7 citations · 17 across the 9 of their papers we have counts for
3 papers · 1 filter
Stochastic Optimization for Spectral Risk Measures
Ronak Mehta, Vincent Roulet, Krishna Pillutla +2
Spectral risk objectives - also called -risks - allow for learning systems to interpolate between optimizing average-case performance (as in empirical risk minimization) and wor…
Deep Unlearning via Randomized Conditionally Independent Hessians
Ronak Mehta, Sourav Pal, Vikas Singh +1
Recent legislation has led to interest in machine unlearning, i.e., removing specific training samples from a predictive model as if they never existed in the training dataset. Unl…
Graph Reparameterizations for Enabling 1000+ Monte Carlo Iterations in Bayesian Deep Neural Networks
Jurijs Nazarovs, Ronak R. Mehta, Vishnu Suresh Lokhande +1
Uncertainty estimation in deep models is essential in many real-world applications and has benefited from developments over the last several years. Recent evidence suggests that ex…