16 citations · 73 across the 29 of their papers we have counts for
5 papers · 1 filter
Fine-Tuning Diffusion Models via Intermediate Distribution Shaping
Gautham Govind Anil, Shaan Ul Haque, Nithish Kannen +3
Diffusion models are widely used for generative tasks across domains. Given a pre-trained diffusion model, it is often desirable to fine-tune it further either to correct for error…
Poisson Midpoint Method for Log Concave Sampling: Beyond the Strong Error Lower Bounds
Rishikesh Srinivasan, Dheeraj Nagaraj
We study the problem of sampling from strongly log-concave distributions over using the Poisson midpoint discretization (a variant of the randomized midpoint method)…
Beyond Propagation of Chaos: A Stochastic Algorithm for Mean Field Optimization
Chandan Tankala, Dheeraj M. Nagaraj, Anant Raj
Gradient flow in the 2-Wasserstein space is widely used to optimize functionals over probability distributions and is typically implemented using an interacting particle system wit…
Interleaved Gibbs Diffusion: Generating Discrete-Continuous Data with Implicit Constraints
Gautham Govind Anil, Sachin Yadav, Dheeraj Nagaraj +2
We introduce Interleaved Gibbs Diffusion (IGD), a novel generative modeling framework for discrete-continuous data, focusing on problems with important, implicit and unspecified co…
Dimension-free Score Matching and Time Bootstrapping for Diffusion Models
Syamantak Kumar, Dheeraj Nagaraj, Purnamrita Sarkar
Diffusion models generate samples by estimating the score function of the target distribution at various noise levels. The model is trained using samples drawn from the target dist…