2 papers
cs.LG2026
Sparse Priors for Efficient Distribution Learning
Saumya Goyal, Barnabás Póczos
Despite the widespread use and success of generative AI techniques today, theoretical guarantees on learning a distribution supported in dimensions from samples degrade as…
cs.LG2026
Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates
Saumya Goyal, Rohith Rongali, Ritabrata Ray +1
We study learning to learn through the lens of hyperparameter tuning. We propose the Langevin Gradient Descent Algorithm (LGD), which approximates the mean of the posterior distrib…