A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New adaptive methods improve deep learning performance.
In this paper, we formulate the Canonical Correlation Analysis (CCA) problem on matrix manifolds. This framework provides a natural way for dealing with matrix constraints and tools for building efficient algorithms even in an adaptive setting. Finally, an adaptive CCA algorithm is proposed and applied to a change dete…
Adaptive regularization methods pre-multiply a descent direction by a preconditioning matrix. Due to the large number of parameters of machine learning problems, full-matrix preconditioning methods are prohibitively expensive. We show how to modify full-matrix adaptive regularization in order to make it practical and e…
Adaptive algorithm improves convergence rate of Langevin dynamics.
Adaptive HMC improves sampling efficiency by optimizing mass matrix.
New nonconvex regularizer speeds up low-rank matrix completion.
Bayesian model infers factor dimensionality and sparse loading matrix adaptively.
Optimal rank-adaptive matrix estimation from linear measurements.
We consider the related tasks of matrix completion and matrix approximation from missing data and propose adaptive sampling procedures for both problems. We show that adaptive sampling allows one to eliminate standard incoherence assumptions on the matrix row space that are necessary for passive sampling procedures. Fo…
New methods improve online matrix optimization with reduced computational cost.
We study low rank matrix and tensor completion and propose novel algorithms that employ adaptive sampling schemes to obtain strong performance guarantees. Our algorithms exploit adaptivity to identify entries that are highly informative for learning the column space of the matrix (tensor) and consequently, our results …
DMFAW improves multi-view clustering with adaptive weights and feature selection.
New algorithm for active learning in multiple matrix completion problems.
Adaptive stochastic gradient methods such as AdaGrad have gained popularity in particular for training deep neural networks. The most commonly used and studied variant maintains a diagonal matrix approximation to second order information by accumulating past gradients which are used to tune the step size adaptively. In…
New methods improve recommendation accuracy for users and items with few ratings.
Kernel matrices (e.g. Gram or similarity matrices) are essential for many state-of-the-art approaches to classification, clustering, and dimensionality reduction. For large datasets, the cost of forming and factoring such kernel matrices becomes intractable. To address this challenge, we introduce a new adaptive sampli…
New method enhances model fine-tuning with minimal data.
Efficient CF approach using fast adaptive PCA for recommender systems.
Two new methods improve graph embedding without needing a complete graph structure.
New method for exact matrix completion with reduced observation complexity.
New method efficiently learns positive-definite curvature for neural nets.
We introduce a new sparse estimator of the covariance matrix for high-dimensional models in which the variables have a known ordering. Our estimator, which is the solution to a convex optimization problem, is equivalently expressed as an estimator which tapers the sample covariance matrix by a Toeplitz, sparsely-banded…
AWNN improves matrix completion by adaptively weighting nearest neighbors.
The paper proposes AIS for Bayesian inversion of multioutput signals with covariance estimation.
We extend the theory of matrix completion to the case where we make Poisson observations for a subset of entries of a low-rank matrix. We consider the (now) usual matrix recovery formulation through maximum likelihood with proper constraints on the matrix , and establish theoretical upper and lower bounds on the rec…
AIR-Net adapts low-rank regularization dynamically for better image completion.
Two randomized algorithms improve hypergraph learning accuracy and efficiency.
Adaptive learning rate algorithms such as RMSProp are widely used for training deep neural networks. RMSProp offers efficient training since it uses first order gradients to approximate Hessian-based preconditioning. However, since the first order gradients include noise caused by stochastic optimization, the approxima…
New method estimates spatial weights matrix for lattice data, improving prediction accuracy.
FAWMF adapts weights for implicit feedback recommendation efficiently.
Linear and Quadratic Discriminant analysis (LDA/QDA) are common tools for classification problems. For these methods we assume observations are normally distributed within group. We estimate a mean and covariance matrix for each group and classify using Bayes theorem. With LDA, we estimate a single, pooled covariance m…
EigenBayes: A fast, adaptive Bayesian shrinkage approach for high-dimensional matrix factorization
Develops a novel stochastic algorithm for diagonal estimation of large matrices.
Adaptive NN method improves matrix completion for non-smooth data.
Let S be a compact Riemann surfaces of genus g >= 2 and G a conformal automoprhism group of order n acting on S. In this paper we give the definition of an adapted generating set and an adapted basis for the first homology group of such a compact Riemann surface. This generating set and basis reflect the action of G in…
Optimal preconditioning improves Langevin sampling efficiency.
DaConA improves recommendation accuracy with auxiliary data by adapting to different data contexts.
We study the adaptive estimation of copula correlation matrix for the semi-parametric elliptical copula model. In this context, the correlations are connected to Kendall's tau through a sine function transformation. Hence, a natural estimate for is the plug-in estimator with Kendall's tau statistic. We …
CMA-ME combines CMA-ES and MAP-Elites for better quality and diversity in continuous domains.
The task of estimating a matrix given a sample of observed entries is known as the \emph{matrix completion problem}. Most works on matrix completion have focused on recovering an unknown real-valued low-rank matrix from a random sample of its entries. Here, we investigate the case of highly quantized observations when …
The paper sets bounds on how much regret is unavoidable in adaptive LQR with unknown B-matrix.
Sparse Inverse Covariance Estimation (SICE) is useful in many practical data analyses. Recovering the connectivity, non-connectivity graph of covariates is classified amongst the most important data mining and learning problems. In this paper, we introduce a novel SICE approach using adaptive thresholding. Our method i…
In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlar…
BLAST optimizes deep model inference by learning efficient matrix structures.
We study the problem of estimating from data, a sparse approximation to the inverse covariance matrix. Estimating a sparsity constrained inverse covariance matrix is a key component in Gaussian graphical model learning, but one that is numerically very challenging. We address this challenge by developing a new adaptive…
Contextual policy search (CPS) is a class of multi-task reinforcement learning algorithms that is particularly useful for robotic applications. A recent state-of-the-art method is Contextual Covariance Matrix Adaptation Evolution Strategies (C-CMA-ES). It is based on the standard black-box optimization algorithm CMA-ES…
New damping technique improves deep learning models by reducing noise in flat directions.
This paper proposes a new method for estimating sparse precision matrices in the high dimensional setting. It has been popular to study fast computation and adaptive procedures for this problem. We propose a novel approach, called Sparse Column-wise Inverse Operator, to address these two issues. We analyze an adaptive …