The efficiency of MSDA against state-of-the-art methods for two problems: least-squares regression and classification by logistic regression is verified.Expand

This paper improves upon the running time for finding a point in a convex set given a separation oracle and achieves the first quadratic bound on the query complexity for the independence and rank oracles.Expand

We consider the adversarial convex bandit problem and we build the first poly(T)-time algorithm with poly(n) √T-regret for this problem. To do so we introduce three new ideas in the derivative-free… Expand

A new algorithm for solving linear programs that requires only Õ(√rank(A)L) iterations where A is the constraint matrix of a linear program with m constraints, n variables, and bit complexity L is presented.Expand

It follows that the ball walk for sampling from an isotropic logconcave density in \R^{n} converges in O^{*}(n^{2.5})steps from a warm start, and the same improved bound on the thin-shell estimate, Poincar constant and Lipschitz concentration constant is obtained.Expand

This paper shows how to generalize and efficiently implement a method proposed by Nesterov, giving faster asymptotic running times for various algorithms that use standard coordinate descent as a black box, and improves the convergence guarantees for Kaczmarz methods.Expand

A method with rate of convergence $\tilde{O}(1/k^{\frac{ 3p +1}{2}})$ after queries to the oracle for any convex function whose p^{th}$ order derivative is Lipschitz.Expand

It is shown that uniform sampling yields a matrix that, in some sense, well approximates a large fraction of the original, which leads to simple iterative row sampling algorithms for matrix approximation that run in input-sparsity time and preserve row structure and sparsity at all intermediate steps.Expand

This paper proposes a Markov chain Monte Carlo (MCMC) algorithm based on the underdamped Langevin diffusion (ULD) to solve the sampling problem and proposes a new framework to discretize stochastic differential equations.Expand

This paper shows how to solve linear programs of the form minAx=b,x≥0 c⊤x with n variables in time O*((nω+n2.5−α/2+n2+1/6) log(n/δ)) where ω is the exponent of matrix multiplication, α is the dual… Expand