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.
Study of Torelli groups of partitioned surfaces with bounds and asymptotic lengths.
We present a new approach for learning compact and intuitive distributed representations with binary encoding. Rather than summing up expert votes as in products of experts, we employ for each variable the opinion of the most reliable expert. Data points are hence explained through a partitioning of the variables into …
Study ping-pong dynamics in hyperbolic-like groups with non-simple points.
Detect changes in noisy dynamical systems using empirical approximations and finite-sample bounds.
Finding the reduced-dimensional structure is critical to understanding complex networks. Existing approaches such as spectral clustering are applicable only when the full network is explicitly observed. In this paper, we focus on the online factorization and partition of implicit large-scale networks based on observati…
Traffic forecasting approaches are critical to developing adaptive strategies for mobility. Traffic patterns have complex spatial and temporal dependencies that make accurate forecasting on large highway networks a challenging task. Recently, diffusion convolutional recurrent neural networks (DCRNNs) have achieved stat…
Proposes using MLP for predicting optimal penalty in changepoint detection.
Lectures detail field theory dynamics and exact WKB analysis.
A new method reduces high-dimensional state space for dynamic choice models.
CW-EDMD improves prediction accuracy by learning local Koopman models for different state-space regions.
Sequential decision making in the presence of uncertainty and stochastic dynamics gives rise to distributions over state/action trajectories in reinforcement learning (RL) and optimal control problems. This observation has led to a variety of connections between RL and inference in probabilistic graphical models (PGMs)…
This paper proves properties of uniformly hyperbolic sets and constructs Markov partitions.
New method detects dynamical system changes in time series data.
We discuss a variant of `blind' community detection, in which we aim to partition an unobserved network from the observation of a (dynamical) graph signal defined on the network. We consider a scenario where our observed graph signals are obtained by filtering white noise input, and the underlying network is different …
New method reduces Gibbs partition function estimation complexity.
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…
Topological data analysis quantifies structural dynamics using persistent homology.
In this report, we present an unsupervised machine learning method for determining groups of molecular systems according to similarity in their dynamics or structures using Ward's minimum variance objective function. We first apply the minimum variance clustering to a set of simulated tripeptides using the information …
flexBART improves BART for categorical predictors by creating flexible tree partitions.
We propose an incremental training method that partitions the original network into sub-networks, which are then gradually incorporated in the running network during the training process. To allow for a smooth dynamic growth of the network, we introduce a look-ahead initialization that outperforms the random initializa…
A new method detects concept drift in streaming data using k-means space partitioning.
Gradient-guided nested sampling improves posterior inference efficiency.
Topic modeling, a method for extracting the underlying themes from a collection of documents, is an increasingly important component of the design of intelligent systems enabling the sense-making of highly dynamic and diverse streams of text data. Traditional methods such as Dynamic Topic Modeling (DTM) do not lend the…
Network representation learning, as an approach to learn low dimensional representations of vertices, has attracted considerable research attention recently. It has been proven extremely useful in many machine learning tasks over large graph. Most existing methods focus on learning the structural representations of ver…
A new algorithm detects changepoints in labeled and unlabeled data.
Spectral clustering is widely used to partition graphs into distinct modules or communities. Existing methods for spectral clustering use the eigenvalues and eigenvectors of the graph Laplacian, an operator that is closely associated with random walks on graphs. We propose a new spectral partitioning method that exploi…
Biracks are algebraic structures related to knots and links. We define a new enhancement of the birack counting invariant for oriented classical and virtual knots and links via algebraic structures called birack dynamical cocycles. The new invariants can also be understood in terms of partitions of the set of birack la…
The Gibbs sampler is a particularly popular Markov chain used for learning and inference problems in Graphical Models (GMs). These tasks are computationally intractable in general, and the Gibbs sampler often suffers from slow mixing. In this paper, we study the Swendsen-Wang dynamics which is a more sophisticated Mark…
DMFAW improves multi-view clustering with adaptive weights and feature selection.
Binomial tree methods (BTM) and explicit difference schemes (EDS) for the variational inequality model of American options with time dependent coefficients are studied. When volatility is time dependent, it is not reasonable to assume that the dynamics of the underlying asset's price forms a binomial tree if a partitio…
Classifies shapes of yield curves in the Svensson family.
The study limits how many parts regular simplicial partitions can overlap.
Abstract M5 branes on ADE singularities yields BPS spectrum and partition functions.
Hypergraph partitioning lies at the heart of a number of problems in machine learning and network sciences. Many algorithms for hypergraph partitioning have been proposed that extend standard approaches for graph partitioning to the case of hypergraphs. However, theoretical aspects of such methods have seldom received …
FNSDA adapts to new dynamics via Fourier space adaptation.
The paper challenges the use of decision trees for pointwise inference due to slow convergence rates.
A method for non-parametric conditional distribution estimation using CRPS-optimal binning.
Proposes PredVAR model for reduced-dimensional dynamics from noisy data.
New method calibrates stochastic reduced-order models from data efficiently.
In this paper, we propose a family of graph partition similarity measures that take the topology of the graph into account. These graph-aware measures are alternatives to using set partition similarity measures that are not specifically designed for graph partitions. The two types of measures, graph-aware and set parti…
The study examines the balancedness of random partition models and finds the rich-get-richer characteristic is a result of model assumptions.
The paper develops mixed-integer formulations for neural networks using partitioning.
New partition designs reduce star discrepancy in high-dimensional sampling.
We propose in this paper an exploratory analysis algorithm for functional data. The method partitions a set of functions into clusters and represents each cluster by a simple prototype (e.g., piecewise constant). The total number of segments in the prototypes, , is chosen by the user and optimally distributed am…
The paper constructs Markov partitions for geodesic flow on hyperbolic surfaces.
Graph partitioning is the problem of dividing the nodes of a graph into balanced partitions while minimizing the edge cut across the partitions. Due to its combinatorial nature, many approximate solutions have been developed, including variants of multi-level methods and spectral clustering. We propose GAP, a Generaliz…
Survey of mass partition problems in geometry and topology.
New method unifies and formalizes data partitioning using a single vector.