

📣 What should *ML explanations* convey, and how does one report these precisely and rigorously? @neuripsconf.bsky.social
come check
Jacopo Teneggi's work on Testing for Explanations via betting this afternoon! I *bet* you'll like it :) openreview.net/pdf?id=A0HSm... @hopkinsdsai.bsky.social