arXiv research

We describe stochastic Newton and stochastic quasi-Newton approaches to efficiently solve large linear least-squares problems where the very large data sets present a significant computational burden (e.g., the size may exceed computer memory or data are collected in real-time). In our proposed framework, stochasticity…

Simple stochastic Newton and cubic Newton methods with fast convergence.

New method uses adaptive sampling for optimization in uncertain conditions.

A new Bayesian filtering method speeds up stochastic Newton optimization.

SVRN accelerates Newton methods by reducing variance and improving performance.

In this work, we present a globalized stochastic semismooth Newton method for solving stochastic optimization problems involving smooth nonconvex and nonsmooth convex terms in the objective function. We assume that only noisy gradient and Hessian information of the smooth part of the objective function is available via…

A new optimization method improves deep learning accuracy without hyper-parameter tuning.

Improved training of large-scale neural networks with reduced variance noise.

A new hybrid Newton algorithm improves convergence in logistic regression.

Paper develops efficient methods for estimating Hessian inverses in stochastic optimization.

Paper proposes a new method to efficiently incorporate curvature information in stochastic optimization.

A new method for machine learning updates reduces complexity and improves robustness.

New method improves zeroth-order stochastic optimization with adaptive sampling.

New algorithms solve non-convex optimization problems efficiently.

A new algorithm reduces communication rounds for distributed convex optimization.

Develops a new method for solving nonsmooth nonconvex optimization problems.

This paper analyzes adaptive gradient algorithms for better performance in ill-conditioned problems.

In this paper we present a novel quasi-Newton algorithm for use in stochastic optimisation. Quasi-Newton methods have had an enormous impact on deterministic optimisation problems because they afford rapid convergence and computationally attractive algorithms. In essence, this is achieved by learning the second-order (…

Proposes a new method for optimizing large-scale models using Nyström approximation of the Hessian.

Develops a new SPP algorithm with variance reduction for weakly convex optimization.

ISAAC Newton uses input-based curvature for efficient training.

New averaging technique speeds up Newton method convergence.

Apollo improves nonconvex stochastic optimization efficiency.

New method improves generalization in deep learning models.

New algorithm improves convergence of Bayesian inference.

We present practical Levenberg-Marquardt variants of Gauss-Newton and natural gradient methods for solving non-convex optimization problems that arise in training deep neural networks involving enormous numbers of variables and huge data sets. Our methods use subsampled Gauss-Newton or Fisher information matrices and e…

The question of how to incorporate curvature information in stochastic approximation methods is challenging. The direct application of classical quasi- Newton updating techniques for deterministic optimization leads to noisy curvature estimates that have harmful effects on the robustness of the iteration. In this paper…

A new quasi-Newton method uses cubic regularization to avoid saddle points in deep learning.

RONM method reduces regret in stochastic convex bandits with decreasing noise.

New algorithms estimate Hessians using random directions for faster stochastic optimization.

The paper studies the solution of stochastic optimization problems in which approximations to the gradient and Hessian are obtained through subsampling. We first consider Newton-like methods that employ these approximations and discuss how to coordinate the accuracy in the gradient and Hessian to yield a superlinear ra…

EGN optimizes deep neural networks with exact Gauss-Newton for large-scale problems.

This paper introduces online algorithms to estimate robust geometric median in large data streams.

During recent years there has been an increased interest in stochastic adaptations of limited memory quasi-Newton methods, which compared to pure gradient-based routines can improve the convergence by incorporating second order information. In this work we propose a direct least-squares approach conceptually similar to…

Deep learning involves a difficult non-convex optimization problem, which is often solved by stochastic gradient (SG) methods. While SG is usually effective, it may not be robust in some situations. Recently, Newton methods have been investigated as an alternative optimization technique, but nearly all existing studies…

Incorporating second order curvature information in gradient based methods have shown to improve convergence drastically despite its computational intensity. In this paper, we propose a stochastic (online) quasi-Newton method with Nesterov's accelerated gradient in both its full and limited memory forms for solving lar…

Stochastic variance reduction algorithms have recently become popular for minimizing the average of a large, but finite number of loss functions. The present paper proposes a Riemannian stochastic quasi-Newton algorithm with variance reduction (R-SQN-VR). The key challenges of averaging, adding, and subtracting multipl…

New method solves optimization problems faster than existing methods.

SGD vs quasi-Newton optimization in neural networks: different landscapes, different generalizability.

We propose a fast proximal Newton-type algorithm for minimizing regularized finite sums that returns an $ε$-suboptimal point in $\tilde{\mathcal{O}}(d(n + \sqrt{κd})\log(\frac{1}ε))$ FLOPS, where $n$ is number of samples, $d$ is feature dimension, and $κ$ is the condition number. As long as $n > d$, the proposed method…

Paper shows faster convergence to local-minimizers in over-parametrized models under interpolation-like conditions.

Stochastic SGN method converges faster than SGD for DNNs.

Global convergence of an online (stochastic) limited memory version of the Broyden-Fletcher- Goldfarb-Shanno (BFGS) quasi-Newton method for solving optimization problems with stochastic objectives that arise in large scale machine learning is established. Lower and upper bounds on the Hessian eigenvalues of the sample …

Nowadays stochastic approximation methods are one of the major research direction to deal with the large-scale machine learning problems. From stochastic first order methods, now the focus is shifting to stochastic second order methods due to their faster convergence and availability of computing resources. In this pap…

In this paper we study stochastic quasi-Newton methods for nonconvex stochastic optimization, where we assume that noisy information about the gradients of the objective function is available via a stochastic first-order oracle (SFO). We propose a general framework for such methods, for which we prove almost sure conve…

Novel approach simplifies VI problems with faster performance.

We present a novel Newton-type method for distributed optimization, which is particularly well suited for stochastic optimization and learning problems. For quadratic objectives, the method enjoys a linear rate of convergence which provably \emph{improves} with the data size, requiring an essentially constant number of…

Develops efficient quasi-Newton methods for training deep neural networks.