27 citations · 50 across the 13 of their papers we have counts for
5 papers · 1 filter
On Representations of Mean-Field Variational Inference
Soumyadip Ghosh, Yingdong Lu, Tomasz Nowicki +1
The mean field variational inference (MFVI) formulation restricts the general Bayesian inference problem to the subspace of product measures. We present a framework to analyze MFVI…
Hamiltonian Monte Carlo with Asymmetrical Momentum Distributions
Soumyadip Ghosh, Yingdong Lu, Tomasz Nowicki
Existing rigorous convergence guarantees for the Hamiltonian Monte Carlo (HMC) algorithm use Gaussian auxiliary momentum variables, which are crucially symmetrically distributed. W…
Unbiased Gradient Estimation for Distributionally Robust Learning
Soumyadip Ghosh, Mark Squillante
Seeking to improve model generalization, we consider a new approach based on distributionally robust learning (DRL) that applies stochastic gradient descent to the outer minimizati…
Efficient Stochastic Gradient Descent for Learning with Distributionally Robust Optimization
Soumyadip Ghosh, Mark Squillante, Ebisa Wollega
Distributionally robust optimization (DRO) problems are increasingly seen as a viable method to train machine learning models for improved model generalization. These min-max formu…
Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD
Sanghamitra Dutta, Gauri Joshi, Soumyadip Ghosh +2
Distributed Stochastic Gradient Descent (SGD) when run in a synchronous manner, suffers from delays in waiting for the slowest learners (stragglers). Asynchronous methods can allev…