989 citations · 6.1k across the 184 of their papers we have counts for
6 papers · 1 filter
Modified Gauss-Newton Algorithms under Noise
Krishna Pillutla, Vincent Roulet, Sham Kakade +1
Gauss-Newton methods and their stochastic version have been widely used in machine learning and signal processing. Their nonsmooth counterparts, modified Gauss-Newton or prox-linea…
Sparsity in Partially Controllable Linear Systems
Yonathan Efroni, Sham Kakade, Akshay Krishnamurthy +1
A fundamental concept in control theory is that of controllability, where any system state can be reached through an appropriate choice of control inputs. Indeed, a large body of c…
Revisiting the Polyak step size
Elad Hazan, Sham Kakade
This paper revisits the Polyak step size schedule for convex optimization problems, proving that a simple variant of it simultaneously attains near optimal convergence rates for th…
Provably Correct Automatic Subdifferentiation for Qualified Programs
Sham Kakade, Jason D. Lee
The Cheap Gradient Principle (Griewank 2008) --- the computational cost of computing the gradient of a scalar-valued function is nearly the same (often within a factor of ) as t…
Stochastic subgradient method converges on tame functions
Damek Davis, Dmitriy Drusvyatskiy, Sham Kakade +1
This work considers the question: what convergence guarantees does the stochastic subgradient method have in the absence of smoothness and convexity? We prove that the stochastic s…
Stochastic convex optimization with bandit feedback
Alekh Agarwal, Dean P. Foster, Daniel Hsu +2
This paper addresses the problem of minimizing a convex, Lipschitz function over a convex, compact set $\xset$ under a stochastic bandit feedback model. In this model, the algo…