arXiv research

NCG methods improve shape optimization efficiency.

NLCG optimizes DNN training, especially with large mini-batches.

This manuscript proposes a probabilistic framework for algorithms that iteratively solve unconstrained linear problems $Bx = b$ with positive definite $B$ for $x$. The goal is to replace the point estimates returned by existing methods with a Gaussian posterior belief over the elements of the inverse of $B$, which can …

Generalizes Fenchel conjugation to nonlinear functions on arbitrary sets.

Conjugate gradient (CG) methods are a class of important methods for solving linear equations and nonlinear optimization problems. In this paper, we propose a new stochastic CG algorithm with variance reduction and we prove its linear convergence with the Fletcher and Reeves method for strongly convex and smooth functi…

Researchers compare different gradient methods for ridge regression, finding conjugate gradients have similar performance.

DE via conjugate policies improves exploration and policy performance.

Unified analysis of conjugate gradients and accelerated methods using duality gap.

The natural gradient method has been used effectively in conjugate Gaussian process models, but the non-conjugate case has been largely unexplored. We examine how natural gradients can be used in non-conjugate stochastic settings, together with hyperparameter learning. We conclude that the natural gradient can signific…

The study proves triviality and rigidity results for Ricci solitons and estimates their conjugate radius.

Harmonic functions of two variables are exactly those that admit a conjugate, namely a function whose gradient has the same length and is everywhere orthogonal to the gradient of the original function. We show that there are also partial differential equations controlling the functions of three variables that admit a c…

We propose a novel Riemannian manifold preconditioning approach for the tensor completion problem with rank constraint. A novel Riemannian metric or inner product is proposed that exploits the least-squares structure of the cost function and takes into account the structured symmetry that exists in Tucker decomposition…

Proposes an extension of BayesCG for solving multiple linear systems.

A new method speeds up deep neural network training.

Improved kernel ridge regression using conjugate gradients.

Bayesian model uses simple functions to forecast macroeconomic data.

The computational and storage complexity of kernel machines presents the primary barrier to their scaling to large, modern, datasets. A common way to tackle the scalability issue is to use the conjugate gradient algorithm, which relieves the constraints on both storage (the kernel matrix need not be stored) and computa…

Improved Gaussian process regression with tighter log marginal likelihood bounds.

Paper proves existence of conjugate points on ellipsoids but not on spheres.

Study of eigenvalues in nonlinear kernels for classification of separable data.

Differential equations explain neural network dynamics.

A method uses CG to create efficient channels for ideal observers.

Conjugate gradient methods improve efficiency for high-dimensional GLMMs.

The paper deals with learning probability distributions of observed data by artificial neural networks. We suggest a so-called gradient conjugate prior (GCP) update appropriate for neural networks, which is a modification of the classical Bayesian update for conjugate priors. We establish a connection between the gradi…

By restricting the iterate on a nonlinear manifold, the recently proposed Riemannian optimization methods prove to be both efficient and effective in low rank tensor completion problems. However, existing methods fail to exploit the easily accessible side information, due to their format mismatch. Consequently, there i…

New method for identifying autoregressive systems on manifolds.

A new network reduces MIMO detection complexity.

Deep learning designs effective preconditioners for water engineering problems.

A new method reduces the cost of solving large-scale linear models.

We propose a conjugate gradient type optimization technique for the computation of the Karcher mean on the set of complex linear subspaces of fixed dimension, modeled by the so-called Grassmannian. The identification of the Grassmannian with Hermitian projection matrices allows an accessible introduction of the geometr…

NeuralIF uses neural networks to improve preconditioning for faster CG convergence.

Let $(M,g(t))$, $0\le t\le T$, $\partial M\neφ$, be a compact $n$-dimensional manifold, $n\ge 2$, with metric $g(t)$ evolving by the Ricci flow such that the second fundamental form of $\partial M$ with respect to the unit outward normal of $\partial M$ is uniformly bounded below on $\partial M\times [0,T]$. We will pr…

We establish a point-wise gradient estimate for $all$ positive solutions of the conjugate heat equation. This contrasts to Perelman's point-wise gradient estimate which works mainly for the fundamental solution rather than all solutions. Like Perelman's estimate, the most general form of our gradient estimate does not …

Improves understanding of stochastic NGVI convergence rates.

New algorithms improve direction finding using prior signal knowledge.

New recommendations improve Gaussian process accuracy and stability.

We present a general method for deriving collapsed variational inference algo- rithms for probabilistic models in the conjugate exponential family. Our method unifies many existing approaches to collapsed variational inference. Our collapsed variational inference leads to a new lower bound on the marginal likelihood. W…

We develop a fast inference method for non-conjugate Gaussian process models on spike count data.

The paper introduces a new framework to understand and optimize deep neural networks.

Sharp Lipschitz bounds and gradient estimates for fully nonlinear parabolic equations.

The paper proves gradient estimates for nonlinear parabolic equations on smooth metric measure spaces.

We propose and study kernel conjugate gradient methods (KCGM) with random projections for least-squares regression over a separable Hilbert space. Considering two types of random projections generated by randomized sketches and Nyström subsampling, we prove optimal statistical results with respect to variants of norms …

We prove Gaussian type bounds for the fundamental solution of the conjugate heat equation evolving under the Ricci flow. As a consequence, for dimension 4 and higher, we show that the backward limit of type I $κ$-solutions of the Ricci flow must be a non-flat gradient shrinking Ricci soliton. This extends Perelman's pr…

The paper provides gradient estimates for nonlinear heat-type equations on smooth metric measure spaces.

We propose a novel Bayesian approach to solve stochastic optimization problems that involve finding extrema of noisy, nonlinear functions. Previous work has focused on representing possible functions explicitly, which leads to a two-step procedure of first, doing inference over the function space and second, finding th…

SING improves state inference in latent SDE models for better drift function estimation.

In this paper, we study elliptic gradient estimates for a nonlinear $f$-heat equation, which is related to the gradient Ricci soliton and the weighted log-Sobolev constant of smooth metric measure spaces. Precisely, we obtain Hamilton's and Souplet-Zhang's gradient estimates for positive solutions to the nonlinear $f$-…

New method scales Bayesian inference for nonlinear SSMs using buffered stochastic gradient.