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.
Determinantal consensus clustering improves clustering robustness.
Consensus Monte Carlo clusters big data with shared anchors.
EB-RANSAC uses energy-based model for robust estimation without complex sampling.
MCE reduces embedding instability in nonlinear dimensionality reduction.
A new algorithm reduces communication in decentralized optimization.
A new framework for clustering high-dimensional data using vertical shards.
Bayesian consensus improves accuracy of forecasts from miscalibrated sources.
We use a cluster ensemble to determine the number of clusters, k, in a group of data. A consensus similarity matrix is formed from the ensemble using multiple algorithms and several values for k. A random walk is induced on the graph defined by the consensus matrix and the eigenvalues of the associated transition proba…
Quantum Proof-of-Work uses boson sampling to secure blockchain consensus.
IMPACC improves consensus clustering for bioinformatics data.
A novel framework for consensus clustering is presented which has the ability to determine both the number of clusters and a final solution using multiple algorithms. A consensus similarity matrix is formed from an ensemble using multiple algorithms and several values for k. A variety of dimension reduction techniques …
New methods estimate correspondence between point sets with outliers.
CB-APM uses analyst consensus as a bottleneck to interpret stock returns.
CoHiRF extends clustering methods to handle high-dimensional data efficiently.
Aggregates predictions from multiple regression models using random projections and kernel methods.
Condorcet's Jury Theorem has been invoked for ensemble classifiers to indicate that the combination of many classifiers can have better predictive performance than a single classifier. Such a theoretical underpinning is unknown for consensus clustering. This article extends Condorcet's Jury Theorem to the mean partitio…
Consensus NN learns from noisy data only for medical image denoising.
When solving consensus optimization problems over a graph, there is often an explicit characterization of the convergence rate of Gradient Descent (GD) using the spectrum of the graph Laplacian. The same type of problems under the Alternating Direction Method of Multipliers (ADMM) are, however, poorly understood. For i…
Practitioners of Bayesian statistics have long depended on Markov chain Monte Carlo (MCMC) to obtain samples from intractable posterior distributions. Unfortunately, MCMC algorithms are typically serial, and do not scale to the large datasets typical of modern machine learning. The recently proposed consensus Monte Car…
Deep learning has demonstrated abilities to learn complex structures, but they can be restricted by available data. Recently, Consensus Networks (CNs) were proposed to alleviate data sparsity by utilizing features from multiple modalities, but they too have been limited by the size of labeled data. In this paper, we ex…
The nowadays massive amounts of generated and communicated data present major challenges in their processing. While capable of successfully classifying nonlinearly separable objects in various settings, subspace clustering (SC) methods incur prohibitively high computational complexity when processing large-scale data. …
Although many successful ensemble clustering approaches have been developed in recent years, there are still two limitations to most of the existing approaches. First, they mostly overlook the issue of uncertain links, which may mislead the overall consensus process. Second, they generally lack the ability to incorpora…
Decentralized ranking consensus via gossip for robust and scalable systems.
New optimization model converges to global minimizers on spheres.
Unified framework for sampling and approximating high-dimensional energy landscapes.
A hybrid machine learning model predicts soccer match scores for FIFA Women's World Cups.
Although consistency is a minimum requirement of any estimator, little is known about consistency of the mean partition approach in consensus clustering. This contribution studies the asymptotic behavior of mean partitions. We show that under normal assumptions, the mean partition approach is consistent and asymptotic …
Data privacy is an important concern in learning, when datasets contain sensitive information about individuals. This paper considers consensus-based distributed optimization under data privacy constraints. Consensus-based optimization consists of a set of computational nodes arranged in a graph, each having a local ob…
The task of clustering a set of objects based on multiple sources of data arises in several modern applications. We propose an integrative statistical model that permits a separate clustering of the objects for each data source. These separate clusterings adhere loosely to an overall consensus clustering, and hence the…
WISCA generates consensus explanations from conflicting model-agnostic interpretability methods.
A novel algorithm minimizes regret in a multi-agent bandit problem with time-varying random graphs and heterogeneous rewards.
Bagging and boosting are proved to be the best methods of building multiple classifiers in classification combination problems. In the area of "flat clustering" problems, it is also recognized that multi-clustering methods based on boosting provide clusterings of an improved quality. In this paper, we introduce a novel…
This paper provides an embedding perspective to consensus clustering.
GraphSAC detects anomalies in large graphs by sampling and filtering node subsets.
This study analyzes and optimizes hyperparameters for machine learning models.
Bayesian framework for online consensus prediction from expert feedback.
In distributed machine learning, where agents collaboratively learn from diverse private data sets, there is a fundamental tension between consensus and optimality. In this paper, we build on recent algorithmic progresses in distributed deep learning to explore various consensus-optimality trade-offs over a fixed commu…
Paper analyzes convergence of decentralized algorithms with noise and bias.
Paper proves PI consensus algorithm converges exponentially under restricted secant inequality.
To devise efficient solutions for approximating a mean partition in consensus clustering, Dimitriadou et al. [3] presented a necessary condition of optimality for a consensus function based on least square distances. We show that their result is pivotal for deriving interesting properties of consensus clustering beyond…
ARCO-BO optimizes multi-agent design under heterogeneity, improving efficiency and performance.
We present here an introduction to Brainstorming approach, that was recently proposed as a consensus meta-learning technique, and used in several practical applications in bioinformatics and chemoinformatics. The consensus learning denotes heterogeneous theoretical classification method, where one trains an ensemble of…
A method to improve clustering explainability using bagging and feature dropout.
Adversaries can manipulate cooperative MARL networks.
In this dissertation we propose alternative analysis of distributed stochastic gradient descent (SGD) algorithms that rely on spectral properties of the data covariance. As a consequence we can relate questions pertaining to speedups and convergence rates for distributed SGD to the data distribution instead of the regu…
The distribution of price returns for a class of uncorrelated diffusive dynamics is considered. The basic assumptions are (1) that there is a "consensus" value associated with a stock, and (2) that the rate of diffusion depends on the deviation of the stock price from the consensus value. We find an analytical expressi…
Consensus dimension reduction combines multiple visualizations to identify shared patterns.
A new decentralized Bayesian learning method using Metropolis-adjusted Hamiltonian Monte Carlo.