2 papers
cs.DS2026
The Power of Test-Time Training for Approximate Sampling
Noah Golowich, Ankur Moitra, Dhruv Rohatgi
Efficiently sampling from a complex probability distribution is a fundamental problem which has become increasingly pertinent in recent years with the rise of generative AI, as sop…
cs.LG2026
The tractability landscape of diffusion alignment: regularization, rewards, and computational primitives
Ankur Moitra, Andrej Risteski, Dhruv Rohatgi
Inference-time reward alignment asks how to turn a pre-trained diffusion model with base law into a sampler that favors a reward while remaining close to . Since there i…