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.
Tensor CANDECOMP/PARAFAC (CP) decomposition is an important tool that solves a wide class of machine learning problems. Existing popular approaches recover components one by one, not necessarily in the order of larger components first. Recently developed simultaneous power method obtains only a high probability recover…
Proposes a Gaussian process for Koopman mode decomposition.
Extends effect variable concept to finite states for web search evaluation.
New method decomposes profits and losses continuously, avoiding discrete reporting issues.
In the recent paper \cite{DESZ}, the notion of -submartingale processes has been introduced. Within a jump-diffusion model, we prove here that a process which satisfies the simultaneous -submartingale property under a suitable family of equivalent probability measur…
Proposes joint LCA for multiview data to identify shared and view-specific components.
Paper compresses RNNs using HT decomposition for better performance.
The paper analyzes stability and asymptotic behavior of hedging strategies in binomial and trinomial models.
Meta-graph is currently the most powerful tool for similarity search on heterogeneous information networks,where a meta-graph is a composition of meta-paths that captures the complex structural information. However, current relevance computing based on meta-graph only considers the complex structural information, but i…
Revisits CP tensor decomposition for noisy, non-orthogonal data.
We propose a heterogeneous simultaneous graphical dynamic linear model (H-SGDLM), which extends the standard SGDLM framework to incorporate a heterogeneous autoregressive realised volatility (HAR-RV) model. This novel approach creates a GPU-scalable multivariate volatility estimator, which decomposes multiple time seri…
In tensor completion tasks, the traditional low-rank tensor decomposition models suffer from the laborious model selection problem due to their high model sensitivity. In particular, for tensor ring (TR) decomposition, the number of model possibilities grows exponentially with the tensor order, which makes it rather ch…
In this paper we study the problem of learning the weights of a deep convolutional neural network. We consider a network where convolutions are carried out over non-overlapping patches with a single kernel in each layer. We develop an algorithm for simultaneously learning all the kernels from the training data. Our app…
Post-hoc model-agnostic interpretation methods such as partial dependence plots can be employed to interpret complex machine learning models. While these interpretation methods can be applied regardless of model complexity, they can produce misleading and verbose results if the model is too complex, especially w.r.t. f…
The paper explores the tradeoffs between fairness measures in machine learning.
New method extracts joint and individual signals from multi-view data.
We present a novel approach which is able to explore the configuration of grouped convolutions within neural networks. Group-size Series (GroSS) decomposition is a mathematical formulation of tensor factorisation into a series of approximations of increasing rank terms. GroSS allows for dynamic and differentiable selec…
We propose a greedy variational method for decomposing a non-negative multivariate signal as a weighted sum of Gaussians, which, borrowing the terminology from statistics, we refer to as a Gaussian mixture model. Notably, our method has the following features: (1) It accepts multivariate signals, i.e. sampled multivari…
Non-orthogonal joint diagonalization (NJD) free of prewhitening has been widely studied in the context of blind source separation (BSS) and array signal processing, etc. However, NJD is used to retrieve the jointly diagonalizable structure for a single set of target matrices which are mostly formulized with a single da…
Paper proposes a new method for training diffusion models using Markov operators.
In this paper, we study on knots and closed incompressible surfaces in the 3-sphere via Morse functions. We show that both of knots and closed incompressible surfaces can be isotoped into a "related Morse position" simultaneously. As an application, we have following results. *Smallness of Montesinos tangles with lengt…
The paper studies maps in the Heisenberg group and their images, called Rickman rugs.
BIDIFAC+ factorizes linked matrices for cancer studies.
New algorithms improve tensor CP decomposition under mild conditions.
A fair PCA method using JEVD ensures balanced data representation.
Deep learning models can have low bias and variance, contrary to classical theory.
Modeling dependent defaults with multivariate Cox processes.
New method evaluates multiple social disparities using machine learning.
Unified model for signed networks separates balance and anomaly effects.
Many advanced Learning from Demonstration (LfD) methods consider the decomposition of complex, real-world tasks into simpler sub-tasks. By reusing the corresponding sub-policies within and between tasks, they provide training data for each policy from different high-level tasks and compose them to perform novel ones. E…
Paper introduces an unsupervised tensor-based anomaly detection method for spatiotemporal data.
Dictionary learning is a cutting-edge area in imaging processing, that has recently led to state-of-the-art results in many signal processing tasks. The idea is to conduct a linear decomposition of a signal using a few atoms of a learned and usually over-completed dictionary instead of a pre-defined basis. Determining …
This work gives a simultaneous analysis of both the ordinary least squares estimator and the ridge regression estimator in the random design setting under mild assumptions on the covariate/response distributions. In particular, the analysis provides sharp results on the ``out-of-sample'' prediction error, as opposed to…
A new method tackles Bayesian inverse problems with complex PDEs.
Paper identifies sparse structures and communities in heterogeneous graphical models.
Explicit encoding of group actions in deep features makes it possible for convolutional neural networks (CNNs) to handle global deformations of images, which is critical to success in many vision tasks. This paper proposes to decompose the convolutional filters over joint steerable bases across the space and the group …
Bayesian Empirical Bayes extends EB to complex structures using probabilistic symmetry.
Developed Taylor series for muscle-finger system analysis.
In this work, we consider Corporate Governance (CG) ties among companies from a multiple network perspective. Such a structure naturally arises from the close interrelation between the Shareholding Network (SH) and the Board of Directors network (BD). In order to capture the simultaneous effects of both networks on CG,…
Link's sphere number equals its bridge number.
Two methods extend multivariate Kelly optimization to large problem sizes.
Proposes a new graph trend filtering model for inhomogeneous graph signals.
MOSAIC detects change points in dynamic networks with low-rank and sparse changes.
In this paper, we analyze the fundamental conditions for low-rank tensor completion given the separation or tensor-train (TT) rank, i.e., ranks of unfoldings. We exploit the algebraic structure of the TT decomposition to obtain the deterministic necessary and sufficient conditions on the locations of the samples to ens…
This work is motivated by multimodality breast cancer imaging data, which is quite challenging in that the signals of discrete tumor-associated microvesicles (TMVs) are randomly distributed with heterogeneous patterns. This imposes a significant challenge for conventional imaging regression and dimension reduction mode…
We consider the problem of constructing a reduced-rank regression model whose coefficient parameter is represented as a singular value decomposition with sparse singular vectors. The traditional estimation procedure for the coefficient parameter often fails when the true rank of the parameter is high. To overcome this …
Study on lengths of random multicurves on hyperbolic surfaces.
A new method quickly identifies key variables and interactions.