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.
Sum-product networks enhance sequence modeling with higher-order factors.
H-GAT improves stock selection by capturing complex higher-order stock relations and integrating both technical and fundamental analysis.
Unified tensor factorization for efficient 3D convolutions in spatio-temporal emotion analysis.
FGNN generalizes graph neural networks to capture higher-order dependencies.
AFN learns adaptive-order feature interactions for better predictive models.
Factorization machines (FMs) are a supervised learning approach that can use second-order feature combinations even when the data is very high-dimensional. Unfortunately, despite increasing interest in FMs, there exists to date no efficient training algorithm for higher-order FMs (HOFMs). In this paper, we present the …
A new method captures higher-order interactions in data clusters.
In this article, we consider a 2 factors-model for pricing defaultable bond with discrete default intensity and barrier where the 2 factors are stochastic risk free short rate process and firm value process. We assume that the default event occurs in an expected manner when the firm value reaches a given default barrie…
High-dimensional tensors or multi-way data are becoming prevalent in areas such as biomedical imaging, chemometrics, networking and bibliometrics. Traditional approaches to finding lower dimensional representations of tensor data include flattening the data and applying matrix factorizations such as principal component…
Proposes tPARAFAC2 for tracking evolving patterns in time-evolving data.
Combines neural networks and probabilistic graphical models for efficient higher-order inference.
Homologically fibered knots are knots whose exteriors satisfy the same homological conditions as fibered knots. In our previous paper, we observed that for such a knot, higher-order Alexander invariants defined by Cochran, Harvey and Friedl are generally factorized into the part of the Magnus matrix and that of a certa…
We recover the higher order terms for the acoustic wave equation from measurements of the modulus of the solution. The recovery of these coefficients is reduced to a question of stability for inverting a Hamiltonian flow transform, not the geodesic X-ray transform encountered in other inverse boundary problems like the…
Here we propose a novel model family with the objective of learning to disentangle the factors of variation in data. Our approach is based on the spike-and-slab restricted Boltzmann machine which we generalize to include higher-order interactions among multiple latent variables. Seen from a generative perspective, the …
A new probabilistic BTD method for tensor data.
Predicting unobserved entries of a partially observed matrix has found wide applicability in several areas, such as recommender systems, computational biology, and computer vision. Many scalable methods with rigorous theoretical guarantees have been developed for algorithms where the matrix is factored into low-rank co…
Lower bounds for higher-order methods in non-convex optimization.
Users form information trails as they browse the web, checkin with a geolocation, rate items, or consume media. A common problem is to predict what a user might do next for the purposes of guidance, recommendation, or prefetching. First-order and higher-order Markov chains have been widely used methods to study such se…
This paper establishes inequalities on quaternionic hyperbolic spaces and the Cayley hyperbolic plane.
The paper proves existence and classification of translating solitons in warped product manifolds.
New method adds all interactions in non-linear models without high computational cost.
Neural network improves K-factor estimation for OFDM systems.
We analyze analytic approximation formulae for pricing zero-coupon bonds in the case when the short-term interest rate is driven by a one-factor mean-reverting process with a volatility nonlinearly depending on the interest rate itself. We derive the order of accuracy of the analytical approximation due to Choi and Wir…
The reduction theorems for general linear and classical connections are generalized for operators with values in higher order gauge-natural bundles. We prove that natural operators depending on the -jets of classical connections, on the -jets of general linear connections and on the -jets of tensor fields …
New method improves DAG learning by using large coefficients for higher-order terms.
A new method learns dynamic graph representations from time-varying data.
Joint analysis of data from multiple sources has the potential to improve our understanding of the underlying structures in complex data sets. For instance, in restaurant recommendation systems, recommendations can be based on rating histories of customers. In addition to rating histories, customers' social networks (e…
IFM improves recommender systems by learning flexible feature interactions.
The Lagrangian formalism on a arbitrary non-fibrating manifold is considered. The kinematical description of this generic situation is based on the concept of (higher-order) Grassmann manifolds which is the factorization of the regular velocity manifold to the action of the differential group. Here we introduce in this…
New method adds interactions to interpretable models for large-scale data.
Study privacy vs. utility in estimating network parameters with aggregated data.
Develops polynomial diffusion models for multi-factor commodity futures dynamics.
Most popular word embedding techniques involve implicit or explicit factorization of a word co-occurrence based matrix into low rank factors. In this paper, we aim to generalize this trend by using numerical methods to factor higher-order word co-occurrence based arrays, or \textit{tensors}. We present four word embedd…
Unified framework for disentangled representations using mechanistic independence.
MRTL learns interpretable spatial patterns efficiently.
In this paper we propose a semi-Markov modulated model of interest rates. We assume that the switching process is a semi-Markov process with finite state space E and the modulated process is a diffusive process. We derive recursive equations for the higher order moments of the discount factor and we describe a Monte Ca…
The study of higher-order homology embeddings for manifold topology.
Bayesian hypergraph inference models disease pathways from EHR data.
In this study, we tested the interaction effect of multimodal datasets using a novel method called the kernel method for detecting higher order interactions among biologically relevant mulit-view data. Using a semiparametric method on a reproducing kernel Hilbert space (RKHS), we used a standard mixed-effects linear mo…
Graphs improve theorem proving in higher-order logic.
Paper introduces techniques to learn higher-order programs, improving predictive accuracy and reducing learning times.
For certain classes of knots we define geometric invariants called higher-order genera. Each of these invariants is a refinement of the slice genus of a knot. We find lower bounds for the higher-order genera in terms of certain von Neumann -invariants, which we call higher-order signatures. The higher-order genera o…
DS-FACTO optimizes factorization machines for large-scale datasets.
A fundamental property of complex networks is the tendency for edges to cluster. The extent of the clustering is typically quantified by the clustering coefficient, which is the probability that a length-2 path is closed, i.e., induces a triangle in the network. However, higher-order cliques beyond triangles are crucia…
DPLS improves asset pricing by capturing non-linear risk factor structures.
Stability of capillary hypersurfaces with higher order mean curvature.
OpenGM is a C++ template library for defining discrete graphical models and performing inference on these models, using a wide range of state-of-the-art algorithms. No restrictions are imposed on the factor graph to allow for higher-order factors and arbitrary neighborhood structures. Large models with repetitive struc…
Paper provides Edgeworth expansions for network moments, improving accuracy of sampling distributions.