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.
PINNACLE optimizes point selection for PINNs, improving accuracy.
A homological selection theorem for C-spaces, as well as, a finite-dimensional homological selection theorem is established. We apply the finite-dimensional homological selection theorem to obtain fixed-point theorems for usco homologically UV^n set-valued maps.
Selects points from Jordan domains on Riemannian surfaces.
Method selects significant spatial covariates in noisy data.
Feature selection methods are widely used in order to solve the 'curse of dimensionality' problem. Many proposed feature selection frameworks, treat all data points equally; neglecting their different representation power and importance. In this paper, we propose an unsupervised hypergraph feature selection method via …
New method selects optimal subdata for efficient parameter estimation.
The paper explores how to select data points for optimal learning performance.
This work tackles online memory selection in continual learning using information theory.
Kernel methods on discrete domains have shown great promise for many challenging data types, for instance, biological sequence data and molecular structure data. Scalable kernel methods like Support Vector Machines may offer good predictive performances but do not intrinsically provide uncertainty estimates. In contras…
The Minimal Learning Machine (MLM) is a nonlinear supervised approach based on learning a linear mapping between distance matrices computed in the input and output data spaces, where distances are calculated using a subset of points called reference points. Its simple formulation has attracted several recent works on e…
Algorithm selects variables and bandwidths for geographically weighted regression.
A method to select validation data from a dataset using statistical criteria.
CAD detects anomalies and selects prototypes using polyhedron curvature.
New method selects data points for better model performance.
Study solves optimal portfolio selection using HJB equation.
In their standard form Gaussian processes (GPs) provide a powerful non-parametric framework for regression and classificaton tasks. Their one limiting property is their scaling where is the number of training data points. In this paper we present a framework for GP training with sequential sele…
We study the problem of detecting change points (CPs) that are characterized by a subset of dimensions in a multi-dimensional sequence. A method for detecting those CPs can be formulated as a two-stage method: one for selecting relevant dimensions, and another for selecting CPs. It has been difficult to properly contro…
CAMS selects best pre-trained model for unlabeled data points.
A new algorithm selects data subsets avoiding outliers and high leverage points.
The scale and complexity of modern data sets and the limitations associated with testing large numbers of hypotheses underline the need for feature selection methods. Spectral techniques rank features according to their degree of consistency with an underlying metric structure, but their current graph-based formulation…
Novel optimization method detects change points in Gaussian data.
The paper examines how to protect LASSO-based feature selection from adversarial attacks.
New algorithm for clustering data streams with no substitutions.
Improved algorithm reduces excess risk in selective learning.
ABM automates feature engineering and variable selection for loss-based models.
New method stabilizes model selection with theoretical guarantees.
New MCMC algorithm reduces subset selection passes to 2 for optimal -dimensional subspace approximation.
In this article, we advocate the ensemble approach for variable selection. We point out that the stochastic mechanism used to generate the variable-selection ensemble (VSE) must be picked with care. We construct a VSE using a stochastic stepwise algorithm, and compare its performance with numerous state-of-the-art algo…
Novel unsupervised feature selection method using multi-step Markov transition probability.
New method selects recent similar periods for better electricity price forecasting.
This article is concerned with Gaussian process quadratures, which are numerical integration methods based on Gaussian process regression methods, and sigma-point methods, which are used in advanced non-linear Kalman filtering and smoothing algorithms. We show that many sigma-point methods can be interpreted as Gaussia…
Data selection methods, such as active learning and core-set selection, are useful tools for machine learning on large datasets. However, they can be prohibitively expensive to apply in deep learning because they depend on feature representations that need to be learned. In this work, we show that we can greatly improv…
To answer the existence of optimal swimmer learning/teaching strategies, this work introduces a two-level clustering in order to analyze temporal dynamics of motor learning in breaststroke swimming. Each level have been performed through Sparse Fisher-EM, a unsupervised framework which can be applied efficiently on lar…
The paper introduces a method to probabilistically select inducing points in sparse Gaussian processes.
We develop parallel predictive entropy search (PPES), a novel algorithm for Bayesian optimization of expensive black-box objective functions. At each iteration, PPES aims to select a batch of points which will maximize the information gain about the global maximizer of the objective. Well known strategies exist for sug…
PS-BAX uses posterior sampling to select evaluation points for efficient Bayesian algorithm execution.
ADS filters data points for efficient batch active learning.
In dictionary selection, several atoms are selected from finite candidates that successfully approximate given data points in the sparse representation. We propose a novel efficient greedy algorithm for dictionary selection. Not only does our algorithm work much faster than the known methods, but it can also handle mor…
Selecting diverse and important items, called landmarks, from a large set is a problem of interest in machine learning. As a specific example, in order to deal with large training sets, kernel methods often rely on low rank matrix Nyström approximations based on the selection or sampling of landmarks. In this context, …
Two new algorithms select matrix rows and columns to preserve distances.
Proposes a quantum-inspired algorithm for selecting representative data subsets.
Paper introduces WWAggr for ensemble CPD, improving accuracy and decision threshold selection.
Integration over non-negative integrands is a central problem in machine learning (e.g. for model averaging, (hyper-)parameter marginalisation, and computing posterior predictive distributions). Bayesian Quadrature is a probabilistic numerical integration technique that performs promisingly when compared to traditional…
We consider the bridge linear regression modeling, which can produce a sparse or non-sparse model. A crucial point in the model building process is the selection of adjusted parameters including a regularization parameter and a tuning parameter in bridge regression models. The choice of the adjusted parameters can be v…
Detecting the emergence of abrupt property changes in time series is a challenging problem. Kernel two-sample test has been studied for this task which makes fewer assumptions on the distributions than traditional parametric approaches. However, selecting kernels is non-trivial in practice. Although kernel selection fo…
Study uses RNN to detect CPs with SI to control false positives.
Existing MAP inference algorithms for determinantal point processes (DPPs) need to calculate determinants or conduct eigenvalue decomposition generally at the scale of the full kernel, which presents a great challenge for real-world applications. In this paper, we introduce a class of DPPs, called BwDPPs, that are char…
New approach to adaptively select bandwidths in nonparametric regression.