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.
Develops a theoretical framework for scalable Gaussian Process regression methods.
A scalable GPVAE method using local adjacencies to approximate GP inference.
A review of nearest neighbour classifiers, focusing on similarity measures and computational efficiency.
The paper analyzes convergence rates of Gaussian process approximations for scalable regression.
In this paper we propose and explore the k-Nearest Neighbour UCB algorithm for multi-armed bandits with covariates. We focus on a setting where the covariates are supported on a metric space of low intrinsic dimension, such as a manifold embedded within a high dimensional ambient feature space. The algorithm is concept…
New algorithm reduces regret in contextual bandits with many near-boundary contexts.
A new adaptive kNN classifier outperforms Random Forests.
A hybrid K-NN and SVM technique improves classification accuracy.
We study the approximate nearest neighbour method for cost-sensitive classification on low-dimensional manifolds embedded within a high-dimensional feature space. We determine the minimax learning rates for distributions on a smooth manifold, in a cost-sensitive setting. This generalises a classic result of Audibert an…
In the machine learning field, dimensionality reduction is an important task. It mitigates the undesired properties of high-dimensional spaces to facilitate classification, compression, and visualization of high-dimensional data. During the last decade, researchers proposed many new (non-linear) techniques for dimensio…
New GPnn method achieves scalable regression with low computational cost.
New method estimates gradients accurately with sharp bounds.
Probabilistic k-nearest neighbour (PKNN) classification has been introduced to improve the performance of original k-nearest neighbour (KNN) classification algorithm by explicitly modelling uncertainty in the classification of each feature vector. However, an issue common to both KNN and PKNN is to select the optimal n…
Optimizes one-class classification methods for better performance.
Intuitive clustering algorithm balances cluster size and cohesion.
DINOSAUR improves retrieval by accounting for embedding uncertainty in recommender systems.
Novel estimator reduces diffusion model variance.
Unified framework evaluates different nearest neighbor classification methods.
A framework for flagging content with limited data.
There is an increasing body of evidence suggesting that exact nearest neighbour search in high-dimensional spaces is affected by the curse of dimensionality at a fundamental level. Does it necessarily mean that the same is true for k nearest neighbours based learning algorithms such as the k-NN classifier? We analyse t…
Study classifies liability insurance policies using machine learning.
A fast and scalable method for variable selection in high-dimensional Gaussian processes.
A recent proposal of data dependent similarity called Isolation Kernel/Similarity has enabled SVM to produce better classification accuracy. We identify shortcomings of using a tree method to implement Isolation Similarity; and propose a nearest neighbour method instead. We formally prove the characteristic of Isolatio…
A new ensemble method using random projections for kNN classification.
The paper combines supervised and unsupervised learning to predict financial market movements.
-nearest neighbour (-NN) is one of the simplest and most widely-used methods for supervised classification, that predicts a query's label by taking weighted ratio of observed labels of objects nearest to the query. The weights and the parameter regulate its bias-variance trade-off, and the …
VNNGP uses nearest neighbors to approximate GPs, improving scalability and performance.
A fast method for LOOCV in k-NN regression reduces computation time.
Proposes a new k-NN algorithm to improve classification accuracy by removing noise and pseudo-neighbours.
The classification of time series data is a well-studied problem with numerous practical applications, such as medical diagnosis and speech recognition. A popular and effective approach is to classify new time series in the same way as their nearest neighbours, whereby proximity is defined using Dynamic Time Warping (D…
Gas demand is made of three components: Residential, Industrial, and Thermoelectric Gas Demand. Herein, the one-day-ahead prediction of each component is studied, using Italian data as a case study. Statistical properties and relationships with temperature are discussed, as a preliminary step for an effective feature s…
Adaptive, sparse graphs improve learning performance.
The classification of phase transitions is a central and challenging task in condensed matter physics. Typically, it relies on the identification of order parameters and the analysis of singularities in the free energy and its derivatives. Here, we propose an alternative framework to identify quantum phase transitions,…
K-Nearest Neighbours (k-NN) is a popular classification and regression algorithm, yet one of its main limitations is the difficulty in choosing the number of neighbours. We present a Bayesian algorithm to compute the posterior probability distribution for k given a target point within a data-set, efficiently and withou…
Deep learning model estimates mutual information with low bias and variance.
The three-state agent-based 2D model of financial markets in the version proposed by Giulia Iori in 2002 has been herein extended. We have introduced the increase of herding behaviour by modelling the altering trust of an agent in his nearest neighbours. The trust increases if the neighbour has foreseen the price chang…
A new method for sampling on manifolds reduces density estimation errors.
Density-based clustering is the task of discovering high-density regions of entities (clusters) that are separated from each other by contiguous regions of low-density. DBSCAN is, arguably, the most popular density-based clustering algorithm. However, its cluster recovery capabilities depend on the combination of the t…
Optimal prototypes found for challenging pathological geometries.
New LSH methods for tensor data improve efficiency and space usage.
Existing methods for retrieving k-nearest neighbours suffer from the curse of dimensionality. We argue this is caused in part by inherent deficiencies of space partitioning, which is the underlying strategy used by most existing methods. We devise a new strategy that avoids partitioning the vector space and present a n…
Most exact methods for k-nearest neighbour search suffer from the curse of dimensionality; that is, their query times exhibit exponential dependence on either the ambient or the intrinsic dimensionality. Dynamic Continuous Indexing (DCI) offers a promising way of circumventing the curse and successfully reduces the dep…
Machine learning models solve inverse eigenvalue problems for symmetric potentials and refractive indices.
Validates network bootstraps for uncertainty quantification in network visualisation.
In this paper we are going to introduce a new nearest neighbours based approach to clustering, and compare it with previous solutions; the resulting algorithm, which takes inspiration from both DBscan and minimum spanning tree approaches, is deterministic but proves simpler, faster and doesnt require to set in advance …
Supervised statistical classification is a vital tool for satellite image processing. It is useful not only when a discrete result, such as feature extraction or surface type, is required, but also for continuum retrievals by dividing the quantity of interest into discrete ranges. Because of the high resolution of mode…
We derive Gaussian approximations for random forest predictions using region-based stabilization.
We give a simple combinatoric proof of an exponential upper bound on the number of distinct 3-manifolds that can be constructed by successively identifying nearest neighbour pairs of triangles in the boundary of a simplicial 3-ball and show that all closed simplicial manifolds that can be constructed in this manner are…