42 citations · 69 across the 6 of their papers we have counts for
4 papers · 1 filter
Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances
Hunter Lang, Aravind Reddy, David Sontag +1
Several works have shown that perturbation stable instances of the MAP inference problem in Potts models can be solved exactly using a natural linear programming (LP) relaxation. H…
Statistical Adaptive Stochastic Gradient Methods
Pengchuan Zhang, Hunter Lang, Qiang Liu +1
We propose a statistical adaptive procedure called SALSA for automatically scheduling the learning rate (step size) in stochastic gradient methods. SALSA first uses a smoothed stoc…
Using Statistics to Automate Stochastic Optimization
Hunter Lang, Pengchuan Zhang, Lin Xiao
Despite the development of numerous adaptive optimizers, tuning the learning rate of stochastic gradient methods remains a major roadblock to obtaining good practical performance i…
Block Stability for MAP Inference
Hunter Lang, David Sontag, Aravindan Vijayaraghavan
To understand the empirical success of approximate MAP inference, recent work (Lang et al., 2018) has shown that some popular approximation algorithms perform very well when the in…