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.
SDP approach recovers communities in multilayer hypergraphs from aggregated similarity matrices.
Enhances graph neural networks by considering feature similarities in node aggregation.
Data aggregation improves HAC for resource-constrained systems.
Transfer knowledge from multiple sources to improve matrix completion.
Partial fusion combines neural networks to balance accuracy and efficiency.
A new method for distributed PCA using matrix β-mean.
The aim of this paper is to provide some theoretical understanding of quasi-Bayesian aggregation methods non-negative matrix factorization. We derive an oracle inequality for an aggregated estimator. This result holds for a very general class of prior distributions and shows how the prior affects the rate of convergenc…
We provide an explicit aggregation in the neoclassical growth model with aggregate shocks and uninsurable employment risk. We show there are two restrictions on the unemployment shock for approximate aggregation to occur. First the probability of unemployment must be positive for each agent in each time period. That en…
Motivated by electricity consumption metering, we extend existing nonnegative matrix factorization (NMF) algorithms to use linear measurements as observations, instead of matrix entries. The objective is to estimate multiple time series at a fine temporal scale from temporal aggregates measured on each individual serie…
Extends graph similarity theory to improve MPNNs' generalization abilities.
State aggregation is a popular model reduction method rooted in optimal control. It reduces the complexity of engineering systems by mapping the system's states into a small number of meta-states. The choice of aggregation map often depends on the data analysts' knowledge and is largely ad hoc. In this paper, we propos…
U-aggregation combines multiple models without labels for better risk prediction.
PTBCC improves accuracy in multi-class annotation aggregation by learning from prototype confusion matrices.
New method aggregates Gaussian experts by detecting conditional independence violations.
New spectral tests assess network model fits efficiently.
Algorithm aggregates rewards from multiple players to learn related tasks in online bandit learning.
Paper optimizes demand aggregation for low-level electricity markets.
A new method combines Gaussian graphical models for better distributed Gaussian process predictions.
In this paper we show that the matrix of chromatic joins and the Gram matrix of the Temperley-Lieb algebra are similar (after rescaling), with the change of basis given by diagonal matrices.
New method aggregates nodes in sparse graphical models.
BOA improves financial forecasting by combining expert models.
Paper explores statistical and computational limits of estimating low-rank Gaussian mixtures.
A new tensor network method for image classification reduces computation cost.
k-Rater reliability corrects under-reporting of aggregated data reliability.
This paper proposes novel algorithms for speaker embedding using subjective inter-speaker similarity based on deep neural networks (DNNs). Although conventional DNN-based speaker embedding such as a -vector can be applied to multi-speaker modeling in speech synthesis, it does not correlate with the subjective inter-…
Regularization is essential when training large neural networks. As deep neural networks can be mathematically interpreted as universal function approximators, they are effective at memorizing sampling noise in the training data. This results in poor generalization to unseen data. Therefore, it is no surprise that a ne…
A key aspect of Federated Learning (FL) is the requirement of a centralized aggregator to maintain and update the global model. However, in many cases orchestrating a centralized aggregator might be infeasible due to numerous operational constraints. In this paper, we introduce BAFFLE, an aggregator free, blockchain dr…
Federated edge learning improves with CSIT-free model aggregation using RIS.
Paper proposes a supervised similarity framework for corporate bonds using RF proximities.
Federated learning is the centralized training of statistical models from decentralized data on mobile devices while preserving the privacy of each device. We present a robust aggregation approach to make federated learning robust to settings when a fraction of the devices may be sending corrupted updates to the server…
FLANDERS detects and blocks extreme model poisoning in federated learning.
Study improves forecasting of aggregated curves in electricity markets.
Single-layer GCN model improves recommendation performance with less complexity.
Study on convergence of graph neural networks on random graphs.
We investigate task clustering for deep-learning based multi-task and few-shot learning in a many-task setting. We propose a new method to measure task similarities with cross-task transfer performance matrix for the deep learning scenario. Although this matrix provides us critical information regarding similarity betw…
Finding a new mathematical representations for graph, which allows direct comparison between different graph structures, is an open-ended research direction. Having such a representation is the first prerequisite for a variety of machine learning algorithms like classification, clustering, etc., over graph datasets. In…
The paper examines risk aggregation under mixtures of marginals, finding that more homogeneous distributions lead to larger uncertainty.
This work improves knowledge distillation by transferring full kernel matrices efficiently.
We develop a secure aggregation protocol for federated learning that reduces communication and computation costs.
Matrix factorization is at the heart of many machine learning algorithms, for example, dimensionality reduction (e.g. kernel PCA) or recommender systems relying on collaborative filtering. Understanding a singular value decomposition (SVD) of a matrix as a neural network optimization problem enables us to decompose lar…
Explicitly or implicitly, most of dimensionality reduction methods need to determine which samples are neighbors and the similarity between the neighbors in the original highdimensional space. The projection matrix is then learned on the assumption that the neighborhood information (e.g., the similarity) is known and f…
Many similarity-based clustering methods work in two separate steps including similarity matrix computation and subsequent spectral clustering. However, similarity measurement is challenging because it is usually impacted by many factors, e.g., the choice of similarity metric, neighborhood size, scale of data, noise an…
Algorithm finds isotropy subgroups of orthogonal similarity on symmetric matrices.
A new method for feature selection robust to noise and design variability.
In high dimensions we propose and analyze an aggregation estimator of the precision matrix for Gaussian graphical models. This estimator, called graphical Exponential Screening (gES), linearly combines a suitable set of individual estimators with different underlying graphs, and balances the estimation error and sparsi…
We propose a new algorithm for finite sum optimization which we call the curvature-aided incremental aggregated gradient (CIAG) method. Motivated by the problem of training a classifier for a d-dimensional problem, where the number of training data is and , the CIAG method seeks to accelerate increme…
In this work, the possibility of clustering correlated random variables was examined, both because of their mutual similarity and because of their similarity to the principal components. The k-means algorithm and spectral algorithms were used for clustering. For spectral methods, the similarity matrix was both the matr…
Model reduction of Markov processes is a basic problem in modeling state-transition systems. Motivated by the state aggregation approach rooted in control theory, we study the statistical state compression of a discrete-state Markov chain from empirical trajectories. Through the lens of spectral decomposition, we study…