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.
HCL learns shared and modality-specific latent representations for multimodal data.
The paper addresses risk sharing and variability measures among agents with general risk preferences.
SPLICE method disentangles shared and private latent variables from multi-view data.
We statistically investigate the distribution of share price and the distributions of three common financial indicators using data from approximately 8,000 companies publicly listed worldwide for the period 2004-2013. We find that the distribution of share price follows Zipf's law; that is, it can be approximated by a …
This paper proposes a novel learning method for multi-task applications. Multi-task neural networks can learn to transfer knowledge across different tasks by using parameter sharing. However, sharing parameters between unrelated tasks can hurt performance. To address this issue, we propose a framework to learn fine-gra…
New model extracts shared brain activity patterns from fMRI data.
There is a growing interest in joint multi-subject fMRI analysis. The challenge of such analysis comes from inherent anatomical and functional variability across subjects. One approach to resolving this is a shared response factor model. This assumes a shared and time synchronized stimulus across subjects. Such a model…
A new method for handling missing values in data.
The group membership prediction (GMP) problem involves predicting whether or not a collection of instances share a certain semantic property. For instance, in kinship verification given a collection of images, the goal is to predict whether or not they share a {\it familial} relationship. In this context we propose a n…
Unified multitask learning framework for mixed-type outcomes.
This paper analyzes stock market data to predict share prices using regression models.
Method embeds numeric tabular datasets into a shared vector space for similarity and retrieval.
We consider high-dimensional distribution estimation through autoregressive networks. By combining the concepts of sparsity, mixtures and parameter sharing we obtain a simple model which is fast to train and which achieves state-of-the-art or better results on several standard benchmark datasets. Specifically, we use a…
Improved neural population modeling using shared features and ensemble detection.
VSML unifies meta learning concepts and enables simple backpropagation.
New method for MTL with varying sparsity patterns across tasks.
Better signal detection in undersampled data using joint and cross covariances.
This work addresses privacy issues in IoT data sharing by balancing information disclosure and user privacy.
Although the growth of share-based payments with performance conditions (hereafter, SPPC) is prominent today, the theoretical price of SPPC has not been sufficiently studied. Reflecting such a situation, the current accounting standards for share-based payments issued in 2004 have had many problems. This paper develops…
Learning representations that disentangle the underlying factors of variability in data is an intuitive way to achieve generalization in deep models. In this work, we address the scenario where generative factors present a multimodal distribution due to the existence of class distinction in the data. We propose N-VAE, …
The aim of this study is to investigate quantitatively whether share prices deviated from company fundamentals in the stock market crash of 2008. For this purpose, we use a large database containing the balance sheets and share prices of 7,796 worldwide companies for the period 2004 through 2013. We develop a panel reg…
We focus on explicitly learning disentangled representation for natural image generation, where the underlying spatial structure and the rendering on the structure can be independently controlled respectively, yet using no tuple supervision. The setting is significant since tuple supervision is costly and sometimes eve…
Engineering problems often involve data sources of variable fidelity with different costs of obtaining an observation. In particular, one can use both a cheap low fidelity function (e.g. a computational experiment with a CFD code) and an expensive high fidelity function (e.g. a wind tunnel experiment) to generate a dat…
Unpaired multi-domain causal representation learning is possible with sufficient conditions.
Although recent studies have shown that electricity systems with shares of wind and solar above 80% can be affordable, economists have raised concerns about market integration. Correlated generation from variable renewable sources depresses market prices, which can cause wind and solar to cannibalise their own revenues…
Paper provides new bounds for risk aggregation and sharing.
A new ICA model identifies shared brain activity patterns across subjects.
Proposes a new method for rank-consistent ordinal regression without weight-sharing constraints.
PAVI speeds up VI for large-scale studies by sharing parameterization across i.i.d. variables.
Study shows environmental spending positively impacts company profitability.
Observed associations in a database may be due in whole or part to variations in unrecorded (latent) variables. Identifying such variables and their causal relationships with one another is a principal goal in many scientific and practical domains. Previous work shows that, given a partition of observed variables such …
DICCA maps multi-view data into a shared latent space with interpretable components.
Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data. We develop a latent variable model that can cope with imbalanced data by dividing the latent space into a shared space…
We introduce a novel mechanism to tighten the local polytope relaxation for MAP inference in Markov random fields with low state space variables. We consider a surjection of the variables to a set of hyper-variables and apply the local polytope relaxation over these hyper-variables. The state space of each individual h…
Graph-coupled causal Bayesian optimization transfers information across related interventions.
This study analyzes how weather impacts bike sharing usage in Washington D.C.
A new distance for mixed-variable, hierarchical datasets with meta variables.
We consider the prediction of weak effects in a multiple-output regression setup, when covariates are expected to explain a small amount, less than , of the variance of the target variables. To facilitate the prediction of the weak effects, we constrain our model structure by introducing a novel Bayesian ap…
Advances in molecular "omics'" technologies have motivated new methodology for the integration of multiple sources of high-content biomedical data. However, most statistical methods for integrating multiple data matrices only consider data shared vertically (one cohort on multiple platforms) or horizontally (different …
Exact optimality achieved in distributed mean estimation with shared randomness.
Discond-VAE separates continuous and discrete factors in data.
The cumulant analysis plays an important role in non Gaussian distributed data analysis. The shares' prices returns are good example of such data. The purpose of this research is to develop the cumulant based algorithm and use it to determine eigenvectors that represent investment portfolios with low variability. Such …
MCPCA analyzes shared factors across multiple data contexts.
ARCO-BO optimizes multi-agent design under heterogeneity, improving efficiency and performance.
Method estimates shared and study-specific factors for multi-study data.
A new meta-learning method using shared variational inference.
Dynamic models improve CoVaR forecasts for financial system risks.
Information-theoretic quantities, such as entropy, are used to quantify the amount of information a given variable provides. Entropies can be used together to compute the mutual information, which quantifies the amount of information two variables share. However, accurately estimating these quantities from data is extr…