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.
The paper connects two clustering methods by showing gradient ascent flow can move up the cluster tree.
DIGRAC clusters directed graphs using flow imbalance, outperforming existing methods.
GC-Flow uses graph flows for better clustering than traditional GCNs.
NeuralFLoC unifies registration and clustering of functional data, overcoming phase variation challenges.
Survey of flow-based algorithms for improving clusters.
A new clustering method estimates non-linear boundaries and automatically selects the number of clusters.
Proves existence of many non--covered Anosov flows on hyperbolic 3-manifolds.
Groups of firms often achieve a competitive advantage through the formation of geo-industrial clusters. Although many exemplary clusters, such as Hollywood or Silicon Valley, have been frequently studied, systematic approaches to identify and analyze the hierarchical structure of the geo-industrial clusters at the glob…
Many applications generate data with an intrinsic network structure such as time series data, image data or social network data. The network Lasso (nLasso) has been proposed recently as a method for joint clustering and optimization of machine learning models for networked data. The nLasso extends the Lasso from sparse…
Proposes a new optimization method for local graph clustering.
Fractal Flow enhances normalizing flows with interpretable latent space and hierarchical modeling.
Mean curvature flow of clusters of n-dimensional surfaces in R^{n+k} that meet in triples at equal angles along smooth edges and higher order junctions on lower dimensional faces is a natural extension of classical mean curvature flow. We call such a flow a mean curvature flow with triple edges. We show that if a smoot…
New algorithm improves clustering and quantization using MMD.
Graph Ricci flow reveals hidden hierarchies in stock market correlations.
Network Lasso clusters sparse graph clusters efficiently.
ClusterLOB clusters market events to identify different trading behaviors.
We define a class of Euclidean distances on weighted graphs, enabling to perform thermodynamic soft graph clustering. The class can be constructed form the "raw coordinates" encountered in spectral clustering, and can be extended by means of higher-dimensional embeddings (Schoenberg transformations). Geographical flow …
We present a graph-based semi-supervised learning (SSL) method for learning edge flows defined on a graph. Specifically, given flow measurements on a subset of edges, we want to predict the flows on the remaining edges. To this end, we develop a computational framework that imposes certain constraints on the overall fl…
Efficient algorithm for clustering and classification using MBO scheme.
New method uses Ricci curvature for hypergraph clustering, outperforming existing techniques.
We present a method for identifying the coherent structures associated with individual Lagrangian flow trajectories even where only sparse particle trajectory data is available. The method, based on techniques in spectral graph theory, uses the Coherent Structure Coloring vector and associated eigenvectors to analyze t…
Kleinberg introduced three natural clustering properties, or axioms, and showed they cannot be simultaneously satisfied by any clustering algorithm. We present a new clustering property, Monotonic Consistency, which avoids the well-known problematic behaviour of Kleinberg's Consistency axiom, and the impossibility resu…
Labor market institutions are central for modern economies, and their polices can directly affect unemployment rates and economic growth. At the individual level, unemployment often has a detrimental impact on people's well-being and health. At the national level, high employment is one of the central goals of any econ…
Study finds stock prices rarely appreciate during capital inflows but often appreciate during normal flows.
CMS uses machine learning to improve particle flow reconstruction.
Gradient flow in ReLU networks biases towards generalization but makes them vulnerable to adversarial attacks.
Framework simulates market microstructure with stable Hawkes processes.
The problem of finding groups in data (cluster analysis) has been extensively studied by researchers from the fields of Statistics and Computer Science, among others. However, despite its popularity it is widely recognized that the investigation of some theoretical aspects of clustering has been relatively sparse. One …
The paper introduces curvature-based clustering algorithms for graph analysis.
Dockless bike sharing systems need effective bike flow prediction models.
GeneraLight improves traffic signal control models' generalization ability.
We study the question of fair clustering under the {\em disparate impact} doctrine, where each protected class must have approximately equal representation in every cluster. We formulate the fair clustering problem under both the -center and the -median objectives, and show that even with two protected classes th…
The ability to accurately forecast and control inpatient census, and thereby workloads, is a critical and longstanding problem in hospital management. Majority of current literature focuses on optimal scheduling of inpatients, but largely ignores the process of accurate estimation of the trajectory of patients througho…
FedGRU uses federated learning to predict traffic flow accurately while preserving user privacy.
The in-game economies of massively multi-player online games (MMOGs) are complex systems that have to be carefully designed and managed. This paper presents the results of an analysis of auction house data from the MMOG Glitch, across a 14 month time period, the entire lifetime of the game. The data comprise almost 3 m…
New method clusters directed graphs using Koopman operators.
Infinity-harmonic functions linked to IMCF clusters, revealing new properties in 2D.
Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities. This problem, however, was studied mainly and extensively in the setting where moving objects can move freely on the euclidean space. In this paper, we study the problem of clustering trajec…
Distance plays a fundamental role in measuring similarity between objects. Various visualization techniques and learning tasks in statistics and machine learning such as shape matching, classification, dimension reduction and clustering often rely on some distance or similarity measure. It is of tremendous importance t…
Cluster analysis aims at separating patients into phenotypically heterogenous groups and defining therapeutically homogeneous patient subclasses. It is an important approach in data-driven disease classification and subtyping. Acute coronary syndrome (ACS) is a syndrome due to sudden decrease of coronary artery blood f…
New method learns latent group structures without clustering, using heat flow dynamics.
Proposes methods for local clustering in attributed graphs.
A graph clustering method that moves nodes to highest-degree neighbors.
New framework quantifies uncertainty in flexible density-based clustering.
New method clusters evolving networks using spatio-temporal graph Laplacian.
Optimal market making strategy for electronic markets with persistent order flows.
We present a robust multiple manifolds structure learning (RMMSL) scheme to robustly estimate data structures under the multiple low intrinsic dimensional manifolds assumption. In the local learning stage, RMMSL efficiently estimates local tangent space by weighted low-rank matrix factorization. In the global learning …
Due to the significance of transportation planning, traffic management, and dispatch optimization, predicting passenger origin-destination has emerged as a crucial requirement for intelligent transportation systems management. In this study, we present a model designed to forecast the origin and destination of travels …