156 citations · 185 across the 8 of their papers we have counts for
9 papers · 1 filter
How I learned to stop worrying and love StopGrads: Stationarity, Convergence, and a case study on Flow Map Learning
Max W. Shen, Mark Goldstein, Zichu Wang +2
Stopgrads are widely used in training machine learning models, but stopgrads can alter the gradient, stationary points and convergence guarantees of the original objective, which c…
Flow Map Learning via Nongradient Vector Flow
Mark Goldstein, Anshuk Uppal, Raghav Singhal +2
Diffusion and flow-based models benefit from simple regression losses, but inference incurs significant overhead because sampling requires integration. Consistency models address t…
Time After Time: Deep-Q Effect Estimation for Interventions on When and What to do
Yoav Wald, Mark Goldstein, Yonathan Efroni +2
Problems in fields such as healthcare, robotics, and finance requires reasoning about the value both of what decision or action to take and when to take it. The prevailing hope is…
Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities
Adriel Saporta, Aahlad Puli, Mark Goldstein +1
Contrastive learning methods, such as CLIP, leverage naturally paired data-for example, images and their corresponding text captions-to learn general representations that transfer…
What's the score? Automated Denoising Score Matching for Nonlinear Diffusions
Raghav Singhal, Mark Goldstein, Rajesh Ranganath
Reversing a diffusion process by learning its score forms the heart of diffusion-based generative modeling and for estimating properties of scientific systems. The diffusion proces…
Stochastic interpolants with data-dependent couplings
Michael S. Albergo, Mark Goldstein, Nicholas M. Boffi +2
Generative models inspired by dynamical transport of measure -- such as flows and diffusions -- construct a continuous-time map between two probability densities. Conventionally, o…