1 citations · 1 across the 6 of their papers we have counts for
7 papers
Generalization Properties of Score-matching Diffusion Models for Intrinsically Low-dimensional Data
Saptarshi Chakraborty, Quentin Berthet, Peter L. Bartlett
Despite the remarkable empirical success of score-based diffusion models, their statistical guarantees remain underdeveloped. Existing analyses often provide pessimistic convergenc…
Efficient Logistic Regression with Mixture of Sigmoids
Federico Di Gennaro, Saptarshi Chakraborty, Nikita Zhivotovskiy
This paper studies the Exponential Weights (EW) algorithm with an isotropic Gaussian prior for online logistic regression. We show that the near-optimal worst-case regret bound $O(…
A Statistical Analysis for Supervised Deep Learning with Exponential Families for Intrinsically Low-dimensional Data
Saptarshi Chakraborty, Peter L. Bartlett
Recent advances have revealed that the rate of convergence of the expected test error in deep supervised learning decays as a function of the intrinsic dimension and not the dimens…
A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data
Saptarshi Chakraborty, Peter L. Bartlett
Despite significant research on the optimization aspects of federated learning, the exploration of generalization error, especially in the realm of heterogeneous federated learning…
Neural-g: A Deep Learning Framework for Mixing Density Estimation
Shijie Wang, Saptarshi Chakraborty, Qian Qin +1
Mixing (or prior) density estimation is an important problem in machine learning and statistics, especially in empirical Bayes -modeling where accurately estimating the prior is…
A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data
Saptarshi Chakraborty, Peter L. Bartlett
Variational Autoencoders (VAEs) have gained significant popularity among researchers as a powerful tool for understanding unknown distributions based on limited samples. This popul…