111 citations · 338 across the 50 of their papers we have counts for
4 papers · 1 filter
Proximal Algorithms for Smoothed Online Convex Optimization with Predictions
Spandan Senapati, Ashwin Shenai, Ketan Rajawat
We consider a smoothed online convex optimization (SOCO) problem with predictions, where the learner has access to a finite lookahead window of time-varying stage costs, but suffer…
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning
Anis Elgabli, Chaouki Ben Issaid, Amrit S. Bedi +3
Newton-type methods are popular in federated learning due to their fast convergence. Still, they suffer from two main issues, namely: low communication efficiency and low privacy d…
Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood
Qiujiang Jin, Alec Koppel, Ketan Rajawat +1
Non-asymptotic analysis of quasi-Newton methods have gained traction recently. In particular, several works have established a non-asymptotic superlinear rate of $\mathcal{O}((1/\s…
Variational Bayesian Filtering with Subspace Information for Extreme Spatio-Temporal Matrix Completion
Charul Paliwal, Pravesh Biyani, Ketan Rajawat
Missing data is a common problem in real-world sensor data collection. The performance of various approaches to impute data degrade rapidly in the extreme scenarios of low data sam…